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Pulastya B commited on
Commit
5ce70d3
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1 Parent(s): 6b731f7

Fixed File path not found issues and Multi-user issues

Browse files
Files changed (5) hide show
  1. Server Logs 2.txt +1061 -0
  2. Server Logs.txt +716 -0
  3. src/api/app.py +66 -61
  4. src/orchestrator.py +35 -8
  5. src/reasoning/reasoner.py +13 -2
Server Logs 2.txt ADDED
@@ -0,0 +1,1061 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ===== Application Startup at 2026-02-19 18:09:03 =====
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+
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+ INFO:src.api.app:👥 Multi-user session isolation initialized (fast mode)
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+ INFO:src.api.app:🔒 Workflow lock initialized for request queuing
5
+ INFO:src.api.app:✅ Frontend assets mounted from /home/user/app/FRRONTEEEND/dist
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+ INFO: Started server process [1]
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+ INFO: Waiting for application startup.
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+ INFO:src.api.app:Initializing legacy global agent for health checks...
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+ 🤖 Initialized with Mistral provider - Model: mistral-large-latest
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+ ✅ Cache database initialized at /home/user/app/cache_db/cache.db
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+ 🧠 Loading SBERT model: all-MiniLM-L6-v2...
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+ INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: cpu
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+ INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2
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+ ✅ SBERT loaded on CPU
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+ 📊 Token Budget: 120,000 tokens available (128,000 - 8,000 reserve)
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+ ✅ Sessions database initialized at ./cache_db/sessions.db
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+ ✅ Created new session: 5081830f-32ee-4f0a-81c5-cf0fc6906e43
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+ 📁 Output directory: /tmp/data_science_agent/outputs
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+ INFO:src.api.app:✅ Health check agent initialized with provider: mistral
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+ INFO:src.api.app:👥 Per-session agents enabled - each user gets isolated instance
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+ INFO:src.api.app:🤖 Multi-agent architecture enabled with 5 specialists
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+ INFO: Application startup complete.
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+ INFO: Uvicorn running on http://0.0.0.0:7860 (Press CTRL+C to quit)
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+ INFO: 10.16.34.155:32798 - "GET / HTTP/1.1" 200 OK
25
+ INFO: 10.16.4.123:43387 - "GET /index.css HTTP/1.1" 200 OK
26
+ INFO: 10.16.34.155:32798 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 200 OK
27
+ INFO: 10.16.4.123:54623 - "GET / HTTP/1.1" 200 OK
28
+ INFO: 10.16.4.123:54623 - "GET /index.css HTTP/1.1" 200 OK
29
+ INFO: 10.16.34.155:7477 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 200 OK
30
+ INFO: 10.16.34.155:53091 - "GET /favicon.ico HTTP/1.1" 200 OK
31
+ [SSE] ENDPOINT: Client connected for session_id=d41f7d5d-57f2-4b66-8137-b91496eda810
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+ [SSE] Queue registered, total subscribers: 1
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+ INFO: 10.16.4.123:30718 - "GET /api/progress/stream/d41f7d5d-57f2-4b66-8137-b91496eda810 HTTP/1.1" 200 OK
34
+ [SSE] SENDING connection event to client
35
+ [SSE] No history to replay (fresh session)
36
+ [SSE] Starting event stream loop for session d41f7d5d-57f2-4b66-8137-b91496eda810
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+ INFO:src.api.app:SSE stream cancelled for session d41f7d5d-57f2-4b66-8137-b91496eda810
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+ INFO:src.api.app:SSE stream closed for session d41f7d5d-57f2-4b66-8137-b91496eda810
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+ INFO:src.api.app:[ASYNC] Created new session: 2df5d6d1...
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+ INFO:src.api.app:[ASYNC] File saved: uber_trips_dataset_50k.csv
41
+ INFO: 10.16.4.123:29159 - "POST /run-async HTTP/1.1" 200 OK
42
+ INFO:src.api.app:[BACKGROUND] Starting analysis for session 2df5d6d1...
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+ [🧹] Clearing SSE history for 2df5d6d1...
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+ INFO:src.api.app:[🆕] Creating lightweight session for 2df5d6d1...
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+ INFO:src.api.app:✅ Session created for 2df5d6d1 (cache: 1/50) - <1s init
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+ [DEBUG] Session fallback triggered - has_ambiguous_ref=True, resolved={}
47
+ [DEBUG] resolve_ambiguity returning: {}
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+ [DEBUG] Orchestrator received resolved_params: {}
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+ [DEBUG] Current file_path: '/tmp/data_science_agent/uber_trips_dataset_50k.csv', target_col: 'None'
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+ 📝 User provided new file: /tmp/data_science_agent/uber_trips_dataset_50k.csv (ignoring session file: none)
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+ 🔍 Extracting dataset schema locally (no LLM)...
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+ [SSE] ENDPOINT: Client connected for session_id=2df5d6d1-f23c-48fb-b353-373e7693b75e
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+ [SSE] Queue registered, total subscribers: 1
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+ INFO: 10.16.34.155:32511 - "GET /api/progress/stream/2df5d6d1-f23c-48fb-b353-373e7693b75e HTTP/1.1" 200 OK
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+ [SSE] SENDING connection event to client
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+ [SSE] No history to replay (fresh session)
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+ [SSE] Starting event stream loop for session 2df5d6d1-f23c-48fb-b353-373e7693b75e
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+ 🧠 Semantic layer: Embedded 14 columns
59
+ Found 10 similar column pairs (potential duplicates)
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+ 🧠 Semantic layer enriched 14 columns
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+ ✅ Schema extracted: 50000 rows × 14 cols
62
+ File size: 8.46 MB
63
+
64
+ 🎯 Intent Classification:
65
+ Mode: EXPLORATORY
66
+ Confidence: 40%
67
+ Reasoning: No strong pattern match, defaulting to exploratory analysis
68
+ Sub-intent: default
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+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=intent_classified, msg=
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+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 1
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+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
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+ [SSE] Successfully queued event to subscriber 1
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+
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+ 🧠 Routing to REASONING LOOP (exploratory mode)
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+ [SSE] GOT event from queue: intent_classified
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+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_mode, msg=🧠 Reasoning Loop activated (exploratory mode)
77
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 2
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+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
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+ [SSE] Successfully queued event to subscriber 1
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+
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+ ============================================================
82
+ 🧠 REASONING LOOP (EXPLORATORY mode)
83
+ Question: Explain the important features in this dataset
84
+ Max iterations: 8
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+ ============================================================
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+
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+ 🔬 Generating hypotheses from data profile...
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+ 📋 Final parameters: ['file_path']
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+ [SSE] GOT event from queue: reasoning_mode
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+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
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+ Generated 5 hypotheses:
92
+ 1. [0.9] The 'fare_amount' distribution exhibits unexpected spikes at round numbers (e.g., $10, $20, $50), suggesting potential fare manipulation, rounding errors, or psychological pricing effects by drivers. This could indicate data quality issues or behavioral patterns worth investigating.
93
+ 2. [0.9] 'distance_km' and 'fare_amount' show a non-linear relationship, with fares increasing disproportionately for very short or very long trips. This may reveal surge pricing effects, city-specific pricing models, or data entry errors in distance calculations.
94
+ 3. [0.8] Certain 'driver_id' or 'rider_id' values appear in an implausibly high number of trips (e.g., >100 trips/day), suggesting potential data duplication, bot activity, or fraudulent ride patterns that could skew demand/supply metrics.
95
+ 4. [0.8] The 'status' column contains a small percentage of 'cancelled' or 'failed' trips, but these are disproportionately concentrated in specific cities or time windows, indicating potential regional service issues or rider/driver behavior differences.
96
+ 5. [0.8] Geospatial anomalies exist where 'pickup_lat'/'pickup_lng' or 'drop_lat'/'drop_lng' fall outside expected city boundaries, suggesting GPS errors, incorrect city assignments, or fraudulent trip reporting.
97
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=hypotheses_generated, msg=
98
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 3
99
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
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+ [SSE] Successfully queued event to subscriber 1
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+
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+ ── Iteration 1/8 ──
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+ 🤔 REASON: Deciding next action...
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+ [SSE] GOT event from queue: hypotheses_generated
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+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
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+ Status: investigating
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+ Reasoning: To explain the important features in the dataset, we first need a comprehensive understanding of the data, including distributions, correlations, and potential relationships between features. This will help identify which features are most relevant or influential.
108
+ Tool: generate_ydata_profiling_report
109
+ Hypothesis: The profiling report will reveal key statistics, distributions, correlations, and potential data quality issues, which will help identify the most important features in the dataset.
110
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
111
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 4
112
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
113
+ [SSE] Successfully queued event to subscriber 1
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+ ⚡ ACT: Executing generate_ydata_profiling_report...
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+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: generate_ydata_profiling_report
116
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 5
117
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
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+ [SSE] Successfully queued event to subscriber 1
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+ 📋 Final parameters: ['file_path', 'output_path', 'minimal', 'title']
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+ [SSE] GOT event from queue: reasoning_step
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+ [SSE] GOT event from queue: tool_executing
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+ INFO:visions.backends:Pandas backend loaded 2.3.3
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+ INFO:visions.backends:Numpy backend loaded 2.1.3
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+ INFO:visions.backends:Pyspark backend NOT loaded
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+ INFO:visions.backends:Python backend loaded
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+ Upgrade to ydata-sdk
127
+ Improve your data and profiling with ydata-sdk, featuring data quality scoring, redundancy detection, outlier identification, text validation, and synthetic data generation.
128
+ Register at https://ydata.ai/register
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+ Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: trip_id]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: driver_id]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: rider_id]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: pickup_lat]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: pickup_lat]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: pickup_lng]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: drop_lat]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: drop_lng]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: distance_km]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: fare_amount]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: payment_method]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: pickup_time]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: drop_time]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: drop_time]
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+ 0%| | 0/14 [00:00<?, ?it/s]
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+ Summarize dataset: 5%|▌ | 1/19 [00:02<00:42, 2.34s/it, Describe variable: drop_time]
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+ Summarize dataset: 16%|█▌ | 3/19 [00:02<00:11, 1.45it/s, Describe variable: drop_time]
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+ Summarize dataset: 32%|███▏ | 6/19 [00:02<00:03, 3.48it/s, Describe variable: drop_time]
148
+ 7%|▋ | 1/14 [00:02<00:31, 2.42s/it]
149
+ Summarize dataset: 53%|█████▎ | 10/19 [00:02<00:01, 6.52it/s, Describe variable: drop_time]
150
+ 43%|████▎ | 6/14 [00:02<00:02, 3.11it/s]
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+ Summarize dataset: 68%|██████▊ | 13/19 [00:03<00:00, 6.39it/s, Describe variable: drop_time]
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+ Summarize dataset: 74%|███████▎ | 14/19 [00:03<00:00, 7.05it/s, Describe variable: drop_time]
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+ 93%|█████████▎| 13/14 [00:02<00:00, 6.71it/s]
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+ 100%|██████████| 14/14 [00:02<00:00, 4.80it/s]
155
+ INFO: 10.16.34.155:49568 - "GET / HTTP/1.1" 200 OK
156
+ INFO: 10.16.34.155:49568 - "GET /index.css HTTP/1.1" 200 OK
157
+ INFO: 10.16.4.123:38750 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 200 OK
158
+ INFO: 10.16.34.155:49568 - "GET /favicon.ico HTTP/1.1" 200 OK
159
+ [SSE] ENDPOINT: Client connected for session_id=1963e777-4930-4200-9ada-ad742e5de6bc
160
+ [SSE] Queue registered, total subscribers: 1
161
+ INFO: 10.16.4.123:1392 - "GET /api/progress/stream/1963e777-4930-4200-9ada-ad742e5de6bc HTTP/1.1" 200 OK
162
+ [SSE] SENDING connection event to client
163
+ [SSE] No history to replay (fresh session)
164
+ [SSE] Starting event stream loop for session 1963e777-4930-4200-9ada-ad742e5de6bc
165
+ Summarize dataset: 74%|███████▎ | 14/19 [00:03<00:00, 7.05it/s, Get variable types]
166
+ Summarize dataset: 75%|███████▌ | 15/20 [00:03<00:00, 7.05it/s, Get dataframe statistics]
167
+ Summarize dataset: 80%|████████ | 16/20 [00:03<00:00, 8.07it/s, Get dataframe statistics]
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+ Summarize dataset: 64%|██████▍ | 16/25 [00:03<00:01, 8.07it/s, Calculate pearson correlation]
169
+ Summarize dataset: 68%|██████▊ | 17/25 [00:03<00:00, 8.07it/s, Calculate spearman correlation]
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+ Summarize dataset: 72%|███████▏ | 18/25 [00:03<00:00, 8.67it/s, Calculate spearman correlation]
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+ Summarize dataset: 72%|███████▏ | 18/25 [00:03<00:00, 8.67it/s, Calculate phi_k correlation]
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+ Summarize dataset: 76%|███████▌ | 19/25 [00:11<00:00, 8.67it/s, Calculate cramers correlation]
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+ Summarize dataset: 80%|████████ | 20/25 [00:11<00:05, 1.06s/it, Calculate cramers correlation]
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+ Summarize dataset: 80%|████████ | 20/25 [00:11<00:05, 1.06s/it, Calculate auto correlation]
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+ Summarize dataset: 84%|████████▍ | 21/25 [00:11<00:04, 1.02s/it, Calculate auto correlation]
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+ Summarize dataset: 84%|████████▍ | 21/25 [00:11<00:04, 1.02s/it, Get scatter matrix]
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+ Summarize dataset: 20%|█▉ | 21/106 [00:11<01:26, 1.02s/it, scatter trip_id, trip_id]
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+ Summarize dataset: 21%|██ | 22/106 [00:13<01:27, 1.04s/it, scatter trip_id, trip_id]
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+ Summarize dataset: 21%|██ | 22/106 [00:13<01:27, 1.04s/it, scatter driver_id, trip_id]
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+ Summarize dataset: 22%|██▏ | 23/106 [00:13<01:11, 1.16it/s, scatter driver_id, trip_id]
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+ Summarize dataset: 22%|██▏ | 23/106 [00:13<01:11, 1.16it/s, scatter rider_id, trip_id]
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+ Summarize dataset: 23%|██▎ | 24/106 [00:13<00:57, 1.43it/s, scatter rider_id, trip_id]
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+ Summarize dataset: 23%|██▎ | 24/106 [00:13<00:57, 1.43it/s, scatter pickup_lat, trip_id]
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+ Summarize dataset: 24%|██▎ | 25/106 [00:13<00:46, 1.72it/s, scatter pickup_lat, trip_id]
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+ Summarize dataset: 24%|██▎ | 25/106 [00:13<00:46, 1.72it/s, scatter pickup_lng, trip_id]
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+ Summarize dataset: 25%|██▍ | 26/106 [00:13<00:37, 2.14it/s, scatter pickup_lng, trip_id]
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+ Summarize dataset: 25%|██▍ | 26/106 [00:13<00:37, 2.14it/s, scatter drop_lat, trip_id]
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+ Summarize dataset: 25%|██▌ | 27/106 [00:13<00:29, 2.69it/s, scatter drop_lat, trip_id]
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+ Summarize dataset: 25%|██▌ | 27/106 [00:13<00:29, 2.69it/s, scatter drop_lng, trip_id]
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+ Summarize dataset: 26%|██▋ | 28/106 [00:13<00:23, 3.31it/s, scatter drop_lng, trip_id]
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+ Summarize dataset: 26%|██▋ | 28/106 [00:13<00:23, 3.31it/s, scatter distance_km, trip_id]
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+ Summarize dataset: 27%|██▋ | 29/106 [00:14<00:19, 3.91it/s, scatter distance_km, trip_id]
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+ Summarize dataset: 27%|██▋ | 29/106 [00:14<00:19, 3.91it/s, scatter fare_amount, trip_id]
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+ Summarize dataset: 28%|██▊ | 30/106 [00:14<00:16, 4.55it/s, scatter fare_amount, trip_id]
195
+ Summarize dataset: 28%|██▊ | 30/106 [00:14<00:16, 4.55it/s, scatter trip_id, driver_id]
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+ Summarize dataset: 29%|██▉ | 31/106 [00:14<00:14, 5.14it/s, scatter trip_id, driver_id]
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+ Summarize dataset: 29%|██▉ | 31/106 [00:14<00:14, 5.14it/s, scatter driver_id, driver_id]
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+ Summarize dataset: 30%|███ | 32/106 [00:14<00:12, 5.75it/s, scatter driver_id, driver_id]
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+ Summarize dataset: 30%|███ | 32/106 [00:14<00:12, 5.75it/s, scatter rider_id, driver_id]
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+ Summarize dataset: 31%|███ | 33/106 [00:14<00:11, 6.33it/s, scatter rider_id, driver_id]
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+ Summarize dataset: 31%|███ | 33/106 [00:14<00:11, 6.33it/s, scatter pickup_lat, driver_id]
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+ Summarize dataset: 32%|███▏ | 34/106 [00:14<00:10, 6.62it/s, scatter pickup_lat, driver_id]
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+ Summarize dataset: 32%|███▏ | 34/106 [00:14<00:10, 6.62it/s, scatter pickup_lng, driver_id]
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+ Summarize dataset: 33%|███▎ | 35/106 [00:14<00:10, 7.08it/s, scatter pickup_lng, driver_id]
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+ Summarize dataset: 33%|███▎ | 35/106 [00:14<00:10, 7.08it/s, scatter drop_lat, driver_id]
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+ Summarize dataset: 34%|███▍ | 36/106 [00:15<00:09, 7.37it/s, scatter drop_lat, driver_id]
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+ Summarize dataset: 54%|█████▍ | 57/106 [00:46<00:30, 1.60it/s, scatter trip_id, pickup_lng]
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+ Summarize dataset: 62%|██████▏ | 66/106 [00:47<00:05, 7.55it/s, scatter trip_id, drop_lat]
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+ Summarize dataset: 65%|██████▌ | 69/106 [00:47<00:04, 8.34it/s, scatter pickup_lat, drop_lat]
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+ Summarize dataset: 68%|██████▊ | 72/106 [00:47<00:04, 8.09it/s, scatter drop_lng, drop_lat]
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+ Summarize dataset: 69%|██████▉ | 73/106 [00:48<00:03, 8.43it/s, scatter distance_km, drop_lat]
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+ Summarize dataset: 70%|██████▉ | 74/106 [00:48<00:04, 7.70it/s, scatter distance_km, drop_lat]
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+ Summarize dataset: 70%|██████▉ | 74/106 [00:48<00:04, 7.70it/s, scatter fare_amount, drop_lat]
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+ Summarize dataset: 71%|███████ | 75/106 [00:48<00:03, 8.05it/s, scatter fare_amount, drop_lat]
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+ Summarize dataset: 71%|███████ | 75/106 [00:48<00:03, 8.05it/s, scatter trip_id, drop_lng]
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+ Summarize dataset: 72%|███████▏ | 76/106 [00:48<00:03, 8.24it/s, scatter trip_id, drop_lng]
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+ Summarize dataset: 72%|███████▏ | 76/106 [00:48<00:03, 8.24it/s, scatter driver_id, drop_lng]
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+ Summarize dataset: 73%|███████▎ | 77/106 [00:48<00:03, 8.24it/s, scatter rider_id, drop_lng]
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+ Summarize dataset: 74%|███████▎ | 78/106 [00:48<00:03, 8.81it/s, scatter rider_id, drop_lng]
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+ Summarize dataset: 74%|███████▎ | 78/106 [00:48<00:03, 8.81it/s, scatter pickup_lat, drop_lng]
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+ Summarize dataset: 75%|███████▍ | 79/106 [00:48<00:03, 8.96it/s, scatter pickup_lat, drop_lng]
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+ Summarize dataset: 75%|███████▍ | 79/106 [00:48<00:03, 8.96it/s, scatter pickup_lng, drop_lng]
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+ Summarize dataset: 75%|███████▌ | 80/106 [00:48<00:02, 8.96it/s, scatter drop_lat, drop_lng]
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+ Summarize dataset: 76%|███████▋ | 81/106 [00:48<00:02, 9.44it/s, scatter drop_lat, drop_lng]
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+ Summarize dataset: 76%|███████▋ | 81/106 [00:48<00:02, 9.44it/s, scatter drop_lng, drop_lng]
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+ Summarize dataset: 77%|███████▋ | 82/106 [00:49<00:02, 9.44it/s, scatter distance_km, drop_lng]
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+ Summarize dataset: 78%|███████▊ | 83/106 [00:49<00:02, 8.99it/s, scatter distance_km, drop_lng]
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+ Summarize dataset: 78%|███████▊ | 83/106 [00:49<00:02, 8.99it/s, scatter fare_amount, drop_lng]
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+ Summarize dataset: 79%|███████▉ | 84/106 [00:49<00:02, 8.99it/s, scatter trip_id, distance_km]
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+ Summarize dataset: 80%|████████ | 85/106 [00:49<00:02, 8.51it/s, scatter trip_id, distance_km]
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+ Summarize dataset: 80%|████████ | 85/106 [00:49<00:02, 8.51it/s, scatter driver_id, distance_km]
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+ Summarize dataset: 81%|████████ | 86/106 [00:49<00:02, 8.51it/s, scatter rider_id, distance_km]
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+ Summarize dataset: 82%|████████▏ | 87/106 [00:49<00:02, 8.20it/s, scatter rider_id, distance_km]
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+ Summarize dataset: 82%|████████▏ | 87/106 [00:49<00:02, 8.20it/s, scatter pickup_lat, distance_km]
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+ Summarize dataset: 83%|████████▎ | 88/106 [00:49<00:02, 7.70it/s, scatter pickup_lat, distance_km]
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+ Summarize dataset: 83%|████████▎ | 88/106 [00:49<00:02, 7.70it/s, scatter pickup_lng, distance_km]
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+ Summarize dataset: 84%|████████▍ | 89/106 [00:50<00:02, 7.61it/s, scatter pickup_lng, distance_km]
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+ Summarize dataset: 84%|████████▍ | 89/106 [00:50<00:02, 7.61it/s, scatter drop_lat, distance_km]
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+ Summarize dataset: 85%|████████▍ | 90/106 [00:50<00:02, 7.95it/s, scatter drop_lat, distance_km]
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+ Summarize dataset: 85%|████████▍ | 90/106 [00:50<00:02, 7.95it/s, scatter drop_lng, distance_km]
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+ Summarize dataset: 86%|████████▌ | 91/106 [00:50<00:01, 7.81it/s, scatter distance_km, distance_km]
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+ Summarize dataset: 87%|████████▋ | 92/106 [00:50<00:01, 7.84it/s, scatter distance_km, distance_km]
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+ Summarize dataset: 87%|████████▋ | 92/106 [00:50<00:01, 7.84it/s, scatter fare_amount, distance_km]
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+ Summarize dataset: 88%|████████▊ | 93/106 [00:50<00:01, 7.86it/s, scatter trip_id, fare_amount]
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+ Summarize dataset: 89%|████████▊ | 94/106 [00:50<00:01, 8.37it/s, scatter trip_id, fare_amount]
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+ Summarize dataset: 89%|████████▊ | 94/106 [00:50<00:01, 8.37it/s, scatter driver_id, fare_amount]
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+ Summarize dataset: 90%|████████▉ | 95/106 [00:50<00:01, 8.60it/s, scatter driver_id, fare_amount]
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+ Summarize dataset: 90%|████████▉ | 95/106 [00:50<00:01, 8.60it/s, scatter rider_id, fare_amount]
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+ Summarize dataset: 91%|█████████ | 96/106 [00:50<00:01, 8.64it/s, scatter rider_id, fare_amount]
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+ Summarize dataset: 91%|█████████ | 96/106 [00:50<00:01, 8.64it/s, scatter pickup_lat, fare_amount]
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+ Summarize dataset: 92%|█████████▏| 97/106 [00:50<00:01, 8.70it/s, scatter pickup_lat, fare_amount]
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+ Summarize dataset: 92%|█████████▏| 97/106 [00:50<00:01, 8.70it/s, scatter pickup_lng, fare_amount]
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+ Summarize dataset: 92%|█████████▏| 98/106 [00:51<00:00, 8.89it/s, scatter pickup_lng, fare_amount]
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+ Summarize dataset: 92%|█████████▏| 98/106 [00:51<00:00, 8.89it/s, scatter drop_lat, fare_amount]
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+ Summarize dataset: 93%|█████████▎| 99/106 [00:51<00:00, 7.61it/s, scatter drop_lat, fare_amount]
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+ Summarize dataset: 93%|█████████▎| 99/106 [00:51<00:00, 7.61it/s, scatter drop_lng, fare_amount]
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+ Summarize dataset: 94%|█████████▍| 100/106 [00:51<00:00, 8.13it/s, scatter drop_lng, fare_amount]
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+ Summarize dataset: 94%|█████████▍| 100/106 [00:51<00:00, 8.13it/s, scatter distance_km, fare_amount]
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+ Summarize dataset: 95%|█████████▌| 101/106 [00:51<00:00, 8.37it/s, scatter distance_km, fare_amount]
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+ Summarize dataset: 95%|█████████▌| 101/106 [00:51<00:00, 8.37it/s, scatter fare_amount, fare_amount]
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+ Summarize dataset: 96%|█████████▌| 102/106 [00:51<00:00, 8.70it/s, scatter fare_amount, fare_amount]
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+ Summarize dataset: 94%|█████████▍| 102/108 [00:51<00:00, 8.70it/s, Missing diagram bar]
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+ Summarize dataset: 95%|█████████▌| 103/108 [00:52<00:01, 4.24it/s, Missing diagram bar]
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+ Summarize dataset: 95%|█████████▌| 103/108 [00:52<00:01, 4.24it/s, Missing diagram matrix]
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+ Summarize dataset: 96%|█████████▋| 104/108 [00:52<00:01, 3.10it/s, Missing diagram matrix]
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+ Summarize dataset: 96%|█████████▋| 104/108 [00:52<00:01, 3.10it/s, Take sample]
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+ Summarize dataset: 97%|█████████▋| 105/108 [00:52<00:00, 3.10it/s, Detecting duplicates]
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+ Summarize dataset: 98%|█████████▊| 106/108 [00:52<00:00, 4.15it/s, Detecting duplicates]
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+ Summarize dataset: 98%|█████████▊| 106/108 [00:52<00:00, 4.15it/s, Get alerts]
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+ Summarize dataset: 99%|█████████▉| 107/108 [00:52<00:00, 4.15it/s, Get reproduction details]
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+ Summarize dataset: 100%|██████████| 108/108 [00:52<00:00, 4.15it/s, Completed]
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+ Summarize dataset: 100%|██████████| 108/108 [00:52<00:00, 2.04it/s, Completed]
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+ Generate report structure: 0%| | 0/1 [00:00<?, ?it/s]INFO:src.api.app:SSE stream cancelled for session 1963e777-4930-4200-9ada-ad742e5de6bc
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+ INFO:src.api.app:SSE stream closed for session 1963e777-4930-4200-9ada-ad742e5de6bc
344
+ INFO: 10.16.34.155:42907 - "GET / HTTP/1.1" 200 OK
345
+ INFO: 10.16.34.155:42907 - "GET /index.css HTTP/1.1" 200 OK
346
+ INFO: 10.16.34.155:42907 - "GET /favicon.ico HTTP/1.1" 200 OK
347
+ Generate report structure: 100%|██████████| 1/1 [00:48<00:00, 48.28s/it]
348
+ Generate report structure: 100%|██████████| 1/1 [00:48<00:00, 48.29s/it]
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+ Render HTML: 0%| | 0/1 [00:00<?, ?it/s]
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+ Render HTML: 100%|██████████| 1/1 [00:01<00:00, 1.94s/it]
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+ Render HTML: 100%|██████████| 1/1 [00:01<00:00, 1.96s/it]
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+ Export report to file: 0%| | 0/1 [00:00<?, ?it/s]
353
+ Export report to file: 100%|██████████| 1/1 [00:00<00:00, 51.99it/s]
354
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_completed, msg=✓ Completed: generate_ydata_profiling_report
355
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 6
356
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
357
+ [SSE] Successfully queued event to subscriber 1
358
+ ✓ Tool completed successfully
359
+ [SSE] GOT event from queue: tool_completed
360
+ 💾 Checkpoint saved: iteration 1, last tool: generate_ydata_profiling_report
361
+ 📊 EVALUATE: Interpreting results...
362
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
363
+ Interpretation: The result indicates that a comprehensive profiling report has been generated, which will detail the statistical properties, distributions, correlations, and missing values of features in the Uber trips dataset. This report is essential for identifying important features but does not itself interpret them—manual review is required to extract actionable insights.
364
+ Answered: False (confidence: 40%)
365
+ Should stop: False
366
+ Next questions: ['Which features show the strongest correlation with trip duration or fare amount?', 'Are there unexpected patterns or outliers in key features (e.g., pickup/dropoff times, locations)?', 'How do missing values or data quality issues impact feature importance?', 'What are the most surprising or counterintuitive relationships between features?']
367
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
368
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 7
369
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
370
+ [SSE] Successfully queued event to subscriber 1
371
+
372
+ ── Iteration 2/8 ──
373
+ 🤔 REASON: Deciding next action...
374
+ [SSE] GOT event from queue: finding_discovered
375
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
376
+ Status: investigating
377
+ Reasoning: The profiling report has been generated but not yet analyzed for feature importance. The next step is to analyze correlations between features, particularly focusing on 'fare_amount' as the target, to identify the most influential features driving fare pricing.
378
+ Tool: analyze_correlations
379
+ Hypothesis: We expect to identify strong correlations between 'fare_amount' and features like 'distance_km', 'city', and temporal features (e.g., 'pickup_time'), which will highlight the most important features in the dataset.
380
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
381
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 8
382
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
383
+ [SSE] Successfully queued event to subscriber 1
384
+ ⚡ ACT: Executing analyze_correlations...
385
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: analyze_correlations
386
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 9
387
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
388
+ [SSE] Successfully queued event to subscriber 1
389
+ 📋 Final parameters: ['file_path', 'target']
390
+ [SSE] GOT event from queue: reasoning_step
391
+ [SSE] GOT event from queue: tool_executing
392
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_completed, msg=✓ Completed: analyze_correlations
393
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 10
394
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
395
+ [SSE] Successfully queued event to subscriber 1
396
+ ✓ Tool completed successfully
397
+ 💾 Checkpoint saved: iteration 2, last tool: analyze_correlations
398
+ 📊 EVALUATE: Interpreting results...
399
+ [SSE] GOT event from queue: tool_completed
400
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
401
+ Interpretation: The `analyze_correlations` tool completed execution, meaning it has computed the statistical relationships between 'fare_amount' and other features in the dataset. However, the raw result does not provide interpretable insights—it only confirms the correlation analysis ran successfully. The actual correlation values and their significance are missing, so we cannot yet determine which features are important drivers of fare amounts.
402
+ Answered: False (confidence: 20%)
403
+ Should stop: False
404
+ Next questions: ["What are the exact correlation coefficients and p-values between 'fare_amount' and other features (e.g., 'distance_km', 'trip_duration', 'surge_multiplier')?", 'Are the observed correlations statistically significant, or could they be artifacts of data quality issues (e.g., outliers, duplicates)?', "How do non-linear relationships (e.g., between 'distance_km' and 'fare_amount') affect feature importance rankings?"]
405
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
406
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 11
407
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
408
+ [SSE] Successfully queued event to subscriber 1
409
+
410
+ ── Iteration 3/8 ──
411
+ 🤔 REASON: Deciding next action...
412
+ [SSE] GOT event from queue: finding_discovered
413
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
414
+ Status: investigating
415
+ Reasoning: The `analyze_correlations` tool was executed but the actual correlation values and their significance are missing. To identify the most important features, we need to visualize the correlation results to interpret the relationships between 'fare_amount' and other features.
416
+ Tool: generate_interactive_correlation_heatmap
417
+ Hypothesis: We expect to visually identify strong correlations between 'fare_amount' and features like 'distance_km', 'city', and temporal features (e.g., 'pickup_time'), which will highlight the most important features in the dataset.
418
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
419
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 12
420
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
421
+ [SSE] Successfully queued event to subscriber 1
422
+ ⚡ ACT: Executing generate_interactive_correlation_heatmap...
423
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: generate_interactive_correlation_heat
424
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 13
425
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
426
+ [SSE] Successfully queued event to subscriber 1
427
+ 📋 Final parameters: ['file_path']
428
+ [SSE] GOT event from queue: reasoning_step
429
+ [SSE] GOT event from queue: tool_executing
430
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_completed, msg=✓ Completed: generate_interactive_correlation_heat
431
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 14
432
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
433
+ [SSE] Successfully queued event to subscriber 1
434
+ ✓ Tool completed successfully
435
+ 💾 Checkpoint saved: iteration 3, last tool: generate_interactive_correlation_heatmap
436
+ 📊 EVALUATE: Interpreting results...
437
+ [SSE] GOT event from queue: tool_completed
438
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
439
+ Interpretation: The interactive correlation heatmap generation means we now have a visual tool to explore and quantify the strength and direction of relationships between features, particularly how they relate to 'fare_amount'. This is a critical step toward identifying important features, but the raw output alone does not provide direct answers—it requires manual interpretation to extract actionable insights.
440
+ Answered: False (confidence: 40%)
441
+ Should stop: False
442
+ Next questions: ["Which features show statistically significant correlations with 'fare_amount', and what is the magnitude of these relationships?", "Are there any unexpected or counterintuitive correlations (e.g., weak correlation between 'distance_km' and 'fare_amount') that warrant further investigation?", "How do temporal features (e.g., 'pickup_time', 'day_of_week') interact with other variables, and do they reveal patterns like surge pricing or demand fluctuations?", 'Are there multicollinearity issues among features that could distort feature importance in predictive models?']
443
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
444
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 15
445
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
446
+ [SSE] Successfully queued event to subscriber 1
447
+
448
+ ── Iteration 4/8 ──
449
+ 🤔 REASON: Deciding next action...
450
+ [SSE] GOT event from queue: finding_discovered
451
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
452
+ Status: investigating
453
+ Reasoning: The interactive correlation heatmap has been generated but not yet interpreted to identify the most important features driving 'fare_amount'. We need to extract and quantify feature importance to answer the user's question confidently.
454
+ Tool: auto_feature_selection
455
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features for further analysis.
456
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
457
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 16
458
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
459
+ [SSE] Successfully queued event to subscriber 1
460
+ ⚡ ACT: Executing auto_feature_selection...
461
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
462
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 17
463
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
464
+ [SSE] Successfully queued event to subscriber 1
465
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
466
+ ⚠️ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed (attempt 1/2): Unsupported file format: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
467
+ Retrying in 1.0s...
468
+ [SSE] GOT event from queue: reasoning_step
469
+ [SSE] GOT event from queue: tool_executing
470
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
471
+ ❌ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed after 2 attempts
472
+ 💡 Suggested fallback tools: []
473
+ ❌ Tool failed: Unsupported file format: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
474
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
475
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 18
476
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
477
+ [SSE] Successfully queued event to subscriber 1
478
+ 📊 EVALUATE: Interpreting results...
479
+ [SSE] GOT event from queue: tool_failed
480
+ INFO: 10.16.34.155:21041 - "GET / HTTP/1.1" 200 OK
481
+ INFO: 10.16.4.123:60395 - "GET /index.css HTTP/1.1" 200 OK
482
+ INFO: 10.16.34.155:21041 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 304 Not Modified
483
+ INFO: 10.16.34.155:21041 - "GET /favicon.ico HTTP/1.1" 200 OK
484
+ INFO: 10.16.34.155:21041 - "GET /favicon.ico HTTP/1.1" 200 OK
485
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
486
+ Interpretation: The feature importance analysis failed due to an unsupported file format (HTML heatmap instead of raw data), meaning we cannot yet directly quantify which features most influence 'fare_amount'. However, prior steps (correlation analysis and profiling) provide indirect evidence about potential important features, though manual interpretation is still required.
487
+ Answered: False (confidence: 30%)
488
+ Should stop: False
489
+ Next questions: ["What are the exact correlation values between 'fare_amount' and other features (e.g., 'distance_km', 'pickup_time') from the raw correlation analysis?", 'Can we extract the numerical correlation matrix from the interactive heatmap to perform feature importance ranking?', 'Are there non-linear relationships or interaction effects (e.g., distance × city) that simple correlation misses?']
490
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
491
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 19
492
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
493
+ [SSE] Successfully queued event to subscriber 1
494
+
495
+ ── Iteration 5/8 ──
496
+ 🤔 REASON: Deciding next action...
497
+ [SSE] GOT event from queue: finding_discovered
498
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
499
+ Status: investigating
500
+ Reasoning: The `auto_feature_selection` tool failed due to an unsupported file format, and prior steps (correlation heatmap and profiling) provide indirect evidence but no direct quantification of feature importance. Running `train_with_autogluon` will train a model and explicitly rank features by their importance to 'fare_amount', addressing the user's question directly.
501
+ Tool: train_with_autogluon
502
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
503
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
504
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 20
505
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
506
+ [SSE] Successfully queued event to subscriber 1
507
+ ⚡ ACT: Executing train_with_autogluon...
508
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: train_with_autogluon
509
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 21
510
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
511
+ [SSE] Successfully queued event to subscriber 1
512
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'time_limit', 'presets']
513
+ [SSE] GOT event from queue: reasoning_step
514
+ [SSE] GOT event from queue: tool_executing
515
+ [SSE] ENDPOINT: Client connected for session_id=1963e777-4930-4200-9ada-ad742e5de6bc
516
+ [SSE] Queue registered, total subscribers: 1
517
+ INFO: 10.16.4.123:23205 - "GET /api/progress/stream/1963e777-4930-4200-9ada-ad742e5de6bc HTTP/1.1" 200 OK
518
+ [SSE] SENDING connection event to client
519
+ [SSE] No history to replay (fresh session)
520
+ [SSE] Starting event stream loop for session 1963e777-4930-4200-9ada-ad742e5de6bc
521
+
522
+ 🚀 AutoGluon Training Starting...
523
+ 📁 Dataset: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
524
+ 🎯 Target: fare_amount
525
+ ⏱️ Time limit: 1800s
526
+ 📊 Presets: medium_quality
527
+ ⚠️ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed (attempt 1/2): Failed to load data: Error tokenizing data. C error: Expected 1 fields in line 7, saw 3
528
+
529
+ Retrying in 1.0s...
530
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'time_limit', 'presets']
531
+
532
+ 🚀 AutoGluon Training Starting...
533
+ 📁 Dataset: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
534
+ 🎯 Target: fare_amount
535
+ ⏱️ Time limit: 1800s
536
+ 📊 Presets: medium_quality
537
+ ❌ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed after 2 attempts
538
+ 💡 Suggested fallback tools: []
539
+ ❌ Tool failed: Failed to load data: Error tokenizing data. C error: Expected 1 fields in line 7, saw 3
540
+
541
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: train_with_autogluon
542
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 22
543
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
544
+ [SSE] Successfully queued event to subscriber 1
545
+ 📊 EVALUATE: Interpreting results...
546
+ [SSE] GOT event from queue: tool_failed
547
+ INFO:src.api.app:SSE stream cancelled for session 1963e777-4930-4200-9ada-ad742e5de6bc
548
+ INFO:src.api.app:SSE stream closed for session 1963e777-4930-4200-9ada-ad742e5de6bc
549
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
550
+ Interpretation: The AutoGluon training failed due to a data tokenization error, likely caused by malformed input data (e.g., inconsistent delimiters or corrupted rows). This means we cannot yet directly quantify feature importance for 'fare_amount' using automated methods, and prior indirect evidence (e.g., correlations) remains unvalidated.
551
+ Answered: False (confidence: 20%)
552
+ Should stop: False
553
+ Next questions: ['What is the root cause of the tokenization error (e.g., delimiter mismatch, corrupted rows), and how can the data be cleaned to resolve it?', 'Can we manually inspect the raw data (e.g., line 7) to identify and fix formatting issues before retrying automated feature importance analysis?', 'Are there alternative methods (e.g., manual feature engineering, simpler models) to estimate feature importance given the current data limitations?']
554
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
555
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 23
556
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
557
+ [SSE] Successfully queued event to subscriber 1
558
+
559
+ ── Iteration 6/8 ──
560
+ 🤔 REASON: Deciding next action...
561
+ [SSE] GOT event from queue: finding_discovered
562
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
563
+ Status: investigating
564
+ Reasoning: The previous `auto_feature_selection` and `train_with_autogluon` steps failed due to incorrect input formats, but we have an interactive correlation heatmap that visually suggests relationships. To quantify feature importance, we need to extract the raw correlation data or re-run feature selection on the original dataset (not the HTML file).
565
+ Tool: auto_feature_selection
566
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
567
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
568
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 24
569
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
570
+ [SSE] Successfully queued event to subscriber 1
571
+ ⚡ ACT: Executing auto_feature_selection...
572
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
573
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 25
574
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
575
+ [SSE] Successfully queued event to subscriber 1
576
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
577
+ ❌ Tool failed: File not found: /tmp/data_science_agent/data/processed_data.parquet
578
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
579
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 26
580
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
581
+ [SSE] Successfully queued event to subscriber 1
582
+ 📊 EVALUATE: Interpreting results...
583
+ [SSE] GOT event from queue: reasoning_step
584
+ [SSE] GOT event from queue: tool_executing
585
+ [SSE] GOT event from queue: tool_failed
586
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
587
+ Interpretation: The failure of the `auto_feature_selection` tool due to a missing file means we cannot directly quantify feature importance for 'fare_amount' at this stage. This blocks our ability to answer the original question about important features, though prior indirect evidence (e.g., correlations) suggests potential candidates like 'distance_km' and 'city'. The root cause appears to be a file path or data pipeline issue, not a methodological limitation.
588
+ Answered: False (confidence: 20%)
589
+ Should stop: False
590
+ Next questions: ['What is the correct file path or format for the processed data to enable feature importance analysis?', 'Can we rerun prior correlation analyses on the raw data to extract quantifiable feature importance metrics (e.g., correlation coefficients) as a temporary workaround?', 'Are there data quality issues (e.g., missing values, incorrect formats) in the raw data that caused the AutoGluon tokenization error and need to be addressed first?']
591
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
592
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 27
593
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
594
+ [SSE] Successfully queued event to subscriber 1
595
+
596
+ ── Iteration 7/8 ──
597
+ 🤔 REASON: Deciding next action...
598
+ [SSE] GOT event from queue: finding_discovered
599
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
600
+ Status: investigating
601
+ Reasoning: The previous attempts to quantify feature importance failed due to file format issues or data errors. The most direct next step is to re-run `auto_feature_selection` on the raw dataset (not the HTML heatmap) to obtain a ranked list of important features for 'fare_amount'.
602
+ Tool: auto_feature_selection
603
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
604
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
605
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 28
606
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
607
+ [SSE] Successfully queued event to subscriber 1
608
+ ⚡ ACT: Executing auto_feature_selection...
609
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
610
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 29
611
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
612
+ [SSE] Successfully queued event to subscriber 1
613
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
614
+ ❌ Tool failed: File not found: /tmp/data_science_agent/data/raw_dataset.parquet
615
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
616
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 30
617
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
618
+ [SSE] Successfully queued event to subscriber 1
619
+ 📊 EVALUATE: Interpreting results...
620
+ [SSE] GOT event from queue: reasoning_step
621
+ [SSE] GOT event from queue: tool_executing
622
+ [SSE] GOT event from queue: tool_failed
623
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
624
+ Interpretation: The failure due to a missing file means we cannot directly quantify feature importance for 'fare_amount' at this stage, blocking the original question. This is likely a data pipeline or file path issue, not a methodological limitation, and requires immediate resolution before proceeding with feature analysis.
625
+ Answered: False (confidence: 10%)
626
+ Should stop: False
627
+ Next questions: ['What is the correct file path for the raw dataset, and does the file exist in the expected location?', 'Are there alternative data sources or formats (e.g., CSV, JSON) available for the same dataset to bypass the file path issue?', 'Can we validate the integrity of the data pipeline to ensure no corruption or misalignment occurred during prior steps?']
628
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
629
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 31
630
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
631
+ [SSE] Successfully queued event to subscriber 1
632
+
633
+ ── Iteration 8/8 ──
634
+ 🤔 REASON: Deciding next action...
635
+ [SSE] GOT event from queue: finding_discovered
636
+ INFO:src.api.app:[ASYNC] Created new session: f0b5e746...
637
+ INFO:src.api.app:[ASYNC] File saved: intraday_tips.csv
638
+ INFO: 10.16.4.123:13810 - "POST /run-async HTTP/1.1" 200 OK
639
+ [SSE] ENDPOINT: Client connected for session_id=f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77
640
+ [SSE] Queue registered, total subscribers: 1
641
+ INFO: 10.16.34.155:48122 - "GET /api/progress/stream/f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77 HTTP/1.1" 200 OK
642
+ [SSE] SENDING connection event to client
643
+ [SSE] No history to replay (fresh session)
644
+ [SSE] Starting event stream loop for session f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77
645
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
646
+ Status: investigating
647
+ Reasoning: The previous attempts to quantify feature importance failed due to file format issues (HTML instead of raw data). We need to ensure the raw data is used for feature selection to directly answer the user's question about important features.
648
+ Tool: auto_feature_selection
649
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
650
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
651
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 32
652
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
653
+ [SSE] Successfully queued event to subscriber 1
654
+ ⚡ ACT: Executing auto_feature_selection...
655
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
656
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 33
657
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
658
+ [SSE] Successfully queued event to subscriber 1
659
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
660
+ ❌ Tool failed: File not found: /tmp/data_science_agent/data/raw_data.parquet
661
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
662
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 34
663
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
664
+ [SSE] Successfully queued event to subscriber 1
665
+ 📊 EVALUATE: Interpreting results...
666
+ [SSE] GOT event from queue: reasoning_step
667
+ [SSE] GOT event from queue: tool_executing
668
+ [SSE] GOT event from queue: tool_failed
669
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
670
+ Interpretation: The result indicates a critical data pipeline failure—specifically, the input file cannot be located—blocking any direct analysis of feature importance for 'fare_amount'. This means the original question remains unanswered, and no new insights about feature importance can be derived until the file path or data access issue is resolved.
671
+ Answered: False (confidence: 10%)
672
+ Should stop: False
673
+ Next questions: ['What is the correct file path for the dataset, and does the file exist in the expected location?', 'Are there alternative datasets or backups available to proceed with feature importance analysis?', 'Can we validate the data pipeline to ensure no other files are missing or corrupted before retrying?', 'Should we prioritize debugging the file access issue or switch to indirect methods (e.g., correlation analysis) to infer feature importance temporarily?']
674
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
675
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 35
676
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
677
+ [SSE] Successfully queued event to subscriber 1
678
+
679
+ ============================================================
680
+ 📝 SYNTHESIZE: Building final answer from 8 findings...
681
+ ============================================================
682
+ [SSE] GOT event from queue: finding_discovered
683
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
684
+ [DEBUG] Report tool detected: generate_ydata_profiling_report
685
+ [DEBUG] nested_result keys: ['success', 'report_path', 'message', 'statistics']
686
+ [DEBUG] Report path found: /tmp/data_science_agent/uber_trips_profiling_report.html
687
+ [DEBUG] Added to artifacts[reports], total reports: 1
688
+ [DEBUG] Extracting plot from tool: generate_interactive_correlation_heatmap
689
+ [DEBUG] result keys: ['success', 'tool', 'arguments', 'result']
690
+ [DEBUG] nested_result keys: ['status', 'plot_type', 'output_path', 'num_features']
691
+ [DEBUG] output_path in nested_result: True
692
+ [DEBUG] output_path value: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
693
+ [DEBUG] Added plot to array:
694
+ [DEBUG] title: Correlation Heatmap
695
+ [DEBUG] url: /outputs/plots/interactive/correlation_heatmap.html
696
+ [DEBUG] type: html
697
+ [DEBUG] Merging 1 reports into plots array
698
+ [DEBUG] Added report to plots array: title='Generate Ydata Profiling Report', url='/outputs//tmp/data_science_agent/uber_trips_profiling_report.html'
699
+ [DEBUG] Final plots array length: 2
700
+
701
+ ✅ Reasoning loop completed in 233.75s
702
+ Iterations: 8
703
+ Tools used: generate_ydata_profiling_report, analyze_correlations, generate_interactive_correlation_heatmap, auto_feature_selection, train_with_autogluon
704
+ API calls: 18
705
+ INFO:src.api.app:[BACKGROUND] Analysis completed for session 2df5d6d1...
706
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=analysis_complete, msg=✅ Analysis completed successfully!
707
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 36
708
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
709
+ [SSE] Successfully queued event to subscriber 1
710
+ [SSE] GOT event from queue: analysis_complete
711
+ INFO:src.api.app:SSE stream closed for session 2df5d6d1-f23c-48fb-b353-373e7693b75e
712
+ INFO:src.api.app:Found file at: /tmp/data_science_agent/uber_trips_profiling_report.html
713
+ INFO: 10.16.4.123:27264 - "GET /outputs//tmp/data_science_agent/uber_trips_profiling_report.html HTTP/1.1" 200 OK
714
+ INFO:src.api.app:Found file at: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
715
+ INFO: 10.16.4.123:46507 - "GET /outputs/plots/interactive/correlation_heatmap.html HTTP/1.1" 200 OK
716
+ Exception ignored in: <function ResourceTracker.__del__ at 0x7fbd3d6aa980>
717
+ Traceback (most recent call last):
718
+ File "/usr/local/lib/python3.12/multiprocessing/resource_tracker.py", line 77, in __del__
719
+ File "/usr/local/lib/python3.12/multiprocessing/resource_tracker.py", line 86, in _stop
720
+ File "/usr/local/lib/python3.12/multiprocessing/resource_tracker.py", line 111, in _stop_locked
721
+ ChildProcessError: [Errno 10] No child processes
722
+ Exception ignored in: <function ResourceTracker.__del__ at 0x7f82849ba980>
723
+ Traceback (most recent call last):
724
+ File "/usr/local/lib/python3.12/multiprocessing/resource_tracker.py", line 77, in __del__
725
+ File "/usr/local/lib/python3.12/multiprocessing/resource_tracker.py", line 86, in _stop
726
+ File "/usr/local/lib/python3.12/multiprocessing/resource_tracker.py", line 111, in _stop_locked
727
+ ChildProcessError: [Errno 10] No child processes
728
+ INFO: 10.16.4.123:8626 - "GET / HTTP/1.1" 200 OK
729
+ INFO:src.api.app:SSE stream cancelled for session f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77
730
+ INFO:src.api.app:SSE stream closed for session f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77
731
+ INFO: 10.16.4.123:8626 - "GET /index.css HTTP/1.1" 200 OK
732
+ INFO: 10.16.34.155:26718 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 304 Not Modified
733
+ INFO: 10.16.4.123:8626 - "GET /favicon.ico HTTP/1.1" 200 OK
734
+ INFO: 10.16.4.123:48958 - "GET / HTTP/1.1" 200 OK
735
+ INFO: 10.16.34.155:49146 - "GET /index.css HTTP/1.1" 200 OK
736
+ INFO: 10.16.34.155:49146 - "GET /favicon.ico HTTP/1.1" 200 OK
737
+ INFO: 10.16.4.123:34863 - "GET / HTTP/1.1" 200 OK
738
+ INFO: 10.16.4.123:34863 - "GET /index.css HTTP/1.1" 200 OK
739
+ INFO: 10.16.34.155:10498 - "GET /favicon.ico HTTP/1.1" 200 OK
740
+ INFO: 10.16.4.123:37099 - "GET /?error=server_error&error_code=unexpected_failure&error_description=Multiple+accounts+with+the+same+email+address+in+the+same+linking+domain+detected%3A+default HTTP/1.1" 200 OK
741
+ INFO: 10.16.34.155:13347 - "GET /index.css HTTP/1.1" 200 OK
742
+ INFO: 10.16.34.155:13347 - "GET /favicon.ico HTTP/1.1" 200 OK
743
+ INFO: 10.16.4.123:51446 - "GET /?error=server_error&error_code=unexpected_failure&error_description=Multiple+accounts+with+the+same+email+address+in+the+same+linking+domain+detected%3A+default HTTP/1.1" 200 OK
744
+ INFO: 10.16.4.123:51446 - "GET /index.css HTTP/1.1" 200 OK
745
+ INFO: 10.16.34.155:52886 - "GET /favicon.ico HTTP/1.1" 200 OK
746
+ INFO: 10.16.4.123:9334 - "GET / HTTP/1.1" 200 OK
747
+ INFO: 10.16.34.155:51128 - "GET /index.css HTTP/1.1" 200 OK
748
+ INFO: 10.16.34.155:51128 - "GET /favicon.ico HTTP/1.1" 200 OK
749
+ INFO: 10.16.4.123:9334 - "GET /favicon.ico HTTP/1.1" 200 OK
750
+ INFO:src.api.app:[ASYNC] Created new session: 01c885ba...
751
+ INFO:src.api.app:[ASYNC] File saved: Train.csv
752
+ INFO: 10.16.34.155:30739 - "POST /run-async HTTP/1.1" 200 OK
753
+ ERROR:src.api.app:Unhandled exception: <asyncio.locks.Lock object at 0x7fa993f3a990 [unlocked, waiters:1]> is bound to a different event loop
754
+ Traceback (most recent call last):
755
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/errors.py", line 164, in __call__
756
+ await self.app(scope, receive, _send)
757
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/cors.py", line 93, in __call__
758
+ await self.simple_response(scope, receive, send, request_headers=headers)
759
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/cors.py", line 144, in simple_response
760
+ await self.app(scope, receive, send)
761
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py", line 63, in __call__
762
+ await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
763
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
764
+ raise exc
765
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
766
+ await app(scope, receive, sender)
767
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in __call__
768
+ await self.app(scope, receive, send)
769
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 716, in __call__
770
+ await self.middleware_stack(scope, receive, send)
771
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 736, in app
772
+ await route.handle(scope, receive, send)
773
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 290, in handle
774
+ await self.app(scope, receive, send)
775
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/routing.py", line 118, in app
776
+ await wrap_app_handling_exceptions(app, request)(scope, receive, send)
777
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
778
+ raise exc
779
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
780
+ await app(scope, receive, sender)
781
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/routing.py", line 105, in app
782
+ await response(scope, receive, send)
783
+ File "/opt/venv/lib/python3.12/site-packages/starlette/responses.py", line 167, in __call__
784
+ await self.background()
785
+ File "/opt/venv/lib/python3.12/site-packages/starlette/background.py", line 36, in __call__
786
+ await task()
787
+ File "/opt/venv/lib/python3.12/site-packages/starlette/background.py", line 23, in __call__
788
+ await run_in_threadpool(self.func, *self.args, **self.kwargs)
789
+ File "/opt/venv/lib/python3.12/site-packages/starlette/concurrency.py", line 32, in run_in_threadpool
790
+ return await anyio.to_thread.run_sync(func)
791
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
792
+ File "/opt/venv/lib/python3.12/site-packages/anyio/to_thread.py", line 63, in run_sync
793
+ return await get_async_backend().run_sync_in_worker_thread(
794
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
795
+ File "/opt/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 2502, in run_sync_in_worker_thread
796
+ return await future
797
+ ^^^^^^^^^^^^
798
+ File "/opt/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 986, in run
799
+ result = context.run(func, *args)
800
+ ^^^^^^^^^^^^^^^^^^^^^^^^
801
+ File "/home/user/app/src/api/app.py", line 527, in run_analysis_background
802
+ loop.run_until_complete(_run_with_lock())
803
+ File "/usr/local/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete
804
+ return future.result()
805
+ ^^^^^^^^^^^^^^^
806
+ File "/home/user/app/src/api/app.py", line 472, in _run_with_lock
807
+ async with workflow_lock:
808
+ ^^^^^^^^^^^^^
809
+ File "/usr/local/lib/python3.12/asyncio/locks.py", line 14, in __aenter__
810
+ await self.acquire()
811
+ File "/usr/local/lib/python3.12/asyncio/locks.py", line 105, in acquire
812
+ fut = self._get_loop().create_future()
813
+ ^^^^^^^^^^^^^^^^
814
+ File "/usr/local/lib/python3.12/asyncio/mixins.py", line 20, in _get_loop
815
+ raise RuntimeError(f'{self!r} is bound to a different event loop')
816
+ RuntimeError: <asyncio.locks.Lock object at 0x7fa993f3a990 [unlocked, waiters:1]> is bound to a different event loop
817
+ ERROR: Exception in ASGI application
818
+ Traceback (most recent call last):
819
+ File "/opt/venv/lib/python3.12/site-packages/uvicorn/protocols/http/httptools_impl.py", line 416, in run_asgi
820
+ result = await app( # type: ignore[func-returns-value]
821
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
822
+ File "/opt/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py", line 60, in __call__
823
+ return await self.app(scope, receive, send)
824
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
825
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/applications.py", line 1135, in __call__
826
+ await super().__call__(scope, receive, send)
827
+ File "/opt/venv/lib/python3.12/site-packages/starlette/applications.py", line 107, in __call__
828
+ await self.middleware_stack(scope, receive, send)
829
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/errors.py", line 186, in __call__
830
+ raise exc
831
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/errors.py", line 164, in __call__
832
+ await self.app(scope, receive, _send)
833
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/cors.py", line 93, in __call__
834
+ await self.simple_response(scope, receive, send, request_headers=headers)
835
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/cors.py", line 144, in simple_response
836
+ await self.app(scope, receive, send)
837
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py", line 63, in __call__
838
+ await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
839
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
840
+ raise exc
841
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
842
+ await app(scope, receive, sender)
843
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in __call__
844
+ await self.app(scope, receive, send)
845
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 716, in __call__
846
+ await self.middleware_stack(scope, receive, send)
847
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 736, in app
848
+ await route.handle(scope, receive, send)
849
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 290, in handle
850
+ await self.app(scope, receive, send)
851
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/routing.py", line 118, in app
852
+ await wrap_app_handling_exceptions(app, request)(scope, receive, send)
853
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
854
+ raise exc
855
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
856
+ await app(scope, receive, sender)
857
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/routing.py", line 105, in app
858
+ await response(scope, receive, send)
859
+ File "/opt/venv/lib/python3.12/site-packages/starlette/responses.py", line 167, in __call__
860
+ await self.background()
861
+ File "/opt/venv/lib/python3.12/site-packages/starlette/background.py", line 36, in __call__
862
+ await task()
863
+ File "/opt/venv/lib/python3.12/site-packages/starlette/background.py", line 23, in __call__
864
+ await run_in_threadpool(self.func, *self.args, **self.kwargs)
865
+ File "/opt/venv/lib/python3.12/site-packages/starlette/concurrency.py", line 32, in run_in_threadpool
866
+ return await anyio.to_thread.run_sync(func)
867
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
868
+ File "/opt/venv/lib/python3.12/site-packages/anyio/to_thread.py", line 63, in run_sync
869
+ return await get_async_backend().run_sync_in_worker_thread(
870
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
871
+ File "/opt/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 2502, in run_sync_in_worker_thread
872
+ return await future
873
+ ^^^^^^^^^^^^
874
+ File "/opt/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 986, in run
875
+ result = context.run(func, *args)
876
+ ^^^^^^^^^^^^^^^^^^^^^^^^
877
+ File "/home/user/app/src/api/app.py", line 527, in run_analysis_background
878
+ loop.run_until_complete(_run_with_lock())
879
+ File "/usr/local/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete
880
+ return future.result()
881
+ ^^^^^^^^^^^^^^^
882
+ File "/home/user/app/src/api/app.py", line 472, in _run_with_lock
883
+ async with workflow_lock:
884
+ ^^^^^^^^^^^^^
885
+ File "/usr/local/lib/python3.12/asyncio/locks.py", line 14, in __aenter__
886
+ await self.acquire()
887
+ File "/usr/local/lib/python3.12/asyncio/locks.py", line 105, in acquire
888
+ fut = self._get_loop().create_future()
889
+ ^^^^^^^^^^^^^^^^
890
+ File "/usr/local/lib/python3.12/asyncio/mixins.py", line 20, in _get_loop
891
+ raise RuntimeError(f'{self!r} is bound to a different event loop')
892
+ RuntimeError: <asyncio.locks.Lock object at 0x7fa993f3a990 [unlocked, waiters:1]> is bound to a different event loop
893
+ [SSE] ENDPOINT: Client connected for session_id=01c885ba-15c3-4d2b-9fc9-2c21b11554fc
894
+ [SSE] Queue registered, total subscribers: 1
895
+ INFO: 10.16.4.123:33989 - "GET /api/progress/stream/01c885ba-15c3-4d2b-9fc9-2c21b11554fc HTTP/1.1" 200 OK
896
+ [SSE] SENDING connection event to client
897
+ [SSE] No history to replay (fresh session)
898
+ [SSE] Starting event stream loop for session 01c885ba-15c3-4d2b-9fc9-2c21b11554fc
899
+ INFO: 10.16.34.155:11366 - "GET / HTTP/1.1" 200 OK
900
+ INFO:src.api.app:SSE stream cancelled for session 01c885ba-15c3-4d2b-9fc9-2c21b11554fc
901
+ INFO:src.api.app:SSE stream closed for session 01c885ba-15c3-4d2b-9fc9-2c21b11554fc
902
+ INFO: 10.16.34.155:11366 - "GET /index.css HTTP/1.1" 200 OK
903
+ INFO: 10.16.4.123:20197 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 304 Not Modified
904
+ INFO: 10.16.4.123:20197 - "GET /favicon.ico HTTP/1.1" 200 OK
905
+ [SSE] ENDPOINT: Client connected for session_id=01c885ba-15c3-4d2b-9fc9-2c21b11554fc
906
+ [SSE] Queue registered, total subscribers: 1
907
+ INFO: 10.16.34.155:40464 - "GET /api/progress/stream/01c885ba-15c3-4d2b-9fc9-2c21b11554fc HTTP/1.1" 200 OK
908
+ [SSE] SENDING connection event to client
909
+ [SSE] No history to replay (fresh session)
910
+ [SSE] Starting event stream loop for session 01c885ba-15c3-4d2b-9fc9-2c21b11554fc
911
+ INFO:src.api.app:[ASYNC] Reusing session: 01c885ba... (follow-up)
912
+ INFO:src.api.app:[ASYNC] File saved: swing_tips.csv
913
+ INFO: 10.16.34.155:7992 - "POST /run-async HTTP/1.1" 200 OK
914
+ ERROR:src.api.app:Unhandled exception: <asyncio.locks.Lock object at 0x7fa993f3a990 [unlocked, waiters:1]> is bound to a different event loop
915
+ Traceback (most recent call last):
916
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/errors.py", line 164, in __call__
917
+ await self.app(scope, receive, _send)
918
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/cors.py", line 93, in __call__
919
+ await self.simple_response(scope, receive, send, request_headers=headers)
920
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/cors.py", line 144, in simple_response
921
+ await self.app(scope, receive, send)
922
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py", line 63, in __call__
923
+ await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
924
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
925
+ raise exc
926
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
927
+ await app(scope, receive, sender)
928
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in __call__
929
+ await self.app(scope, receive, send)
930
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 716, in __call__
931
+ await self.middleware_stack(scope, receive, send)
932
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 736, in app
933
+ await route.handle(scope, receive, send)
934
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 290, in handle
935
+ await self.app(scope, receive, send)
936
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/routing.py", line 118, in app
937
+ await wrap_app_handling_exceptions(app, request)(scope, receive, send)
938
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
939
+ raise exc
940
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
941
+ await app(scope, receive, sender)
942
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/routing.py", line 105, in app
943
+ await response(scope, receive, send)
944
+ File "/opt/venv/lib/python3.12/site-packages/starlette/responses.py", line 167, in __call__
945
+ await self.background()
946
+ File "/opt/venv/lib/python3.12/site-packages/starlette/background.py", line 36, in __call__
947
+ await task()
948
+ File "/opt/venv/lib/python3.12/site-packages/starlette/background.py", line 23, in __call__
949
+ await run_in_threadpool(self.func, *self.args, **self.kwargs)
950
+ File "/opt/venv/lib/python3.12/site-packages/starlette/concurrency.py", line 32, in run_in_threadpool
951
+ return await anyio.to_thread.run_sync(func)
952
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
953
+ File "/opt/venv/lib/python3.12/site-packages/anyio/to_thread.py", line 63, in run_sync
954
+ return await get_async_backend().run_sync_in_worker_thread(
955
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
956
+ File "/opt/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 2502, in run_sync_in_worker_thread
957
+ return await future
958
+ ^^^^^^^^^^^^
959
+ File "/opt/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 986, in run
960
+ result = context.run(func, *args)
961
+ ^^^^^^^^^^^^^^^^^^^^^^^^
962
+ File "/home/user/app/src/api/app.py", line 527, in run_analysis_background
963
+ loop.run_until_complete(_run_with_lock())
964
+ File "/usr/local/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete
965
+ return future.result()
966
+ ^^^^^^^^^^^^^^^
967
+ File "/home/user/app/src/api/app.py", line 472, in _run_with_lock
968
+ async with workflow_lock:
969
+ ^^^^^^^^^^^^^
970
+ File "/usr/local/lib/python3.12/asyncio/locks.py", line 14, in __aenter__
971
+ await self.acquire()
972
+ File "/usr/local/lib/python3.12/asyncio/locks.py", line 105, in acquire
973
+ fut = self._get_loop().create_future()
974
+ ^^^^^^^^^^^^^^^^
975
+ File "/usr/local/lib/python3.12/asyncio/mixins.py", line 20, in _get_loop
976
+ raise RuntimeError(f'{self!r} is bound to a different event loop')
977
+ RuntimeError: <asyncio.locks.Lock object at 0x7fa993f3a990 [unlocked, waiters:1]> is bound to a different event loop
978
+ ERROR: Exception in ASGI application
979
+ Traceback (most recent call last):
980
+ File "/opt/venv/lib/python3.12/site-packages/uvicorn/protocols/http/httptools_impl.py", line 416, in run_asgi
981
+ result = await app( # type: ignore[func-returns-value]
982
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
983
+ File "/opt/venv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py", line 60, in __call__
984
+ return await self.app(scope, receive, send)
985
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
986
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/applications.py", line 1135, in __call__
987
+ await super().__call__(scope, receive, send)
988
+ File "/opt/venv/lib/python3.12/site-packages/starlette/applications.py", line 107, in __call__
989
+ await self.middleware_stack(scope, receive, send)
990
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/errors.py", line 186, in __call__
991
+ raise exc
992
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/errors.py", line 164, in __call__
993
+ await self.app(scope, receive, _send)
994
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/cors.py", line 93, in __call__
995
+ await self.simple_response(scope, receive, send, request_headers=headers)
996
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/cors.py", line 144, in simple_response
997
+ await self.app(scope, receive, send)
998
+ File "/opt/venv/lib/python3.12/site-packages/starlette/middleware/exceptions.py", line 63, in __call__
999
+ await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
1000
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
1001
+ raise exc
1002
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
1003
+ await app(scope, receive, sender)
1004
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in __call__
1005
+ await self.app(scope, receive, send)
1006
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 716, in __call__
1007
+ await self.middleware_stack(scope, receive, send)
1008
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 736, in app
1009
+ await route.handle(scope, receive, send)
1010
+ File "/opt/venv/lib/python3.12/site-packages/starlette/routing.py", line 290, in handle
1011
+ await self.app(scope, receive, send)
1012
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/routing.py", line 118, in app
1013
+ await wrap_app_handling_exceptions(app, request)(scope, receive, send)
1014
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
1015
+ raise exc
1016
+ File "/opt/venv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 42, in wrapped_app
1017
+ await app(scope, receive, sender)
1018
+ File "/opt/venv/lib/python3.12/site-packages/fastapi/routing.py", line 105, in app
1019
+ await response(scope, receive, send)
1020
+ File "/opt/venv/lib/python3.12/site-packages/starlette/responses.py", line 167, in __call__
1021
+ await self.background()
1022
+ File "/opt/venv/lib/python3.12/site-packages/starlette/background.py", line 36, in __call__
1023
+ await task()
1024
+ File "/opt/venv/lib/python3.12/site-packages/starlette/background.py", line 23, in __call__
1025
+ await run_in_threadpool(self.func, *self.args, **self.kwargs)
1026
+ File "/opt/venv/lib/python3.12/site-packages/starlette/concurrency.py", line 32, in run_in_threadpool
1027
+ return await anyio.to_thread.run_sync(func)
1028
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
1029
+ File "/opt/venv/lib/python3.12/site-packages/anyio/to_thread.py", line 63, in run_sync
1030
+ return await get_async_backend().run_sync_in_worker_thread(
1031
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
1032
+ File "/opt/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 2502, in run_sync_in_worker_thread
1033
+ return await future
1034
+ ^^^^^^^^^^^^
1035
+ File "/opt/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 986, in run
1036
+ result = context.run(func, *args)
1037
+ ^^^^^^^^^^^^^^^^^^^^^^^^
1038
+ File "/home/user/app/src/api/app.py", line 527, in run_analysis_background
1039
+ loop.run_until_complete(_run_with_lock())
1040
+ File "/usr/local/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete
1041
+ return future.result()
1042
+ ^^^^^^^^^^^^^^^
1043
+ File "/home/user/app/src/api/app.py", line 472, in _run_with_lock
1044
+ async with workflow_lock:
1045
+ ^^^^^^^^^^^^^
1046
+ File "/usr/local/lib/python3.12/asyncio/locks.py", line 14, in __aenter__
1047
+ await self.acquire()
1048
+ File "/usr/local/lib/python3.12/asyncio/locks.py", line 105, in acquire
1049
+ fut = self._get_loop().create_future()
1050
+ ^^^^^^^^^^^^^^^^
1051
+ File "/usr/local/lib/python3.12/asyncio/mixins.py", line 20, in _get_loop
1052
+ raise RuntimeError(f'{self!r} is bound to a different event loop')
1053
+ RuntimeError: <asyncio.locks.Lock object at 0x7fa993f3a990 [unlocked, waiters:1]> is bound to a different event loop
1054
+ INFO:src.api.app:SSE stream cancelled for session 01c885ba-15c3-4d2b-9fc9-2c21b11554fc
1055
+ INFO:src.api.app:SSE stream closed for session 01c885ba-15c3-4d2b-9fc9-2c21b11554fc
1056
+ [SSE] ENDPOINT: Client connected for session_id=01c885ba-15c3-4d2b-9fc9-2c21b11554fc
1057
+ [SSE] Queue registered, total subscribers: 1
1058
+ INFO: 10.16.4.123:25199 - "GET /api/progress/stream/01c885ba-15c3-4d2b-9fc9-2c21b11554fc HTTP/1.1" 200 OK
1059
+ [SSE] SENDING connection event to client
1060
+ [SSE] No history to replay (fresh session)
1061
+ [SSE] Starting event stream loop for session 01c885ba-15c3-4d2b-9fc9-2c21b11554fc
Server Logs.txt ADDED
@@ -0,0 +1,716 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ===== Application Startup at 2026-02-19 18:09:03 =====
2
+
3
+ INFO:src.api.app:👥 Multi-user session isolation initialized (fast mode)
4
+ INFO:src.api.app:🔒 Workflow lock initialized for request queuing
5
+ INFO:src.api.app:✅ Frontend assets mounted from /home/user/app/FRRONTEEEND/dist
6
+ INFO: Started server process [1]
7
+ INFO: Waiting for application startup.
8
+ INFO:src.api.app:Initializing legacy global agent for health checks...
9
+ 🤖 Initialized with Mistral provider - Model: mistral-large-latest
10
+ ✅ Cache database initialized at /home/user/app/cache_db/cache.db
11
+ 🧠 Loading SBERT model: all-MiniLM-L6-v2...
12
+ INFO:sentence_transformers.SentenceTransformer:Use pytorch device_name: cpu
13
+ INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2
14
+ ✅ SBERT loaded on CPU
15
+ 📊 Token Budget: 120,000 tokens available (128,000 - 8,000 reserve)
16
+ ✅ Sessions database initialized at ./cache_db/sessions.db
17
+ ✅ Created new session: 5081830f-32ee-4f0a-81c5-cf0fc6906e43
18
+ 📁 Output directory: /tmp/data_science_agent/outputs
19
+ INFO:src.api.app:✅ Health check agent initialized with provider: mistral
20
+ INFO:src.api.app:👥 Per-session agents enabled - each user gets isolated instance
21
+ INFO:src.api.app:🤖 Multi-agent architecture enabled with 5 specialists
22
+ INFO: Application startup complete.
23
+ INFO: Uvicorn running on http://0.0.0.0:7860 (Press CTRL+C to quit)
24
+ INFO: 10.16.34.155:32798 - "GET / HTTP/1.1" 200 OK
25
+ INFO: 10.16.4.123:43387 - "GET /index.css HTTP/1.1" 200 OK
26
+ INFO: 10.16.34.155:32798 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 200 OK
27
+ INFO: 10.16.4.123:54623 - "GET / HTTP/1.1" 200 OK
28
+ INFO: 10.16.4.123:54623 - "GET /index.css HTTP/1.1" 200 OK
29
+ INFO: 10.16.34.155:7477 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 200 OK
30
+ INFO: 10.16.34.155:53091 - "GET /favicon.ico HTTP/1.1" 200 OK
31
+ [SSE] ENDPOINT: Client connected for session_id=d41f7d5d-57f2-4b66-8137-b91496eda810
32
+ [SSE] Queue registered, total subscribers: 1
33
+ INFO: 10.16.4.123:30718 - "GET /api/progress/stream/d41f7d5d-57f2-4b66-8137-b91496eda810 HTTP/1.1" 200 OK
34
+ [SSE] SENDING connection event to client
35
+ [SSE] No history to replay (fresh session)
36
+ [SSE] Starting event stream loop for session d41f7d5d-57f2-4b66-8137-b91496eda810
37
+ INFO:src.api.app:SSE stream cancelled for session d41f7d5d-57f2-4b66-8137-b91496eda810
38
+ INFO:src.api.app:SSE stream closed for session d41f7d5d-57f2-4b66-8137-b91496eda810
39
+ INFO:src.api.app:[ASYNC] Created new session: 2df5d6d1...
40
+ INFO:src.api.app:[ASYNC] File saved: uber_trips_dataset_50k.csv
41
+ INFO: 10.16.4.123:29159 - "POST /run-async HTTP/1.1" 200 OK
42
+ INFO:src.api.app:[BACKGROUND] Starting analysis for session 2df5d6d1...
43
+ [🧹] Clearing SSE history for 2df5d6d1...
44
+ INFO:src.api.app:[🆕] Creating lightweight session for 2df5d6d1...
45
+ INFO:src.api.app:✅ Session created for 2df5d6d1 (cache: 1/50) - <1s init
46
+ [DEBUG] Session fallback triggered - has_ambiguous_ref=True, resolved={}
47
+ [DEBUG] resolve_ambiguity returning: {}
48
+ [DEBUG] Orchestrator received resolved_params: {}
49
+ [DEBUG] Current file_path: '/tmp/data_science_agent/uber_trips_dataset_50k.csv', target_col: 'None'
50
+ 📝 User provided new file: /tmp/data_science_agent/uber_trips_dataset_50k.csv (ignoring session file: none)
51
+ 🔍 Extracting dataset schema locally (no LLM)...
52
+ [SSE] ENDPOINT: Client connected for session_id=2df5d6d1-f23c-48fb-b353-373e7693b75e
53
+ [SSE] Queue registered, total subscribers: 1
54
+ INFO: 10.16.34.155:32511 - "GET /api/progress/stream/2df5d6d1-f23c-48fb-b353-373e7693b75e HTTP/1.1" 200 OK
55
+ [SSE] SENDING connection event to client
56
+ [SSE] No history to replay (fresh session)
57
+ [SSE] Starting event stream loop for session 2df5d6d1-f23c-48fb-b353-373e7693b75e
58
+ 🧠 Semantic layer: Embedded 14 columns
59
+ Found 10 similar column pairs (potential duplicates)
60
+ 🧠 Semantic layer enriched 14 columns
61
+ ✅ Schema extracted: 50000 rows × 14 cols
62
+ File size: 8.46 MB
63
+
64
+ 🎯 Intent Classification:
65
+ Mode: EXPLORATORY
66
+ Confidence: 40%
67
+ Reasoning: No strong pattern match, defaulting to exploratory analysis
68
+ Sub-intent: default
69
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=intent_classified, msg=
70
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 1
71
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
72
+ [SSE] Successfully queued event to subscriber 1
73
+
74
+ 🧠 Routing to REASONING LOOP (exploratory mode)
75
+ [SSE] GOT event from queue: intent_classified
76
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_mode, msg=🧠 Reasoning Loop activated (exploratory mode)
77
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 2
78
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
79
+ [SSE] Successfully queued event to subscriber 1
80
+
81
+ ============================================================
82
+ 🧠 REASONING LOOP (EXPLORATORY mode)
83
+ Question: Explain the important features in this dataset
84
+ Max iterations: 8
85
+ ============================================================
86
+
87
+ 🔬 Generating hypotheses from data profile...
88
+ 📋 Final parameters: ['file_path']
89
+ [SSE] GOT event from queue: reasoning_mode
90
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
91
+ Generated 5 hypotheses:
92
+ 1. [0.9] The 'fare_amount' distribution exhibits unexpected spikes at round numbers (e.g., $10, $20, $50), suggesting potential fare manipulation, rounding errors, or psychological pricing effects by drivers. This could indicate data quality issues or behavioral patterns worth investigating.
93
+ 2. [0.9] 'distance_km' and 'fare_amount' show a non-linear relationship, with fares increasing disproportionately for very short or very long trips. This may reveal surge pricing effects, city-specific pricing models, or data entry errors in distance calculations.
94
+ 3. [0.8] Certain 'driver_id' or 'rider_id' values appear in an implausibly high number of trips (e.g., >100 trips/day), suggesting potential data duplication, bot activity, or fraudulent ride patterns that could skew demand/supply metrics.
95
+ 4. [0.8] The 'status' column contains a small percentage of 'cancelled' or 'failed' trips, but these are disproportionately concentrated in specific cities or time windows, indicating potential regional service issues or rider/driver behavior differences.
96
+ 5. [0.8] Geospatial anomalies exist where 'pickup_lat'/'pickup_lng' or 'drop_lat'/'drop_lng' fall outside expected city boundaries, suggesting GPS errors, incorrect city assignments, or fraudulent trip reporting.
97
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=hypotheses_generated, msg=
98
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 3
99
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
100
+ [SSE] Successfully queued event to subscriber 1
101
+
102
+ ── Iteration 1/8 ──
103
+ 🤔 REASON: Deciding next action...
104
+ [SSE] GOT event from queue: hypotheses_generated
105
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
106
+ Status: investigating
107
+ Reasoning: To explain the important features in the dataset, we first need a comprehensive understanding of the data, including distributions, correlations, and potential relationships between features. This will help identify which features are most relevant or influential.
108
+ Tool: generate_ydata_profiling_report
109
+ Hypothesis: The profiling report will reveal key statistics, distributions, correlations, and potential data quality issues, which will help identify the most important features in the dataset.
110
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
111
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 4
112
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
113
+ [SSE] Successfully queued event to subscriber 1
114
+ ⚡ ACT: Executing generate_ydata_profiling_report...
115
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: generate_ydata_profiling_report
116
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 5
117
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
118
+ [SSE] Successfully queued event to subscriber 1
119
+ 📋 Final parameters: ['file_path', 'output_path', 'minimal', 'title']
120
+ [SSE] GOT event from queue: reasoning_step
121
+ [SSE] GOT event from queue: tool_executing
122
+ INFO:visions.backends:Pandas backend loaded 2.3.3
123
+ INFO:visions.backends:Numpy backend loaded 2.1.3
124
+ INFO:visions.backends:Pyspark backend NOT loaded
125
+ INFO:visions.backends:Python backend loaded
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+ Upgrade to ydata-sdk
127
+ Improve your data and profiling with ydata-sdk, featuring data quality scoring, redundancy detection, outlier identification, text validation, and synthetic data generation.
128
+ Register at https://ydata.ai/register
129
+ Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: trip_id]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: driver_id]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: rider_id]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: pickup_lat]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: pickup_lat]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: pickup_lng]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: drop_lat]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: drop_lng]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: distance_km]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: fare_amount]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: payment_method]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: pickup_time]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: drop_time]
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+ Summarize dataset: 0%| | 0/19 [00:00<?, ?it/s, Describe variable: drop_time]
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+ 0%| | 0/14 [00:00<?, ?it/s]
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+ Summarize dataset: 5%|▌ | 1/19 [00:02<00:42, 2.34s/it, Describe variable: drop_time]
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+ Summarize dataset: 16%|█▌ | 3/19 [00:02<00:11, 1.45it/s, Describe variable: drop_time]
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+ Summarize dataset: 32%|███▏ | 6/19 [00:02<00:03, 3.48it/s, Describe variable: drop_time]
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+ 7%|▋ | 1/14 [00:02<00:31, 2.42s/it]
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+ Summarize dataset: 53%|█████▎ | 10/19 [00:02<00:01, 6.52it/s, Describe variable: drop_time]
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+ 43%|████▎ | 6/14 [00:02<00:02, 3.11it/s]
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+ Summarize dataset: 68%|██████▊ | 13/19 [00:03<00:00, 6.39it/s, Describe variable: drop_time]
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+ Summarize dataset: 74%|███████▎ | 14/19 [00:03<00:00, 7.05it/s, Describe variable: drop_time]
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+ 93%|█████████▎| 13/14 [00:02<00:00, 6.71it/s]
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+ 100%|██████████| 14/14 [00:02<00:00, 4.80it/s]
155
+ INFO: 10.16.34.155:49568 - "GET / HTTP/1.1" 200 OK
156
+ INFO: 10.16.34.155:49568 - "GET /index.css HTTP/1.1" 200 OK
157
+ INFO: 10.16.4.123:38750 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 200 OK
158
+ INFO: 10.16.34.155:49568 - "GET /favicon.ico HTTP/1.1" 200 OK
159
+ [SSE] ENDPOINT: Client connected for session_id=1963e777-4930-4200-9ada-ad742e5de6bc
160
+ [SSE] Queue registered, total subscribers: 1
161
+ INFO: 10.16.4.123:1392 - "GET /api/progress/stream/1963e777-4930-4200-9ada-ad742e5de6bc HTTP/1.1" 200 OK
162
+ [SSE] SENDING connection event to client
163
+ [SSE] No history to replay (fresh session)
164
+ [SSE] Starting event stream loop for session 1963e777-4930-4200-9ada-ad742e5de6bc
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+ Summarize dataset: 74%|███████▎ | 14/19 [00:03<00:00, 7.05it/s, Get variable types]
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+ Summarize dataset: 75%|███████▌ | 15/20 [00:03<00:00, 7.05it/s, Get dataframe statistics]
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+ Summarize dataset: 80%|████████ | 16/20 [00:03<00:00, 8.07it/s, Get dataframe statistics]
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+ Summarize dataset: 64%|██████▍ | 16/25 [00:03<00:01, 8.07it/s, Calculate pearson correlation]
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+ Summarize dataset: 68%|██████▊ | 17/25 [00:03<00:00, 8.07it/s, Calculate spearman correlation]
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+ Summarize dataset: 72%|███████▏ | 18/25 [00:03<00:00, 8.67it/s, Calculate spearman correlation]
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+ Summarize dataset: 72%|███████▏ | 18/25 [00:03<00:00, 8.67it/s, Calculate phi_k correlation]
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+ Summarize dataset: 76%|███████▌ | 19/25 [00:11<00:00, 8.67it/s, Calculate cramers correlation]
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+ Summarize dataset: 80%|████████ | 20/25 [00:11<00:05, 1.06s/it, Calculate cramers correlation]
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+ Summarize dataset: 80%|████████ | 20/25 [00:11<00:05, 1.06s/it, Calculate auto correlation]
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+ Summarize dataset: 84%|████████▍ | 21/25 [00:11<00:04, 1.02s/it, Calculate auto correlation]
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+ Summarize dataset: 84%|████████▍ | 21/25 [00:11<00:04, 1.02s/it, Get scatter matrix]
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+ Summarize dataset: 20%|█▉ | 21/106 [00:11<01:26, 1.02s/it, scatter trip_id, trip_id]
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+ Summarize dataset: 21%|██ | 22/106 [00:13<01:27, 1.04s/it, scatter trip_id, trip_id]
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+ Summarize dataset: 21%|██ | 22/106 [00:13<01:27, 1.04s/it, scatter driver_id, trip_id]
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+ Summarize dataset: 22%|██▏ | 23/106 [00:13<01:11, 1.16it/s, scatter driver_id, trip_id]
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+ Summarize dataset: 22%|██▏ | 23/106 [00:13<01:11, 1.16it/s, scatter rider_id, trip_id]
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+ Summarize dataset: 23%|██▎ | 24/106 [00:13<00:57, 1.43it/s, scatter rider_id, trip_id]
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+ Summarize dataset: 23%|██▎ | 24/106 [00:13<00:57, 1.43it/s, scatter pickup_lat, trip_id]
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+ Summarize dataset: 24%|██▎ | 25/106 [00:13<00:46, 1.72it/s, scatter pickup_lat, trip_id]
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+ Summarize dataset: 24%|██▎ | 25/106 [00:13<00:46, 1.72it/s, scatter pickup_lng, trip_id]
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+ Summarize dataset: 25%|██▍ | 26/106 [00:13<00:37, 2.14it/s, scatter pickup_lng, trip_id]
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+ Summarize dataset: 25%|██▍ | 26/106 [00:13<00:37, 2.14it/s, scatter drop_lat, trip_id]
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+ Summarize dataset: 25%|██▌ | 27/106 [00:13<00:29, 2.69it/s, scatter drop_lat, trip_id]
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+ Summarize dataset: 25%|██▌ | 27/106 [00:13<00:29, 2.69it/s, scatter drop_lng, trip_id]
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+ Summarize dataset: 26%|██▋ | 28/106 [00:13<00:23, 3.31it/s, scatter drop_lng, trip_id]
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+ Summarize dataset: 26%|██▋ | 28/106 [00:13<00:23, 3.31it/s, scatter distance_km, trip_id]
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+ Summarize dataset: 27%|██▋ | 29/106 [00:14<00:19, 3.91it/s, scatter distance_km, trip_id]
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+ Summarize dataset: 27%|██▋ | 29/106 [00:14<00:19, 3.91it/s, scatter fare_amount, trip_id]
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+ Summarize dataset: 28%|██▊ | 30/106 [00:14<00:16, 4.55it/s, scatter fare_amount, trip_id]
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+ Summarize dataset: 28%|██▊ | 30/106 [00:14<00:16, 4.55it/s, scatter trip_id, driver_id]
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+ Summarize dataset: 29%|██▉ | 31/106 [00:14<00:14, 5.14it/s, scatter trip_id, driver_id]
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+ Summarize dataset: 29%|██▉ | 31/106 [00:14<00:14, 5.14it/s, scatter driver_id, driver_id]
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+ Summarize dataset: 30%|███ | 32/106 [00:14<00:12, 5.75it/s, scatter driver_id, driver_id]
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+ Summarize dataset: 30%|███ | 32/106 [00:14<00:12, 5.75it/s, scatter rider_id, driver_id]
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+ Summarize dataset: 31%|███ | 33/106 [00:14<00:11, 6.33it/s, scatter rider_id, driver_id]
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+ Summarize dataset: 31%|███ | 33/106 [00:14<00:11, 6.33it/s, scatter pickup_lat, driver_id]
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+ Summarize dataset: 32%|███▏ | 34/106 [00:14<00:10, 6.62it/s, scatter pickup_lat, driver_id]
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+ Summarize dataset: 32%|███▏ | 34/106 [00:14<00:10, 6.62it/s, scatter pickup_lng, driver_id]
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+ Summarize dataset: 33%|███▎ | 35/106 [00:14<00:10, 7.08it/s, scatter pickup_lng, driver_id]
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+ Summarize dataset: 33%|███▎ | 35/106 [00:14<00:10, 7.08it/s, scatter drop_lat, driver_id]
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+ Summarize dataset: 34%|███▍ | 36/106 [00:15<00:09, 7.37it/s, scatter drop_lat, driver_id]
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+ Summarize dataset: 34%|███▍ | 36/106 [00:15<00:09, 7.37it/s, scatter drop_lng, driver_id]
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+ Summarize dataset: 35%|███▍ | 37/106 [00:15<00:09, 7.28it/s, scatter drop_lng, driver_id]
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+ Summarize dataset: 35%|███▍ | 37/106 [00:15<00:09, 7.28it/s, scatter distance_km, driver_id]
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+ Summarize dataset: 36%|███▌ | 38/106 [00:15<00:09, 7.40it/s, scatter distance_km, driver_id]
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+ Summarize dataset: 36%|███▌ | 38/106 [00:15<00:09, 7.40it/s, scatter fare_amount, driver_id]
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+ Summarize dataset: 37%|███▋ | 39/106 [00:15<00:08, 7.57it/s, scatter fare_amount, driver_id]
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+ Summarize dataset: 37%|███▋ | 39/106 [00:15<00:08, 7.57it/s, scatter trip_id, rider_id]
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+ Summarize dataset: 38%|███▊ | 40/106 [00:15<00:08, 7.86it/s, scatter trip_id, rider_id]
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+ Summarize dataset: 38%|███▊ | 40/106 [00:15<00:08, 7.86it/s, scatter driver_id, rider_id]
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+ Summarize dataset: 39%|███▊ | 41/106 [00:15<00:08, 7.86it/s, scatter rider_id, rider_id]
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+ Summarize dataset: 40%|███▉ | 42/106 [00:15<00:07, 8.75it/s, scatter rider_id, rider_id]
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+ Summarize dataset: 40%|███▉ | 42/106 [00:15<00:07, 8.75it/s, scatter pickup_lat, rider_id]
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+ Summarize dataset: 41%|████ | 43/106 [00:15<00:07, 8.57it/s, scatter pickup_lat, rider_id]
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+ Summarize dataset: 41%|████ | 43/106 [00:15<00:07, 8.57it/s, scatter pickup_lng, rider_id]
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+ Summarize dataset: 42%|████▏ | 44/106 [00:15<00:07, 8.03it/s, scatter pickup_lng, rider_id]
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+ Summarize dataset: 42%|████▏ | 44/106 [00:15<00:07, 8.03it/s, scatter drop_lat, rider_id]
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+ Summarize dataset: 42%|████▏ | 45/106 [00:16<00:07, 8.40it/s, scatter drop_lat, rider_id]
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+ Summarize dataset: 42%|████▏ | 45/106 [00:16<00:07, 8.40it/s, scatter drop_lng, rider_id]
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+ Summarize dataset: 43%|████▎ | 46/106 [00:16<00:07, 8.40it/s, scatter drop_lng, rider_id]
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+ Summarize dataset: 43%|████▎ | 46/106 [00:16<00:07, 8.40it/s, scatter distance_km, rider_id]
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+ Summarize dataset: 44%|████▍ | 47/106 [00:16<00:06, 8.54it/s, scatter distance_km, rider_id]
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+ Summarize dataset: 44%|████▍ | 47/106 [00:16<00:06, 8.54it/s, scatter fare_amount, rider_id]
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+ Summarize dataset: 45%|████▌ | 48/106 [00:16<00:06, 8.54it/s, scatter trip_id, pickup_lat]
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+ Summarize dataset: 46%|████▌ | 49/106 [00:44<06:08, 6.47s/it, scatter trip_id, pickup_lat]
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+ Summarize dataset: 46%|████▌ | 49/106 [00:44<06:08, 6.47s/it, scatter driver_id, pickup_lat]
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+ Summarize dataset: 47%|████▋ | 50/106 [00:45<04:39, 4.99s/it, scatter driver_id, pickup_lat]
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+ Summarize dataset: 47%|████▋ | 50/106 [00:45<04:39, 4.99s/it, scatter rider_id, pickup_lat]
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+ Summarize dataset: 48%|████▊ | 51/106 [00:45<03:25, 3.74s/it, scatter rider_id, pickup_lat]
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+ Summarize dataset: 48%|████▊ | 51/106 [00:45<03:25, 3.74s/it, scatter pickup_lat, pickup_lat]
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+ Summarize dataset: 49%|████▉ | 52/106 [00:45<02:30, 2.79s/it, scatter pickup_lat, pickup_lat]
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+ Summarize dataset: 49%|████▉ | 52/106 [00:45<02:30, 2.79s/it, scatter pickup_lng, pickup_lat]
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+ Summarize dataset: 50%|█████ | 53/106 [00:45<01:48, 2.06s/it, scatter pickup_lng, pickup_lat]
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+ Summarize dataset: 50%|█████ | 53/106 [00:45<01:48, 2.06s/it, scatter drop_lat, pickup_lat]
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+ Summarize dataset: 51%|█████ | 54/106 [00:45<01:19, 1.53s/it, scatter drop_lat, pickup_lat]
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+ Summarize dataset: 51%|█████ | 54/106 [00:45<01:19, 1.53s/it, scatter drop_lng, pickup_lat]
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+ Summarize dataset: 52%|█████▏ | 55/106 [00:46<00:57, 1.12s/it, scatter drop_lng, pickup_lat]
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+ Summarize dataset: 52%|█████▏ | 55/106 [00:46<00:57, 1.12s/it, scatter distance_km, pickup_lat]
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+ Summarize dataset: 53%|█████▎ | 56/106 [00:46<00:41, 1.20it/s, scatter distance_km, pickup_lat]
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+ Summarize dataset: 53%|█████▎ | 56/106 [00:46<00:41, 1.20it/s, scatter fare_amount, pickup_lat]
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+ Summarize dataset: 54%|█████▍ | 57/106 [00:46<00:30, 1.60it/s, scatter fare_amount, pickup_lat]
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+ Summarize dataset: 54%|█████▍ | 57/106 [00:46<00:30, 1.60it/s, scatter trip_id, pickup_lng]
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+ Summarize dataset: 55%|█████▍ | 58/106 [00:46<00:22, 2.09it/s, scatter trip_id, pickup_lng]
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+ Summarize dataset: 55%|█████▍ | 58/106 [00:46<00:22, 2.09it/s, scatter driver_id, pickup_lng]
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+ Summarize dataset: 56%|█████▌ | 59/106 [00:46<00:17, 2.73it/s, scatter driver_id, pickup_lng]
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+ Summarize dataset: 56%|█████▌ | 59/106 [00:46<00:17, 2.73it/s, scatter rider_id, pickup_lng]
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+ Summarize dataset: 57%|█████▋ | 60/106 [00:46<00:13, 3.46it/s, scatter rider_id, pickup_lng]
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+ Summarize dataset: 57%|█████▋ | 60/106 [00:46<00:13, 3.46it/s, scatter pickup_lat, pickup_lng]
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+ Summarize dataset: 58%|█████▊ | 61/106 [00:46<00:10, 4.17it/s, scatter pickup_lat, pickup_lng]
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+ Summarize dataset: 58%|█████▊ | 61/106 [00:46<00:10, 4.17it/s, scatter pickup_lng, pickup_lng]
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+ Summarize dataset: 58%|█████▊ | 62/106 [00:46<00:08, 4.97it/s, scatter pickup_lng, pickup_lng]
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+ Summarize dataset: 58%|█████▊ | 62/106 [00:46<00:08, 4.97it/s, scatter drop_lat, pickup_lng]
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+ Summarize dataset: 59%|█████▉ | 63/106 [00:46<00:08, 4.97it/s, scatter drop_lng, pickup_lng]
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+ Summarize dataset: 60%|██████ | 64/106 [00:47<00:06, 6.46it/s, scatter drop_lng, pickup_lng]
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+ Summarize dataset: 60%|██████ | 64/106 [00:47<00:06, 6.46it/s, scatter distance_km, pickup_lng]
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+ Summarize dataset: 61%|██████▏ | 65/106 [00:47<00:05, 6.95it/s, scatter distance_km, pickup_lng]
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+ Summarize dataset: 61%|██████▏ | 65/106 [00:47<00:05, 6.95it/s, scatter fare_amount, pickup_lng]
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+ Summarize dataset: 62%|██████▏ | 66/106 [00:47<00:05, 7.55it/s, scatter fare_amount, pickup_lng]
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+ Summarize dataset: 62%|██████▏ | 66/106 [00:47<00:05, 7.55it/s, scatter trip_id, drop_lat]
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+ Summarize dataset: 63%|██████▎ | 67/106 [00:47<00:04, 7.85it/s, scatter trip_id, drop_lat]
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+ Summarize dataset: 63%|██████▎ | 67/106 [00:47<00:04, 7.85it/s, scatter driver_id, drop_lat]
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+ Summarize dataset: 64%|██████▍ | 68/106 [00:47<00:04, 7.85it/s, scatter rider_id, drop_lat]
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+ Summarize dataset: 65%|██████▌ | 69/106 [00:47<00:04, 8.34it/s, scatter rider_id, drop_lat]
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+ Summarize dataset: 65%|██████▌ | 69/106 [00:47<00:04, 8.34it/s, scatter pickup_lat, drop_lat]
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+ Summarize dataset: 66%|██████▌ | 70/106 [00:47<00:04, 8.54it/s, scatter pickup_lat, drop_lat]
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+ Summarize dataset: 66%|██████▌ | 70/106 [00:47<00:04, 8.54it/s, scatter pickup_lng, drop_lat]
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+ Summarize dataset: 67%|██████▋ | 71/106 [00:47<00:04, 8.08it/s, scatter pickup_lng, drop_lat]
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+ Summarize dataset: 67%|██████▋ | 71/106 [00:47<00:04, 8.08it/s, scatter drop_lat, drop_lat]
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+ Summarize dataset: 68%|██████▊ | 72/106 [00:47<00:04, 8.09it/s, scatter drop_lat, drop_lat]
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+ Summarize dataset: 68%|██████▊ | 72/106 [00:47<00:04, 8.09it/s, scatter drop_lng, drop_lat]
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+ Summarize dataset: 69%|██████▉ | 73/106 [00:48<00:03, 8.43it/s, scatter drop_lng, drop_lat]
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+ Summarize dataset: 69%|██████▉ | 73/106 [00:48<00:03, 8.43it/s, scatter distance_km, drop_lat]
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+ Summarize dataset: 70%|██████▉ | 74/106 [00:48<00:04, 7.70it/s, scatter distance_km, drop_lat]
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+ Summarize dataset: 70%|██████▉ | 74/106 [00:48<00:04, 7.70it/s, scatter fare_amount, drop_lat]
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+ Summarize dataset: 71%|███████ | 75/106 [00:48<00:03, 8.05it/s, scatter fare_amount, drop_lat]
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+ Summarize dataset: 71%|███████ | 75/106 [00:48<00:03, 8.05it/s, scatter trip_id, drop_lng]
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+ Summarize dataset: 72%|███████▏ | 76/106 [00:48<00:03, 8.24it/s, scatter trip_id, drop_lng]
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+ Summarize dataset: 72%|███████▏ | 76/106 [00:48<00:03, 8.24it/s, scatter driver_id, drop_lng]
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+ Summarize dataset: 73%|███████▎ | 77/106 [00:48<00:03, 8.24it/s, scatter rider_id, drop_lng]
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+ Summarize dataset: 74%|███████▎ | 78/106 [00:48<00:03, 8.81it/s, scatter rider_id, drop_lng]
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+ Summarize dataset: 74%|███████▎ | 78/106 [00:48<00:03, 8.81it/s, scatter pickup_lat, drop_lng]
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+ Summarize dataset: 75%|███████▍ | 79/106 [00:48<00:03, 8.96it/s, scatter pickup_lat, drop_lng]
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+ Summarize dataset: 75%|███████▍ | 79/106 [00:48<00:03, 8.96it/s, scatter pickup_lng, drop_lng]
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+ Summarize dataset: 75%|███████▌ | 80/106 [00:48<00:02, 8.96it/s, scatter drop_lat, drop_lng]
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+ Summarize dataset: 76%|███████▋ | 81/106 [00:48<00:02, 9.44it/s, scatter drop_lat, drop_lng]
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+ Summarize dataset: 76%|███████▋ | 81/106 [00:48<00:02, 9.44it/s, scatter drop_lng, drop_lng]
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+ Summarize dataset: 77%|███████▋ | 82/106 [00:49<00:02, 9.44it/s, scatter distance_km, drop_lng]
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+ Summarize dataset: 78%|███████▊ | 83/106 [00:49<00:02, 8.99it/s, scatter distance_km, drop_lng]
294
+ Summarize dataset: 78%|███████▊ | 83/106 [00:49<00:02, 8.99it/s, scatter fare_amount, drop_lng]
295
+ Summarize dataset: 79%|███████▉ | 84/106 [00:49<00:02, 8.99it/s, scatter trip_id, distance_km]
296
+ Summarize dataset: 80%|████████ | 85/106 [00:49<00:02, 8.51it/s, scatter trip_id, distance_km]
297
+ Summarize dataset: 80%|████████ | 85/106 [00:49<00:02, 8.51it/s, scatter driver_id, distance_km]
298
+ Summarize dataset: 81%|████████ | 86/106 [00:49<00:02, 8.51it/s, scatter driver_id, distance_km]
299
+ Summarize dataset: 81%|████████ | 86/106 [00:49<00:02, 8.51it/s, scatter rider_id, distance_km]
300
+ Summarize dataset: 82%|████████▏ | 87/106 [00:49<00:02, 8.20it/s, scatter rider_id, distance_km]
301
+ Summarize dataset: 82%|████████▏ | 87/106 [00:49<00:02, 8.20it/s, scatter pickup_lat, distance_km]
302
+ Summarize dataset: 83%|████████▎ | 88/106 [00:49<00:02, 7.70it/s, scatter pickup_lat, distance_km]
303
+ Summarize dataset: 83%|████████▎ | 88/106 [00:49<00:02, 7.70it/s, scatter pickup_lng, distance_km]
304
+ Summarize dataset: 84%|████████▍ | 89/106 [00:50<00:02, 7.61it/s, scatter pickup_lng, distance_km]
305
+ Summarize dataset: 84%|████████▍ | 89/106 [00:50<00:02, 7.61it/s, scatter drop_lat, distance_km]
306
+ Summarize dataset: 85%|████████▍ | 90/106 [00:50<00:02, 7.95it/s, scatter drop_lat, distance_km]
307
+ Summarize dataset: 85%|████████▍ | 90/106 [00:50<00:02, 7.95it/s, scatter drop_lng, distance_km]
308
+ Summarize dataset: 86%|████████▌ | 91/106 [00:50<00:01, 7.81it/s, scatter drop_lng, distance_km]
309
+ Summarize dataset: 86%|████████▌ | 91/106 [00:50<00:01, 7.81it/s, scatter distance_km, distance_km]
310
+ Summarize dataset: 87%|████████▋ | 92/106 [00:50<00:01, 7.84it/s, scatter distance_km, distance_km]
311
+ Summarize dataset: 87%|████████▋ | 92/106 [00:50<00:01, 7.84it/s, scatter fare_amount, distance_km]
312
+ Summarize dataset: 88%|████████▊ | 93/106 [00:50<00:01, 7.86it/s, scatter fare_amount, distance_km]
313
+ Summarize dataset: 88%|████████▊ | 93/106 [00:50<00:01, 7.86it/s, scatter trip_id, fare_amount]
314
+ Summarize dataset: 89%|████████▊ | 94/106 [00:50<00:01, 8.37it/s, scatter trip_id, fare_amount]
315
+ Summarize dataset: 89%|████████▊ | 94/106 [00:50<00:01, 8.37it/s, scatter driver_id, fare_amount]
316
+ Summarize dataset: 90%|████████▉ | 95/106 [00:50<00:01, 8.60it/s, scatter driver_id, fare_amount]
317
+ Summarize dataset: 90%|████████▉ | 95/106 [00:50<00:01, 8.60it/s, scatter rider_id, fare_amount]
318
+ Summarize dataset: 91%|█████████ | 96/106 [00:50<00:01, 8.64it/s, scatter rider_id, fare_amount]
319
+ Summarize dataset: 91%|█████████ | 96/106 [00:50<00:01, 8.64it/s, scatter pickup_lat, fare_amount]
320
+ Summarize dataset: 92%|█████████▏| 97/106 [00:50<00:01, 8.70it/s, scatter pickup_lat, fare_amount]
321
+ Summarize dataset: 92%|█████████▏| 97/106 [00:50<00:01, 8.70it/s, scatter pickup_lng, fare_amount]
322
+ Summarize dataset: 92%|█████████▏| 98/106 [00:51<00:00, 8.89it/s, scatter pickup_lng, fare_amount]
323
+ Summarize dataset: 92%|█████████▏| 98/106 [00:51<00:00, 8.89it/s, scatter drop_lat, fare_amount]
324
+ Summarize dataset: 93%|█████████▎| 99/106 [00:51<00:00, 7.61it/s, scatter drop_lat, fare_amount]
325
+ Summarize dataset: 93%|█████████▎| 99/106 [00:51<00:00, 7.61it/s, scatter drop_lng, fare_amount]
326
+ Summarize dataset: 94%|█████████▍| 100/106 [00:51<00:00, 8.13it/s, scatter drop_lng, fare_amount]
327
+ Summarize dataset: 94%|█████████▍| 100/106 [00:51<00:00, 8.13it/s, scatter distance_km, fare_amount]
328
+ Summarize dataset: 95%|█████████▌| 101/106 [00:51<00:00, 8.37it/s, scatter distance_km, fare_amount]
329
+ Summarize dataset: 95%|█████████▌| 101/106 [00:51<00:00, 8.37it/s, scatter fare_amount, fare_amount]
330
+ Summarize dataset: 96%|█████████▌| 102/106 [00:51<00:00, 8.70it/s, scatter fare_amount, fare_amount]
331
+ Summarize dataset: 94%|█████████▍| 102/108 [00:51<00:00, 8.70it/s, Missing diagram bar]
332
+ Summarize dataset: 95%|█████████▌| 103/108 [00:52<00:01, 4.24it/s, Missing diagram bar]
333
+ Summarize dataset: 95%|█████████▌| 103/108 [00:52<00:01, 4.24it/s, Missing diagram matrix]
334
+ Summarize dataset: 96%|█████████▋| 104/108 [00:52<00:01, 3.10it/s, Missing diagram matrix]
335
+ Summarize dataset: 96%|█████████▋| 104/108 [00:52<00:01, 3.10it/s, Take sample]
336
+ Summarize dataset: 97%|█████████▋| 105/108 [00:52<00:00, 3.10it/s, Detecting duplicates]
337
+ Summarize dataset: 98%|█████████▊| 106/108 [00:52<00:00, 4.15it/s, Detecting duplicates]
338
+ Summarize dataset: 98%|█████████▊| 106/108 [00:52<00:00, 4.15it/s, Get alerts]
339
+ Summarize dataset: 99%|█████████▉| 107/108 [00:52<00:00, 4.15it/s, Get reproduction details]
340
+ Summarize dataset: 100%|██████████| 108/108 [00:52<00:00, 4.15it/s, Completed]
341
+ Summarize dataset: 100%|██████████| 108/108 [00:52<00:00, 2.04it/s, Completed]
342
+ Generate report structure: 0%| | 0/1 [00:00<?, ?it/s]INFO:src.api.app:SSE stream cancelled for session 1963e777-4930-4200-9ada-ad742e5de6bc
343
+ INFO:src.api.app:SSE stream closed for session 1963e777-4930-4200-9ada-ad742e5de6bc
344
+ INFO: 10.16.34.155:42907 - "GET / HTTP/1.1" 200 OK
345
+ INFO: 10.16.34.155:42907 - "GET /index.css HTTP/1.1" 200 OK
346
+ INFO: 10.16.34.155:42907 - "GET /favicon.ico HTTP/1.1" 200 OK
347
+ Generate report structure: 100%|██████████| 1/1 [00:48<00:00, 48.28s/it]
348
+ Generate report structure: 100%|██████████| 1/1 [00:48<00:00, 48.29s/it]
349
+ Render HTML: 0%| | 0/1 [00:00<?, ?it/s]
350
+ Render HTML: 100%|██████████| 1/1 [00:01<00:00, 1.94s/it]
351
+ Render HTML: 100%|██████████| 1/1 [00:01<00:00, 1.96s/it]
352
+ Export report to file: 0%| | 0/1 [00:00<?, ?it/s]
353
+ Export report to file: 100%|██████████| 1/1 [00:00<00:00, 51.99it/s]
354
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_completed, msg=✓ Completed: generate_ydata_profiling_report
355
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 6
356
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
357
+ [SSE] Successfully queued event to subscriber 1
358
+ ✓ Tool completed successfully
359
+ [SSE] GOT event from queue: tool_completed
360
+ 💾 Checkpoint saved: iteration 1, last tool: generate_ydata_profiling_report
361
+ 📊 EVALUATE: Interpreting results...
362
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
363
+ Interpretation: The result indicates that a comprehensive profiling report has been generated, which will detail the statistical properties, distributions, correlations, and missing values of features in the Uber trips dataset. This report is essential for identifying important features but does not itself interpret them—manual review is required to extract actionable insights.
364
+ Answered: False (confidence: 40%)
365
+ Should stop: False
366
+ Next questions: ['Which features show the strongest correlation with trip duration or fare amount?', 'Are there unexpected patterns or outliers in key features (e.g., pickup/dropoff times, locations)?', 'How do missing values or data quality issues impact feature importance?', 'What are the most surprising or counterintuitive relationships between features?']
367
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
368
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 7
369
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
370
+ [SSE] Successfully queued event to subscriber 1
371
+
372
+ ── Iteration 2/8 ──
373
+ 🤔 REASON: Deciding next action...
374
+ [SSE] GOT event from queue: finding_discovered
375
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
376
+ Status: investigating
377
+ Reasoning: The profiling report has been generated but not yet analyzed for feature importance. The next step is to analyze correlations between features, particularly focusing on 'fare_amount' as the target, to identify the most influential features driving fare pricing.
378
+ Tool: analyze_correlations
379
+ Hypothesis: We expect to identify strong correlations between 'fare_amount' and features like 'distance_km', 'city', and temporal features (e.g., 'pickup_time'), which will highlight the most important features in the dataset.
380
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
381
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 8
382
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
383
+ [SSE] Successfully queued event to subscriber 1
384
+ ⚡ ACT: Executing analyze_correlations...
385
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: analyze_correlations
386
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 9
387
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
388
+ [SSE] Successfully queued event to subscriber 1
389
+ 📋 Final parameters: ['file_path', 'target']
390
+ [SSE] GOT event from queue: reasoning_step
391
+ [SSE] GOT event from queue: tool_executing
392
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_completed, msg=✓ Completed: analyze_correlations
393
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 10
394
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
395
+ [SSE] Successfully queued event to subscriber 1
396
+ ✓ Tool completed successfully
397
+ 💾 Checkpoint saved: iteration 2, last tool: analyze_correlations
398
+ 📊 EVALUATE: Interpreting results...
399
+ [SSE] GOT event from queue: tool_completed
400
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
401
+ Interpretation: The `analyze_correlations` tool completed execution, meaning it has computed the statistical relationships between 'fare_amount' and other features in the dataset. However, the raw result does not provide interpretable insights—it only confirms the correlation analysis ran successfully. The actual correlation values and their significance are missing, so we cannot yet determine which features are important drivers of fare amounts.
402
+ Answered: False (confidence: 20%)
403
+ Should stop: False
404
+ Next questions: ["What are the exact correlation coefficients and p-values between 'fare_amount' and other features (e.g., 'distance_km', 'trip_duration', 'surge_multiplier')?", 'Are the observed correlations statistically significant, or could they be artifacts of data quality issues (e.g., outliers, duplicates)?', "How do non-linear relationships (e.g., between 'distance_km' and 'fare_amount') affect feature importance rankings?"]
405
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
406
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 11
407
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
408
+ [SSE] Successfully queued event to subscriber 1
409
+
410
+ ── Iteration 3/8 ──
411
+ 🤔 REASON: Deciding next action...
412
+ [SSE] GOT event from queue: finding_discovered
413
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
414
+ Status: investigating
415
+ Reasoning: The `analyze_correlations` tool was executed but the actual correlation values and their significance are missing. To identify the most important features, we need to visualize the correlation results to interpret the relationships between 'fare_amount' and other features.
416
+ Tool: generate_interactive_correlation_heatmap
417
+ Hypothesis: We expect to visually identify strong correlations between 'fare_amount' and features like 'distance_km', 'city', and temporal features (e.g., 'pickup_time'), which will highlight the most important features in the dataset.
418
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
419
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 12
420
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
421
+ [SSE] Successfully queued event to subscriber 1
422
+ ⚡ ACT: Executing generate_interactive_correlation_heatmap...
423
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: generate_interactive_correlation_heat
424
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 13
425
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
426
+ [SSE] Successfully queued event to subscriber 1
427
+ 📋 Final parameters: ['file_path']
428
+ [SSE] GOT event from queue: reasoning_step
429
+ [SSE] GOT event from queue: tool_executing
430
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_completed, msg=✓ Completed: generate_interactive_correlation_heat
431
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 14
432
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
433
+ [SSE] Successfully queued event to subscriber 1
434
+ ✓ Tool completed successfully
435
+ 💾 Checkpoint saved: iteration 3, last tool: generate_interactive_correlation_heatmap
436
+ 📊 EVALUATE: Interpreting results...
437
+ [SSE] GOT event from queue: tool_completed
438
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
439
+ Interpretation: The interactive correlation heatmap generation means we now have a visual tool to explore and quantify the strength and direction of relationships between features, particularly how they relate to 'fare_amount'. This is a critical step toward identifying important features, but the raw output alone does not provide direct answers—it requires manual interpretation to extract actionable insights.
440
+ Answered: False (confidence: 40%)
441
+ Should stop: False
442
+ Next questions: ["Which features show statistically significant correlations with 'fare_amount', and what is the magnitude of these relationships?", "Are there any unexpected or counterintuitive correlations (e.g., weak correlation between 'distance_km' and 'fare_amount') that warrant further investigation?", "How do temporal features (e.g., 'pickup_time', 'day_of_week') interact with other variables, and do they reveal patterns like surge pricing or demand fluctuations?", 'Are there multicollinearity issues among features that could distort feature importance in predictive models?']
443
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
444
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 15
445
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
446
+ [SSE] Successfully queued event to subscriber 1
447
+
448
+ ── Iteration 4/8 ──
449
+ 🤔 REASON: Deciding next action...
450
+ [SSE] GOT event from queue: finding_discovered
451
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
452
+ Status: investigating
453
+ Reasoning: The interactive correlation heatmap has been generated but not yet interpreted to identify the most important features driving 'fare_amount'. We need to extract and quantify feature importance to answer the user's question confidently.
454
+ Tool: auto_feature_selection
455
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features for further analysis.
456
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
457
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 16
458
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
459
+ [SSE] Successfully queued event to subscriber 1
460
+ ⚡ ACT: Executing auto_feature_selection...
461
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
462
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 17
463
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
464
+ [SSE] Successfully queued event to subscriber 1
465
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
466
+ ⚠️ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed (attempt 1/2): Unsupported file format: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
467
+ Retrying in 1.0s...
468
+ [SSE] GOT event from queue: reasoning_step
469
+ [SSE] GOT event from queue: tool_executing
470
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
471
+ ❌ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed after 2 attempts
472
+ 💡 Suggested fallback tools: []
473
+ ❌ Tool failed: Unsupported file format: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
474
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
475
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 18
476
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
477
+ [SSE] Successfully queued event to subscriber 1
478
+ 📊 EVALUATE: Interpreting results...
479
+ [SSE] GOT event from queue: tool_failed
480
+ INFO: 10.16.34.155:21041 - "GET / HTTP/1.1" 200 OK
481
+ INFO: 10.16.4.123:60395 - "GET /index.css HTTP/1.1" 200 OK
482
+ INFO: 10.16.34.155:21041 - "GET /assets/index-C_AP0pY-.js HTTP/1.1" 304 Not Modified
483
+ INFO: 10.16.34.155:21041 - "GET /favicon.ico HTTP/1.1" 200 OK
484
+ INFO: 10.16.34.155:21041 - "GET /favicon.ico HTTP/1.1" 200 OK
485
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
486
+ Interpretation: The feature importance analysis failed due to an unsupported file format (HTML heatmap instead of raw data), meaning we cannot yet directly quantify which features most influence 'fare_amount'. However, prior steps (correlation analysis and profiling) provide indirect evidence about potential important features, though manual interpretation is still required.
487
+ Answered: False (confidence: 30%)
488
+ Should stop: False
489
+ Next questions: ["What are the exact correlation values between 'fare_amount' and other features (e.g., 'distance_km', 'pickup_time') from the raw correlation analysis?", 'Can we extract the numerical correlation matrix from the interactive heatmap to perform feature importance ranking?', 'Are there non-linear relationships or interaction effects (e.g., distance × city) that simple correlation misses?']
490
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
491
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 19
492
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
493
+ [SSE] Successfully queued event to subscriber 1
494
+
495
+ ── Iteration 5/8 ──
496
+ 🤔 REASON: Deciding next action...
497
+ [SSE] GOT event from queue: finding_discovered
498
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
499
+ Status: investigating
500
+ Reasoning: The `auto_feature_selection` tool failed due to an unsupported file format, and prior steps (correlation heatmap and profiling) provide indirect evidence but no direct quantification of feature importance. Running `train_with_autogluon` will train a model and explicitly rank features by their importance to 'fare_amount', addressing the user's question directly.
501
+ Tool: train_with_autogluon
502
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
503
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
504
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 20
505
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
506
+ [SSE] Successfully queued event to subscriber 1
507
+ ⚡ ACT: Executing train_with_autogluon...
508
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: train_with_autogluon
509
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 21
510
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
511
+ [SSE] Successfully queued event to subscriber 1
512
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'time_limit', 'presets']
513
+ [SSE] GOT event from queue: reasoning_step
514
+ [SSE] GOT event from queue: tool_executing
515
+ [SSE] ENDPOINT: Client connected for session_id=1963e777-4930-4200-9ada-ad742e5de6bc
516
+ [SSE] Queue registered, total subscribers: 1
517
+ INFO: 10.16.4.123:23205 - "GET /api/progress/stream/1963e777-4930-4200-9ada-ad742e5de6bc HTTP/1.1" 200 OK
518
+ [SSE] SENDING connection event to client
519
+ [SSE] No history to replay (fresh session)
520
+ [SSE] Starting event stream loop for session 1963e777-4930-4200-9ada-ad742e5de6bc
521
+
522
+ 🚀 AutoGluon Training Starting...
523
+ 📁 Dataset: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
524
+ 🎯 Target: fare_amount
525
+ ⏱️ Time limit: 1800s
526
+ 📊 Presets: medium_quality
527
+ ⚠️ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed (attempt 1/2): Failed to load data: Error tokenizing data. C error: Expected 1 fields in line 7, saw 3
528
+
529
+ Retrying in 1.0s...
530
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'time_limit', 'presets']
531
+
532
+ 🚀 AutoGluon Training Starting...
533
+ 📁 Dataset: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
534
+ 🎯 Target: fare_amount
535
+ ⏱️ Time limit: 1800s
536
+ 📊 Presets: medium_quality
537
+ ❌ <src.orchestrator.DataScienceCopilot object at 0x7fa993fa0aa0> failed after 2 attempts
538
+ 💡 Suggested fallback tools: []
539
+ ❌ Tool failed: Failed to load data: Error tokenizing data. C error: Expected 1 fields in line 7, saw 3
540
+
541
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: train_with_autogluon
542
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 22
543
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
544
+ [SSE] Successfully queued event to subscriber 1
545
+ 📊 EVALUATE: Interpreting results...
546
+ [SSE] GOT event from queue: tool_failed
547
+ INFO:src.api.app:SSE stream cancelled for session 1963e777-4930-4200-9ada-ad742e5de6bc
548
+ INFO:src.api.app:SSE stream closed for session 1963e777-4930-4200-9ada-ad742e5de6bc
549
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
550
+ Interpretation: The AutoGluon training failed due to a data tokenization error, likely caused by malformed input data (e.g., inconsistent delimiters or corrupted rows). This means we cannot yet directly quantify feature importance for 'fare_amount' using automated methods, and prior indirect evidence (e.g., correlations) remains unvalidated.
551
+ Answered: False (confidence: 20%)
552
+ Should stop: False
553
+ Next questions: ['What is the root cause of the tokenization error (e.g., delimiter mismatch, corrupted rows), and how can the data be cleaned to resolve it?', 'Can we manually inspect the raw data (e.g., line 7) to identify and fix formatting issues before retrying automated feature importance analysis?', 'Are there alternative methods (e.g., manual feature engineering, simpler models) to estimate feature importance given the current data limitations?']
554
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
555
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 23
556
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
557
+ [SSE] Successfully queued event to subscriber 1
558
+
559
+ ── Iteration 6/8 ──
560
+ 🤔 REASON: Deciding next action...
561
+ [SSE] GOT event from queue: finding_discovered
562
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
563
+ Status: investigating
564
+ Reasoning: The previous `auto_feature_selection` and `train_with_autogluon` steps failed due to incorrect input formats, but we have an interactive correlation heatmap that visually suggests relationships. To quantify feature importance, we need to extract the raw correlation data or re-run feature selection on the original dataset (not the HTML file).
565
+ Tool: auto_feature_selection
566
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
567
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
568
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 24
569
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
570
+ [SSE] Successfully queued event to subscriber 1
571
+ ⚡ ACT: Executing auto_feature_selection...
572
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
573
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 25
574
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
575
+ [SSE] Successfully queued event to subscriber 1
576
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
577
+ ❌ Tool failed: File not found: /tmp/data_science_agent/data/processed_data.parquet
578
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
579
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 26
580
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
581
+ [SSE] Successfully queued event to subscriber 1
582
+ 📊 EVALUATE: Interpreting results...
583
+ [SSE] GOT event from queue: reasoning_step
584
+ [SSE] GOT event from queue: tool_executing
585
+ [SSE] GOT event from queue: tool_failed
586
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
587
+ Interpretation: The failure of the `auto_feature_selection` tool due to a missing file means we cannot directly quantify feature importance for 'fare_amount' at this stage. This blocks our ability to answer the original question about important features, though prior indirect evidence (e.g., correlations) suggests potential candidates like 'distance_km' and 'city'. The root cause appears to be a file path or data pipeline issue, not a methodological limitation.
588
+ Answered: False (confidence: 20%)
589
+ Should stop: False
590
+ Next questions: ['What is the correct file path or format for the processed data to enable feature importance analysis?', 'Can we rerun prior correlation analyses on the raw data to extract quantifiable feature importance metrics (e.g., correlation coefficients) as a temporary workaround?', 'Are there data quality issues (e.g., missing values, incorrect formats) in the raw data that caused the AutoGluon tokenization error and need to be addressed first?']
591
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
592
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 27
593
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
594
+ [SSE] Successfully queued event to subscriber 1
595
+
596
+ ── Iteration 7/8 ──
597
+ 🤔 REASON: Deciding next action...
598
+ [SSE] GOT event from queue: finding_discovered
599
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
600
+ Status: investigating
601
+ Reasoning: The previous attempts to quantify feature importance failed due to file format issues or data errors. The most direct next step is to re-run `auto_feature_selection` on the raw dataset (not the HTML heatmap) to obtain a ranked list of important features for 'fare_amount'.
602
+ Tool: auto_feature_selection
603
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
604
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
605
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 28
606
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
607
+ [SSE] Successfully queued event to subscriber 1
608
+ ⚡ ACT: Executing auto_feature_selection...
609
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
610
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 29
611
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
612
+ [SSE] Successfully queued event to subscriber 1
613
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
614
+ ❌ Tool failed: File not found: /tmp/data_science_agent/data/raw_dataset.parquet
615
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
616
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 30
617
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
618
+ [SSE] Successfully queued event to subscriber 1
619
+ 📊 EVALUATE: Interpreting results...
620
+ [SSE] GOT event from queue: reasoning_step
621
+ [SSE] GOT event from queue: tool_executing
622
+ [SSE] GOT event from queue: tool_failed
623
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
624
+ Interpretation: The failure due to a missing file means we cannot directly quantify feature importance for 'fare_amount' at this stage, blocking the original question. This is likely a data pipeline or file path issue, not a methodological limitation, and requires immediate resolution before proceeding with feature analysis.
625
+ Answered: False (confidence: 10%)
626
+ Should stop: False
627
+ Next questions: ['What is the correct file path for the raw dataset, and does the file exist in the expected location?', 'Are there alternative data sources or formats (e.g., CSV, JSON) available for the same dataset to bypass the file path issue?', 'Can we validate the integrity of the data pipeline to ensure no corruption or misalignment occurred during prior steps?']
628
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
629
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 31
630
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
631
+ [SSE] Successfully queued event to subscriber 1
632
+
633
+ ── Iteration 8/8 ──
634
+ 🤔 REASON: Deciding next action...
635
+ [SSE] GOT event from queue: finding_discovered
636
+ INFO:src.api.app:[ASYNC] Created new session: f0b5e746...
637
+ INFO:src.api.app:[ASYNC] File saved: intraday_tips.csv
638
+ INFO: 10.16.4.123:13810 - "POST /run-async HTTP/1.1" 200 OK
639
+ [SSE] ENDPOINT: Client connected for session_id=f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77
640
+ [SSE] Queue registered, total subscribers: 1
641
+ INFO: 10.16.34.155:48122 - "GET /api/progress/stream/f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77 HTTP/1.1" 200 OK
642
+ [SSE] SENDING connection event to client
643
+ [SSE] No history to replay (fresh session)
644
+ [SSE] Starting event stream loop for session f0b5e746-66ec-40ba-b4dc-dbff0ff5ac77
645
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
646
+ Status: investigating
647
+ Reasoning: The previous attempts to quantify feature importance failed due to file format issues (HTML instead of raw data). We need to ensure the raw data is used for feature selection to directly answer the user's question about important features.
648
+ Tool: auto_feature_selection
649
+ Hypothesis: We expect to identify the top features (e.g., 'distance_km', 'city', temporal features) that strongly influence 'fare_amount', providing a ranked list of important features with quantifiable importance scores.
650
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=reasoning_step, msg=
651
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 32
652
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
653
+ [SSE] Successfully queued event to subscriber 1
654
+ ⚡ ACT: Executing auto_feature_selection...
655
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_executing, msg=🔧 Executing: auto_feature_selection
656
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 33
657
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
658
+ [SSE] Successfully queued event to subscriber 1
659
+ 📋 Final parameters: ['file_path', 'target_col', 'task_type', 'method']
660
+ ❌ Tool failed: File not found: /tmp/data_science_agent/data/raw_data.parquet
661
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=tool_failed, msg=❌ FAILED: auto_feature_selection
662
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 34
663
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
664
+ [SSE] Successfully queued event to subscriber 1
665
+ 📊 EVALUATE: Interpreting results...
666
+ [SSE] GOT event from queue: reasoning_step
667
+ [SSE] GOT event from queue: tool_executing
668
+ [SSE] GOT event from queue: tool_failed
669
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
670
+ Interpretation: The result indicates a critical data pipeline failure—specifically, the input file cannot be located—blocking any direct analysis of feature importance for 'fare_amount'. This means the original question remains unanswered, and no new insights about feature importance can be derived until the file path or data access issue is resolved.
671
+ Answered: False (confidence: 10%)
672
+ Should stop: False
673
+ Next questions: ['What is the correct file path for the dataset, and does the file exist in the expected location?', 'Are there alternative datasets or backups available to proceed with feature importance analysis?', 'Can we validate the data pipeline to ensure no other files are missing or corrupted before retrying?', 'Should we prioritize debugging the file access issue or switch to indirect methods (e.g., correlation analysis) to infer feature importance temporarily?']
674
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=finding_discovered, msg=
675
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 35
676
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
677
+ [SSE] Successfully queued event to subscriber 1
678
+
679
+ ============================================================
680
+ 📝 SYNTHESIZE: Building final answer from 8 findings...
681
+ ============================================================
682
+ [SSE] GOT event from queue: finding_discovered
683
+ INFO:httpx:HTTP Request: POST https://api.mistral.ai/v1/chat/completions "HTTP/1.1 200 OK"
684
+ [DEBUG] Report tool detected: generate_ydata_profiling_report
685
+ [DEBUG] nested_result keys: ['success', 'report_path', 'message', 'statistics']
686
+ [DEBUG] Report path found: /tmp/data_science_agent/uber_trips_profiling_report.html
687
+ [DEBUG] Added to artifacts[reports], total reports: 1
688
+ [DEBUG] Extracting plot from tool: generate_interactive_correlation_heatmap
689
+ [DEBUG] result keys: ['success', 'tool', 'arguments', 'result']
690
+ [DEBUG] nested_result keys: ['status', 'plot_type', 'output_path', 'num_features']
691
+ [DEBUG] output_path in nested_result: True
692
+ [DEBUG] output_path value: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
693
+ [DEBUG] Added plot to array:
694
+ [DEBUG] title: Correlation Heatmap
695
+ [DEBUG] url: /outputs/plots/interactive/correlation_heatmap.html
696
+ [DEBUG] type: html
697
+ [DEBUG] Merging 1 reports into plots array
698
+ [DEBUG] Added report to plots array: title='Generate Ydata Profiling Report', url='/outputs//tmp/data_science_agent/uber_trips_profiling_report.html'
699
+ [DEBUG] Final plots array length: 2
700
+
701
+ ✅ Reasoning loop completed in 233.75s
702
+ Iterations: 8
703
+ Tools used: generate_ydata_profiling_report, analyze_correlations, generate_interactive_correlation_heatmap, auto_feature_selection, train_with_autogluon
704
+ API calls: 18
705
+ INFO:src.api.app:[BACKGROUND] Analysis completed for session 2df5d6d1...
706
+ [SSE] PROGRESS_MANAGER EMIT: session=2df5d6d1-f23c-48fb-b353-373e7693b75e, event_type=analysis_complete, msg=✅ Analysis completed successfully!
707
+ [SSE] History stored, total events for 2df5d6d1-f23c-48fb-b353-373e7693b75e: 36
708
+ [SSE] Found 1 subscribers for 2df5d6d1-f23c-48fb-b353-373e7693b75e
709
+ [SSE] Successfully queued event to subscriber 1
710
+ [SSE] GOT event from queue: analysis_complete
711
+ INFO:src.api.app:SSE stream closed for session 2df5d6d1-f23c-48fb-b353-373e7693b75e
712
+ INFO:src.api.app:Found file at: /tmp/data_science_agent/uber_trips_profiling_report.html
713
+ INFO: 10.16.4.123:27264 - "GET /outputs//tmp/data_science_agent/uber_trips_profiling_report.html HTTP/1.1" 200 OK
714
+ INFO:src.api.app:Found file at: /tmp/data_science_agent/outputs/plots/interactive/correlation_heatmap.html
715
+ INFO: 10.16.4.123:46507 - "GET /outputs/plots/interactive/correlation_heatmap.html HTTP/1.1" 200 OK
716
+
src/api/app.py CHANGED
@@ -165,7 +165,7 @@ class SessionState:
165
  request_count: int = 0
166
 
167
  session_states: Dict[str, SessionState] = {} # session_id -> SessionState
168
- agent_cache_lock = asyncio.Lock()
169
  MAX_CACHED_SESSIONS = 50 # Increased limit for scale
170
  SESSION_TTL_MINUTES = 60 # Sessions expire after 1 hour of inactivity
171
  logger.info("👥 Multi-user session isolation initialized (fast mode)")
@@ -189,8 +189,8 @@ async def get_agent_for_session(session_id: str) -> DataScienceCopilot:
189
  Session state is isolated using thread-local storage to prevent race conditions.
190
  This reduces per-user initialization from 20s to <1s.
191
 
192
- THREAD SAFETY: Uses threading.local() so each request thread gets its own
193
- agent reference with isolated session, preventing cross-contamination.
194
 
195
  Args:
196
  session_id: Unique session identifier
@@ -200,7 +200,7 @@ async def get_agent_for_session(session_id: str) -> DataScienceCopilot:
200
  """
201
  global agent
202
 
203
- async with agent_cache_lock:
204
  # Ensure base agent exists (heavy components loaded once at startup)
205
  if agent is None:
206
  logger.warning("Base agent not initialized - this shouldn't happen after startup")
@@ -294,7 +294,10 @@ def cleanup_expired_sessions():
294
  # - Race conditions on file writes
295
  # - Memory exhaustion from parallel model training
296
  # - Session state corruption
297
- workflow_lock = asyncio.Lock()
 
 
 
298
  logger.info("🔒 Workflow lock initialized for request queuing")
299
 
300
  # Mount static files for React frontend
@@ -466,65 +469,67 @@ class AnalysisRequest(BaseModel):
466
 
467
  def run_analysis_background(file_path: str, task_description: str, target_col: Optional[str],
468
  use_cache: bool, max_iterations: int, session_id: str):
469
- """Background task to run analysis and emit events."""
470
- async def _run_with_lock():
471
- """Wrap analysis in lock to ensure sequential execution."""
472
- async with workflow_lock:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
473
  try:
474
- logger.info(f"[BACKGROUND] Starting analysis for session {session_id[:8]}...")
475
-
476
- # 🧹 Clear SSE history for fresh event stream (prevents duplicate results)
477
- print(f"[🧹] Clearing SSE history for {session_id[:8]}...")
478
- if session_id in progress_manager._history:
479
- progress_manager._history[session_id] = []
480
-
481
- # 👥 Get isolated agent for this session
482
- session_agent = await get_agent_for_session(session_id)
483
-
484
- result = session_agent.analyze(
485
- file_path=file_path,
486
- task_description=task_description,
487
- target_col=target_col,
488
- use_cache=use_cache,
489
- max_iterations=max_iterations
490
- )
491
-
492
- logger.info(f"[BACKGROUND] Analysis completed for session {session_id[:8]}...")
493
-
494
- # Send appropriate completion event based on status
495
- if result.get("status") == "error":
496
- progress_manager.emit(session_id, {
497
- "type": "analysis_failed",
498
- "status": "error",
499
- "message": result.get("summary", "❌ Analysis failed"),
500
- "error": result.get("error", "Analysis error"),
501
- "result": result
502
- })
503
- else:
504
- progress_manager.emit(session_id, {
505
- "type": "analysis_complete",
506
- "status": result.get("status"),
507
- "message": "✅ Analysis completed successfully!",
508
- "result": result
509
- })
510
-
511
- except Exception as e:
512
- logger.error(f"[BACKGROUND] Analysis failed for session {session_id[:8]}...: {e}")
513
  progress_manager.emit(session_id, {
514
  "type": "analysis_failed",
515
- "error": str(e),
516
- "message": f"❌ Analysis failed: {str(e)}"
 
 
517
  })
518
-
519
- # Run async function in event loop
520
- import asyncio
521
- try:
522
- loop = asyncio.get_event_loop()
523
- except RuntimeError:
524
- loop = asyncio.new_event_loop()
525
- asyncio.set_event_loop(loop)
526
-
527
- loop.run_until_complete(_run_with_lock())
 
 
 
 
 
 
 
528
 
529
 
530
  @app.post("/run-async")
@@ -572,7 +577,7 @@ async def run_analysis_async(
572
  else:
573
  # 🛡️ VALIDATION: Check if this session has dataset cached
574
  has_dataset = False
575
- async with agent_cache_lock:
576
  # Check session_states cache for this specific session_id
577
  if session_id in session_states:
578
  state = session_states[session_id]
 
165
  request_count: int = 0
166
 
167
  session_states: Dict[str, SessionState] = {} # session_id -> SessionState
168
+ agent_cache_lock = threading.Lock() # threading.Lock for cross-event-loop safety
169
  MAX_CACHED_SESSIONS = 50 # Increased limit for scale
170
  SESSION_TTL_MINUTES = 60 # Sessions expire after 1 hour of inactivity
171
  logger.info("👥 Multi-user session isolation initialized (fast mode)")
 
189
  Session state is isolated using thread-local storage to prevent race conditions.
190
  This reduces per-user initialization from 20s to <1s.
191
 
192
+ THREAD SAFETY: Uses threading.Lock so this works from both the main event loop
193
+ AND background thread-pool workers (avoiding asyncio event-loop binding issues).
194
 
195
  Args:
196
  session_id: Unique session identifier
 
200
  """
201
  global agent
202
 
203
+ with agent_cache_lock:
204
  # Ensure base agent exists (heavy components loaded once at startup)
205
  if agent is None:
206
  logger.warning("Base agent not initialized - this shouldn't happen after startup")
 
294
  # - Race conditions on file writes
295
  # - Memory exhaustion from parallel model training
296
  # - Session state corruption
297
+ # NOTE: Uses threading.Lock (not asyncio.Lock) because run_analysis_background
298
+ # is executed in a Starlette thread pool worker, not the main event loop.
299
+ import threading
300
+ workflow_lock = threading.Lock()
301
  logger.info("🔒 Workflow lock initialized for request queuing")
302
 
303
  # Mount static files for React frontend
 
469
 
470
  def run_analysis_background(file_path: str, task_description: str, target_col: Optional[str],
471
  use_cache: bool, max_iterations: int, session_id: str):
472
+ """Background task to run analysis and emit events.
473
+
474
+ Runs in a Starlette thread-pool worker. Uses threading.Lock (not asyncio)
475
+ to serialize concurrent analysis requests.
476
+ """
477
+ with workflow_lock:
478
+ try:
479
+ logger.info(f"[BACKGROUND] Starting analysis for session {session_id[:8]}...")
480
+
481
+ # 🧹 Clear SSE history for fresh event stream (prevents duplicate results)
482
+ print(f"[🧹] Clearing SSE history for {session_id[:8]}...")
483
+ if session_id in progress_manager._history:
484
+ progress_manager._history[session_id] = []
485
+
486
+ # 👥 Get isolated agent for this session
487
+ # get_agent_for_session is async but now uses threading.Lock internally,
488
+ # so we need a small event loop just for the await
489
+ import asyncio
490
  try:
491
+ loop = asyncio.new_event_loop()
492
+ asyncio.set_event_loop(loop)
493
+ session_agent = loop.run_until_complete(get_agent_for_session(session_id))
494
+ finally:
495
+ loop.close()
496
+
497
+ result = session_agent.analyze(
498
+ file_path=file_path,
499
+ task_description=task_description,
500
+ target_col=target_col,
501
+ use_cache=use_cache,
502
+ max_iterations=max_iterations
503
+ )
504
+
505
+ logger.info(f"[BACKGROUND] Analysis completed for session {session_id[:8]}...")
506
+
507
+ # Send appropriate completion event based on status
508
+ if result.get("status") == "error":
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
509
  progress_manager.emit(session_id, {
510
  "type": "analysis_failed",
511
+ "status": "error",
512
+ "message": result.get("summary", "❌ Analysis failed"),
513
+ "error": result.get("error", "Analysis error"),
514
+ "result": result
515
  })
516
+ else:
517
+ progress_manager.emit(session_id, {
518
+ "type": "analysis_complete",
519
+ "status": result.get("status"),
520
+ "message": "✅ Analysis completed successfully!",
521
+ "result": result
522
+ })
523
+
524
+ except Exception as e:
525
+ logger.error(f"[BACKGROUND] Analysis failed for session {session_id[:8]}...: {e}")
526
+ import traceback
527
+ traceback.print_exc()
528
+ progress_manager.emit(session_id, {
529
+ "type": "analysis_failed",
530
+ "error": str(e),
531
+ "message": f"❌ Analysis failed: {str(e)}"
532
+ })
533
 
534
 
535
  @app.post("/run-async")
 
577
  else:
578
  # 🛡️ VALIDATION: Check if this session has dataset cached
579
  has_dataset = False
580
+ with agent_cache_lock:
581
  # Check session_states cache for this specific session_id
582
  if session_id in session_states:
583
  state = session_states[session_id]
src/orchestrator.py CHANGED
@@ -1303,25 +1303,35 @@ You receive quality reports from EDA agent and deliver clean data to modeling ag
1303
  return self.cache._generate_key(cache_key_str)
1304
 
1305
  def _get_last_successful_file(self, workflow_history: List[Dict]) -> str:
1306
- """Find the last successfully created file from workflow history."""
 
 
 
 
 
 
1307
  # Check in reverse order for file-creating tools
1308
  for step in reversed(workflow_history):
1309
  result = step.get("result", {})
1310
  if result.get("success"):
1311
  # Check for output_path in result
1312
  if "output_path" in result:
1313
- return result["output_path"]
 
1314
  # For nested results
1315
  if "result" in result and isinstance(result["result"], dict):
1316
  nested = result["result"]
1317
  if "output_path" in nested:
1318
- return nested["output_path"]
 
1319
  # Check output_dir for dashboard-type tools
1320
  if "output_dir" in nested:
1321
  return nested["output_dir"]
1322
  # Check generated_files from execute_python_code
1323
  if "generated_files" in nested and nested["generated_files"]:
1324
- return nested["generated_files"][0]
 
 
1325
  # Check tool arguments for file_path as last resort
1326
  args = step.get("arguments", step.get("result", {}).get("arguments", {}))
1327
  if isinstance(args, dict) and "file_path" in args:
@@ -1660,10 +1670,19 @@ You receive quality reports from EDA agent and deliver clean data to modeling ag
1660
  report_path = nested_result.get("output_path") or nested_result.get("report_path")
1661
  if report_path:
1662
  print(f"[DEBUG] Report path found: {report_path}")
 
 
 
 
 
 
 
 
 
1663
  artifacts["reports"].append({
1664
  "name": tool.replace("_", " ").title(),
1665
  "path": report_path,
1666
- "url": f"/outputs/{report_path.replace('./outputs/', '')}"
1667
  })
1668
  print(f"[DEBUG] Added to artifacts[reports], total reports: {len(artifacts['reports'])}")
1669
 
@@ -3098,7 +3117,8 @@ You receive quality reports from EDA agent and deliver clean data to modeling ag
3098
 
3099
  # Track for API response
3100
  workflow_history = []
3101
- current_file = file_path # Tracks the latest output file
 
3102
 
3103
  # Emit mode info for UI
3104
  if hasattr(self, 'session') and self.session:
@@ -3220,13 +3240,20 @@ You receive quality reports from EDA agent and deliver clean data to modeling ag
3220
 
3221
  tool_result = self._execute_tool(tool_name, tool_args)
3222
 
3223
- # Track output file for next iteration
3224
  if tool_result.get("success", True):
3225
  result_data = tool_result.get("result", {})
3226
  if isinstance(result_data, dict):
3227
  new_file = result_data.get("output_file") or result_data.get("output_path")
3228
  if new_file:
3229
- current_file = new_file
 
 
 
 
 
 
 
3230
 
3231
  # Emit success
3232
  if hasattr(self, 'session') and self.session:
 
1303
  return self.cache._generate_key(cache_key_str)
1304
 
1305
  def _get_last_successful_file(self, workflow_history: List[Dict]) -> str:
1306
+ """Find the last successfully created DATA file from workflow history.
1307
+
1308
+ Only returns actual data files (CSV, parquet, etc.), NOT visualization
1309
+ artifacts (HTML, PNG, etc.) which would break downstream tools.
1310
+ """
1311
+ data_extensions = ('.csv', '.parquet', '.xlsx', '.xls', '.json', '.tsv')
1312
+
1313
  # Check in reverse order for file-creating tools
1314
  for step in reversed(workflow_history):
1315
  result = step.get("result", {})
1316
  if result.get("success"):
1317
  # Check for output_path in result
1318
  if "output_path" in result:
1319
+ if result["output_path"].lower().endswith(data_extensions):
1320
+ return result["output_path"]
1321
  # For nested results
1322
  if "result" in result and isinstance(result["result"], dict):
1323
  nested = result["result"]
1324
  if "output_path" in nested:
1325
+ if nested["output_path"].lower().endswith(data_extensions):
1326
+ return nested["output_path"]
1327
  # Check output_dir for dashboard-type tools
1328
  if "output_dir" in nested:
1329
  return nested["output_dir"]
1330
  # Check generated_files from execute_python_code
1331
  if "generated_files" in nested and nested["generated_files"]:
1332
+ for gen_file in nested["generated_files"]:
1333
+ if gen_file.lower().endswith(data_extensions):
1334
+ return gen_file
1335
  # Check tool arguments for file_path as last resort
1336
  args = step.get("arguments", step.get("result", {}).get("arguments", {}))
1337
  if isinstance(args, dict) and "file_path" in args:
 
1670
  report_path = nested_result.get("output_path") or nested_result.get("report_path")
1671
  if report_path:
1672
  print(f"[DEBUG] Report path found: {report_path}")
1673
+ # Clean path for URL — handle both ./outputs and /tmp paths
1674
+ if report_path.startswith('./outputs/'):
1675
+ url_path = report_path.replace('./outputs/', '')
1676
+ elif report_path.startswith('/tmp/data_science_agent/outputs/'):
1677
+ url_path = report_path.replace('/tmp/data_science_agent/outputs/', '')
1678
+ elif report_path.startswith('/tmp/data_science_agent/'):
1679
+ url_path = report_path.replace('/tmp/data_science_agent/', '')
1680
+ else:
1681
+ url_path = report_path.split('/')[-1]
1682
  artifacts["reports"].append({
1683
  "name": tool.replace("_", " ").title(),
1684
  "path": report_path,
1685
+ "url": f"/outputs/{url_path}"
1686
  })
1687
  print(f"[DEBUG] Added to artifacts[reports], total reports: {len(artifacts['reports'])}")
1688
 
 
3117
 
3118
  # Track for API response
3119
  workflow_history = []
3120
+ original_data_file = file_path # NEVER changes — always the uploaded dataset
3121
+ current_file = file_path # Tracks the latest DATA file (csv/parquet only)
3122
 
3123
  # Emit mode info for UI
3124
  if hasattr(self, 'session') and self.session:
 
3240
 
3241
  tool_result = self._execute_tool(tool_name, tool_args)
3242
 
3243
+ # Track output file for next iteration — ONLY update for data files
3244
  if tool_result.get("success", True):
3245
  result_data = tool_result.get("result", {})
3246
  if isinstance(result_data, dict):
3247
  new_file = result_data.get("output_file") or result_data.get("output_path")
3248
  if new_file:
3249
+ # Only update current_file for actual data files (CSV, parquet, etc.)
3250
+ # NOT for visualizations (HTML, PNG, JPG) or reports
3251
+ data_extensions = ('.csv', '.parquet', '.xlsx', '.xls', '.json', '.tsv')
3252
+ if new_file.lower().endswith(data_extensions):
3253
+ current_file = new_file
3254
+ print(f" 📂 Updated current data file: {new_file}")
3255
+ else:
3256
+ print(f" 📊 Output artifact (not updating data file): {new_file}")
3257
 
3258
  # Emit success
3259
  if hasattr(self, 'session') and self.session:
src/reasoning/reasoner.py CHANGED
@@ -73,14 +73,16 @@ CRITICAL RULES:
73
  - Output ONLY valid JSON, no other text
74
  - Use EXACT tool names from the available tools list
75
  - Use EXACT column names from the dataset schema
76
- - The file_path argument should use the most recent output file when available
 
 
77
  - For visualization, pick the chart type that best answers the question
78
  - NEVER hallucinate column names - use only columns from the schema"""
79
 
80
  REASONER_USER_TEMPLATE = """**User's question**: {question}
81
 
82
  **Dataset info**:
83
- - File: {file_path}
84
  - Rows: {num_rows:,} | Columns: {num_columns}
85
  - Numeric columns: {numeric_columns}
86
  - Categorical columns: {categorical_columns}
@@ -92,6 +94,8 @@ REASONER_USER_TEMPLATE = """**User's question**: {question}
92
  **Available tools**:
93
  {tools_description}
94
 
 
 
95
  Decide the next action. Respond with ONLY this JSON:
96
  {{
97
  "status": "investigating" or "done",
@@ -308,6 +312,13 @@ class Reasoner:
308
  ]:
309
  arguments["file_path"] = file_path
310
 
 
 
 
 
 
 
 
311
  return ReasoningOutput(
312
  status=status,
313
  reasoning=data.get("reasoning", ""),
 
73
  - Output ONLY valid JSON, no other text
74
  - Use EXACT tool names from the available tools list
75
  - Use EXACT column names from the dataset schema
76
+ - For the file_path argument, ALWAYS use the ORIGINAL DATA FILE path (the CSV/parquet that was uploaded), NOT any output artifact paths (HTML reports, plots, etc.)
77
+ - If a previous tool produced a new data file (CSV/parquet), use THAT as file_path
78
+ - NEVER use an HTML, PNG, or report path as file_path for data-consuming tools
79
  - For visualization, pick the chart type that best answers the question
80
  - NEVER hallucinate column names - use only columns from the schema"""
81
 
82
  REASONER_USER_TEMPLATE = """**User's question**: {question}
83
 
84
  **Dataset info**:
85
+ - Original data file (use this for file_path): {file_path}
86
  - Rows: {num_rows:,} | Columns: {num_columns}
87
  - Numeric columns: {numeric_columns}
88
  - Categorical columns: {categorical_columns}
 
94
  **Available tools**:
95
  {tools_description}
96
 
97
+ IMPORTANT: For ANY tool that needs a file_path argument, use "{file_path}" — the original data file. Do NOT use paths to HTML reports, plots, or other output artifacts.
98
+
99
  Decide the next action. Respond with ONLY this JSON:
100
  {{
101
  "status": "investigating" or "done",
 
312
  ]:
313
  arguments["file_path"] = file_path
314
 
315
+ # 🛡️ SAFETY: Override file_path if LLM picked a non-data file (HTML, PNG, etc.)
316
+ if "file_path" in arguments:
317
+ fp = arguments["file_path"]
318
+ non_data_extensions = ('.html', '.png', '.jpg', '.jpeg', '.svg', '.gif', '.pdf')
319
+ if fp.lower().endswith(non_data_extensions):
320
+ arguments["file_path"] = file_path
321
+
322
  return ReasoningOutput(
323
  status=status,
324
  reasoning=data.get("reasoning", ""),