Update backend.py

backend.py

@@ -1,21 +1,15 @@
----
-
-```python
-from fastapi import FastAPI, HTTPException, Depends, Response, status
+from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse, HTMLResponse, FileResponse
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel
 from langchain_ollama import OllamaLLM
 from langchain.agents import AgentExecutor, create_react_agent
 from langchain.tools import tool
 from langchain import hub
 import time, uuid
-import pdfkit
-import os
 
 app = FastAPI()
 
-# Allow CORS
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -23,38 +17,32 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Initialize LLM
 llm = OllamaLLM(model="qwen3", temperature=0.2)
 
 # ── Tools ──────────────────────────────────────────────────────────
 
 @tool
 def intake_incident(description: str) -> str:
-    """Step 1: Intake and classify an AV incident. Extract vehicle ID, timestamp, location, and incident 
-type."""
+    """Step 1: Intake and classify an AV incident. Extract vehicle ID, timestamp, location, and incident type."""
     return llm.invoke(
         f"You are an AV intake agent. Extract and classify this incident. "
-        f"Return: vehicle_id, timestamp, location, incident_type, initial_severity (P1/P2/P3).\n\nIncident: 
-{description}"
+        f"Return: vehicle_id, timestamp, location, incident_type, initial_severity (P1/P2/P3).\n\nIncident: {description}"
     )
 
 @tool
 def enrich_incident(intake_summary: str) -> str:
-    """Step 2: Enrich the incident with AV domain knowledge. Identify affected subsystems (GNSS, LiDAR, 
-perception, planning, control)."""
+    """Step 2: Enrich the incident with AV domain knowledge. Identify affected subsystems (GNSS, LiDAR, perception, planning, control)."""
     return llm.invoke(
-        f"You are an AV enrichment agent. Given this intake summary, identify which AV subsystems are 
-affected "
+        f"You are an AV enrichment agent. Given this intake summary, identify which AV subsystems are affected "
         f"and what sensor or software failures may be involved.\n\nIntake: {intake_summary}"
     )
 
 @tool
 def analyze_root_cause(enriched_summary: str) -> str:
-    """Step 3: Perform root cause analysis on an enriched AV incident summary. Flag compliance issues."""
+    """Step 3: Perform root cause analysis on an enriched AV incident summary."""
     return llm.invoke(
         f"You are an AV root cause analysis agent. Analyze the enriched incident and identify "
-        f"the most probable root causes ranked by confidence. Reference ISO 26262, SAE J3016, or NHTSA AV 
-Policy where relevant.\n\nEnriched summary: {enriched_summary}"
+        f"the most probable root causes ranked by confidence. Reference ISO 26262, SAE J3016, or NHTSA AV Policy where relevant.\n\nEnriched summary: {enriched_summary}"
     )
 
 @tool
@@ -62,22 +50,18 @@ def generate_response_plan(root_cause_analysis: str) -> str:
     """Step 4: Generate a recommended response and remediation plan based on root cause analysis."""
     return llm.invoke(
         f"You are an AV safety response agent. Based on this root cause analysis, generate: "
-        f"(1) immediate response actions, (2) short-term fixes, (3) long-term prevention 
-measures.\n\nAnalysis: {root_cause_analysis}"
+        f"(1) immediate response actions, (2) short-term fixes, (3) long-term prevention measures.\n\nAnalysis: {root_cause_analysis}"
     )
 
 @tool
 def write_incident_report(full_analysis: str) -> str:
-    """Writes a formal AV incident report in HTML format with Qwen personality."""
+    """Writes a formal AV incident report in Markdown format with Qwen personality."""
     return llm.invoke(
         f"You are Qwen, the AutoPulse Bot. Start your report EXACTLY with this greeting:\n"
-        f"'Hi, I'm Qwen the AutoPulse Bot. I am now looking through your submissions on issues and I will 
-diagnose.'\n\n"
-        f"Then write a formal incident report in HTML with sections: "
-        f"Executive Summary, Timeline, Affected Subsystems, Root Causes, Compliance Flags, Response Plan, 
-Recommendations.\n\n"
-        f"At the very end, add a section called '## Qwen's Personal Recommendation' where you give your own 
-"
+        f"'Hi, I'm Qwen the AutoPulse Bot. I am now looking through your submissions on issues and I will diagnose.'\n\n"
+        f"Then write a formal incident report in Markdown with sections: "
+        f"Executive Summary, Timeline, Affected Subsystems, Root Causes, Compliance Flags, Response Plan, Recommendations.\n\n"
+        f"At the very end, add a section called '## Qwen's Personal Recommendation' where you give your own "
         f"expert suggestion based on your knowledge of AV systems, drawing from your own reasoning — "
         f"not just repeating the analysis above. Be specific, technical, and insightful.\n\n"
         f"Analysis to report on: {full_analysis}"
@@ -85,12 +69,10 @@ Recommendations.\n\n"
 
 @tool
 def analyze_branch_diff(diff_and_failure: str) -> str:
-    """Analyzes a git diff and failure description to rank root cause suspects by file, line, and 
-confidence."""
+    """Analyzes a git diff and failure description to rank root cause suspects by file, line, and confidence."""
     return llm.invoke(
         f"You are a code forensics agent. Analyze this git diff and failure description. "
-        f"Rank root cause suspects by file path, line range, mechanism, and confidence 
-(high/medium/low).\n\n{diff_and_failure}"
+        f"Rank root cause suspects by file path, line range, mechanism, and confidence (high/medium/low).\n\n{diff_and_failure}"
     )
 
 @tool
@@ -98,11 +80,10 @@ def forensic_url_analysis(url_or_content: str) -> str:
     """Performs forensic analysis on a URL or file content for security and safety issues."""
     return llm.invoke(
         f"You are a forensic analysis agent. Analyze this content for suspicious patterns, "
-        f"security vulnerabilities, or safety issues. Flag anomalies with severity.\n\nContent: 
-{url_or_content}"
+        f"security vulnerabilities, or safety issues. Flag anomalies with severity.\n\nContent: {url_or_content}"
     )
 
-# ── Agent Setup ────────────────────────────────────────────────────
+# ── Agent setup ────────────────────────────────────────────────────
 
 tools = [
     intake_incident,
@@ -124,7 +105,7 @@ agent_executor = AgentExecutor(
     max_iterations=8,
 )
 
-# ── Request Models ─────────────────────────────────────────────────
+# ── Request models ─────────────────────────────────────────────────
 
 class Query(BaseModel):
     input: str
@@ -148,49 +129,37 @@ def health():
 @app.post("/triage")
 def triage(query: Query):
     """Full 5-step agentic triage pipeline."""
-    try:
-        result = agent_executor.invoke({
-            "input": (
-                f"Run the full AV incident triage pipeline for this incident: {query.input}\n\n"
-                f"Steps: (1) intake_incident, (2) enrich_incident, (3) analyze_root_cause, "
-                f"(4) generate_response_plan, (5) write_incident_report. "
-                f"Pass the output of each step into the next."
-            )
-        })
-        return HTMLResponse(result["output"])
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    result = agent_executor.invoke({
+        "input": (
+            f"Run the full AV incident triage pipeline for this incident: {query.input}\n\n"
+            f"Steps: (1) intake_incident, (2) enrich_incident, (3) analyze_root_cause, "
+            f"(4) generate_response_plan, (5) write_incident_report. "
+            f"Pass the output of each step into the next."
+        )
+    })
+    return {"output": result["output"]}
 
 @app.post("/branch-debug")
 def branch_debug(query: Query):
     """Agentic branch diff root cause analysis."""
-    try:
-        result = agent_executor.invoke({
-            "input": f"Use analyze_branch_diff to find root causes in this diff and failure: {query.input}"
-        })
-        return HTMLResponse(result["output"])
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    result = agent_executor.invoke({
+        "input": f"Use analyze_branch_diff to find root causes in this diff and failure: {query.input}"
+    })
+    return {"output": result["output"]}
 
 @app.post("/forensic")
 def forensic(query: Query):
     """Agentic forensic analysis."""
-    try:
-        result = agent_executor.invoke({
-            "input": f"Use forensic_url_analysis to analyze this content for issues: {query.input}"
-        })
-        return HTMLResponse(result["output"])
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    result = agent_executor.invoke({
+        "input": f"Use forensic_url_analysis to analyze this content for issues: {query.input}"
+    })
+    return {"output": result["output"]}
 
 @app.post("/analyze")
 def analyze(query: Query):
     """General agentic analysis endpoint."""
-    try:
-        result = agent_executor.invoke({"input": query.input})
-        return HTMLResponse(result["output"])
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    result = agent_executor.invoke({"input": query.input})
+    return {"output": result["output"]}
 
 @app.post("/v1/chat/completions")
 def chat_completions(req: ChatRequest):
@@ -198,51 +167,23 @@ def chat_completions(req: ChatRequest):
     user_messages = [m.content for m in req.messages if m.role == "user"]
     user_input = user_messages[-1] if user_messages else ""
 
-    try:
-        result = agent_executor.invoke({
-            "input": (
-                f"Run the full AV incident triage pipeline: "
-                f"(1) intake_incident, (2) enrich_incident, (3) analyze_root_cause, "
-                f"(4) generate_response_plan, (5) write_incident_report. "
-                f"Input: {user_input}"
-            )
-        })
-        return JSONResponse({
-            "id": f"chatcmpl-{uuid.uuid4().hex}",
-            "object": "chat.completion",
-            "created": int(time.time()),
-            "model": req.model,
-            "choices": [{
-                "index": 0,
-                "message": {"role": "assistant", "content": result["output"]},
-                "finish_reason": "stop"
-            }]
-        })
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/download/{report_id}")
-def download_report(report_id: str):
-    """Download report as PDF."""
-    # For simplicity, assume report is stored in a cache or DB
-    html_content = get_report(report_id)  # Replace with actual logic
-    pdf = pdfkit.from_string(html_content, output_path="report.pdf")
-    return FileResponse("report.pdf", filename=f"report_{report_id}.pdf")
-
-def get_report(report_id: str):
-    """Mock function to fetch report content (replace with real DB/cache logic)."""
-    return "## Incident Report\n\n### Executive Summary\n\nThis is a sample report."
-
-# ── Optional: Dashboard Endpoint ──
-@app.get("/reports")
-def list_reports():
-    """List all reports (mock implementation)."""
-    return {"reports": ["report_123", "report_456"]}
-
-# ── Run the app ──
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
-```
-
----
+    result = agent_executor.invoke({
+        "input": (
+            f"Run the full AV incident triage pipeline: "
+            f"(1) intake_incident, (2) enrich_incident, (3) analyze_root_cause, "
+            f"(4) generate_response_plan, (5) write_incident_report. "
+            f"Input: {user_input}"
+        )
+    })
+
+    return JSONResponse({
+        "id": f"chatcmpl-{uuid.uuid4().hex}",
+        "object": "chat.completion",
+        "created": int(time.time()),
+        "model": req.model,
+        "choices": [{
+            "index": 0,
+            "message": {"role": "assistant", "content": result["output"]},
+            "finish_reason": "stop"
+        }]
+    })