Update app.py

app.py

@@ -22,16 +22,25 @@ import regex as re2
 import re
 
 from langchain_cohere import ChatCohere  # noqa: F401
-
 from settings import (
     GENERAL_CONVERSATION_PROMPT,
     COHERE_MODEL_PRIMARY,
-    COHERE_TIMEOUT_S,   # noqa: F401
-    USE_OPEN_FALLBACKS  # noqa: F401
+    COHERE_TIMEOUT_S,
+    USE_OPEN_FALLBACKS,
 )
+from audit_log import log_event
+from privacy import safety_filter, refusal_reply
+from llm_router import cohere_chat, _co_client, cohere_embed
+
 # Try to import optional HIPAA flags; fall back to safe defaults if not defined.
 try:
-    from settings import PHI_MODE, PERSIST_HISTORY, HISTORY_TTL_DAYS, REDACT_BEFORE_LLM, ALLOW_EXTERNAL_PHI
+    from settings import (
+        PHI_MODE,
+        PERSIST_HISTORY,
+        HISTORY_TTL_DAYS,
+        REDACT_BEFORE_LLM,
+        ALLOW_EXTERNAL_PHI,
+    )
 except Exception:
     PHI_MODE = False
     PERSIST_HISTORY = True
@@ -39,13 +48,8 @@ except Exception:
     REDACT_BEFORE_LLM = False
     ALLOW_EXTERNAL_PHI = True
 
-from audit_log import log_event
-from privacy import safety_filter, refusal_reply
-from llm_router import cohere_chat, _co_client, cohere_embed
-
 
 # ---------------------- Helpers (analysis logic selectively improved) ----------------------
-
 def load_markdown_text(filepath: str) -> str:
     try:
         with open(filepath, "r", encoding="utf-8") as f:
@@ -72,6 +76,7 @@ PHI_PATTERNS = [
     (re.compile(r"\b\d{5}(-\d{4})?\b"), "[REDACTED_ZIP]"),
 ]
 
+
 def redact_phi(text: str) -> str:
     if not isinstance(text, str):
         return text
@@ -80,6 +85,7 @@ def redact_phi(text: str) -> str:
         t = pat.sub(repl, t)
     return t
 
+
 def safe_log(event_name: str, meta: dict | None = None):
     # Avoid logging raw PHI or payloads
     try:
@@ -97,7 +103,7 @@ def _create_python_script(user_scenario: str, schema_context: str) -> str:
     The AI first creates a step-by-step plan, then writes code to execute it.
     This ensures the analysis is logical, correctly aggregated, and aligned with the user's goal.
     """
-    prompt_for_coder = f"""\
+    prompt_for_coder = f"""\ 
 You are an expert-level Python data scientist acting as a consultant. Your task is to analyze data to answer a user's business request.
 
 --- USER'S SCENARIO ---
@@ -111,31 +117,33 @@ You are an expert-level Python data scientist acting as a consultant. Your task
 You must follow a rigorous two-step process:
 
 **Step 1: Create a Detailed Analysis Plan.**
-First, think step-by-step. Deconstruct the user's request into a clear, logical plan. The plan must identify the key metrics, necessary data manipulations (cleaning, grouping, aggregation), and the final outputs required.
+First, think step-by-step. Deconstruct the user's request into a clear, logical plan.
+The plan must identify the key metrics, necessary data manipulations (cleaning, grouping, aggregation), and the final outputs required.
+
 - **CRITICAL for aggregation:** If the user asks for analysis by category (e.g., "specialty," "department"), you MUST identify the correct high-level categorical column for grouping. DO NOT aggregate by granular, free-text procedure descriptions unless explicitly asked. Your goal is to find meaningful, strategic trends.
 
 **Step 2: Write the Python Script.**
 Based on your plan, write a complete Python script.
 
 CRITICAL SCRIPTING RULES:
-1.  **NO FILE READING:** The data is already loaded into a list of pandas DataFrames called `dfs`. You MUST use this variable. Do not include `pd.read_csv`.
-2.  **STRICTLY JSON OUTPUT:** The script's ONLY output to stdout MUST be a single, well-structured JSON object containing all the raw data findings from your plan.
-3.  **ROBUST DATA CLEANING:** Before performing calculations, clean data robustly. Convert numeric columns to numbers using `pd.to_numeric(..., errors='coerce')`. Handle missing values (`NaN`) appropriately (e.g., by excluding them from averages).
-4.  **JSON SERIALIZATION:** Ensure all data in the final dictionary is JSON-serializable. Use `.item()` for single numpy values and `.tolist()` for arrays/series.
+1. **NO FILE READING:** The data is already loaded into a list of pandas DataFrames called `dfs`. You MUST use this variable. Do not include `pd.read_csv`.
+2. **STRICTLY JSON OUTPUT:** The script's ONLY output to stdout MUST be a single, well-structured JSON object containing all the raw data findings from your plan.
+3. **ROBUST DATA CLEANING:** Before performing calculations, clean data robustly. Convert numeric columns to numbers using `pd.to_numeric(..., errors='coerce')`. Handle missing values (`NaN`) appropriately (e.g., by excluding them from averages).
+4. **JSON SERIALIZATION:** Ensure all data in the final dictionary is JSON-serializable. Use `.item()` for single numpy values and `.tolist()` for arrays/series.
 
 Now, provide your response in the following format:
 
 **ANALYSIS PLAN:**
-```text
-1.  **Objective:** [Briefly state the main goal]
-2.  **Data Cleaning:** [Describe steps to clean and prepare the data]
-3.  **Analysis Step A:** [e.g., "Calculate average wait times per hospital by grouping `dfs[0]` by 'Facility' and averaging 'Surgery_Median'."]
-4.  **Analysis Step B:** [e.g., "Identify top 5 specialties by grouping `dfs[0]` by the 'Specialty' column and calculating the mean of 'Surgery_Median'."]
-5.  **Analysis Step C:** [e.g., "Determine zone-level performance by grouping by 'Zone' and comparing to the overall provincial average."]
-6.  **JSON Output Structure:** [Describe the keys and values of the final JSON object]
-PYTHON SCRIPT:
-code
-Python
+
+Objective: [Briefly state the main goal]
+Data Cleaning: [Describe steps to clean and prepare the data]
+Analysis Step A: [e.g., "Calculate average wait times per hospital by grouping dfs[0] by 'Facility' and averaging 'Surgery_Median'."]
+Analysis Step B: [e.g., "Identify top 5 specialties by grouping dfs[0] by the 'Specialty' column and calculating the mean of 'Surgery_Median'."]
+Analysis Step C: [e.g., "Determine zone-level performance by grouping by 'Zone' and comparing to the overall provincial average."]
+JSON Output Structure: [Describe the keys and values of the final JSON object]
+
+text**PYTHON SCRIPT:**
+```python
 # Your complete Python script starts here
 import pandas as pd
 import json
@@ -147,17 +155,14 @@ import re
 print(json.dumps(final_data_structure, indent=4))
 """
 generated_text = cohere_chat(prompt_for_coder)
-# This regex is more robust for extracting the final code block
+This regex is more robust for extracting the final code block
 match = re2.search(r"PYTHON SCRIPT:\s*python\n(.*?)", generated_text, re2.DOTALL)
 if match:
 return match.group(1).strip()
-code
-Code
-# Fallback if the structured format fails
-fallback_match = re2.search(r"```python\n(.*?)```", generated_text, re2.DOTALL)
+Fallback if the structured format fails
+fallback_match = re2.search(r"python\n(.*?)", generated_text, re2.DOTALL)
 if fallback_match:
-    return fallback_match.group(1).strip()
-
+return fallback_match.group(1).strip()
 return "print(json.dumps({'error': 'Failed to generate a valid Python script from the plan.'}))"
 def _generate_long_report(prompt: str) -> str:
 try:
@@ -179,7 +184,7 @@ IMPROVED: Generates a professional, structured report from the JSON data.
 The prompt guides the AI to synthesize insights in a standard consulting format,
 ensuring a high level of detail and actionable recommendations.
 """
-prompt_for_writer = f"""
+prompt_for_writer = f"""\
 You are an expert management consultant specializing in data-driven strategy. A Python script has been executed to extract key data points based on a user's request. Your task is to synthesize this raw data into a polished, comprehensive, and actionable report.
 --- USER'S ORIGINAL SCENARIO ---
 {user_scenario}
@@ -189,26 +194,40 @@ You are an expert management consultant specializing in data-driven strategy. A
 --- END RAW DATA ---
 CRITICAL INSTRUCTIONS:
 You must write a final report that follows this exact structure:
-### Executive Summary
+Executive Summary
+
 Start with a brief paragraph summarizing the core problem, key findings, and top recommendations. This should be a high-level overview for a leadership audience.
-### 1. [First Key Finding, e.g., Hospitals with the Longest Wait Times]
+
+1. [First Key Finding, e.g., Hospitals with the Longest Wait Times]
+
 Present the relevant data in a Markdown table.
 Write a short narrative interpreting the data. What does it mean? Are there any outliers? Why might these facilities have long waits (e.g., specialized care, rural location, capacity issues)?
-### 2. [Second Key Finding, e.g., Specialties with the Longest Wait Times]
+
+2. [Second Key Finding, e.g., Specialties with the Longest Wait Times]
+
 Present the relevant data in a Markdown table.
 Interpret the findings. Why are these specialties facing delays (e.g., specialist shortages, equipment needs)?
-### 3. [Third Key Finding, e.g., Zone-Level Performance]
+
+3. [Third Key Finding, e.g., Zone-Level Performance]
+
 Present the data in a table, including a comparison to a relevant average or baseline.
 Analyze the geographic or systemic issues this data reveals.
-### 4. [Fourth Key Finding, if applicable, e.g., Geographic Distribution]
+
+4. [Fourth Key Finding, if applicable, e.g., Geographic Distribution]
+
 Synthesize location data with the wait-time findings.
 Discuss the implications for patient equity, travel burdens, and access to care.
-### 5. Recommendations for Resource Allocation
+
+5. Recommendations for Resource Allocation
+
 Provide specific, actionable, and justified recommendations.
 Structure them by category (e.g., by facility, by specialty, by zone).
 For each recommendation, provide a clear rationale directly linked to the data findings above (e.g., "Allocate additional resources to Glace Bay Hospital because it is a rural facility in a high-wait zone, suggesting a capacity bottleneck.").
-### Data Limitations
+
+Data Limitations
+
 Briefly mention any potential limitations of the analysis (e.g., missing data, use of proxies, case severity not included). This adds credibility to the report.
+
 Do not just repeat the JSON data. Your value is in interpreting the numbers, connecting the dots between different findings, and providing clear, data-backed strategic advice.
 """
 return _generate_long_report(prompt_for_writer)
@@ -220,383 +239,337 @@ cli = _co_client()
 if not cli:
 return "Cohere client not initialized."
 vecs = cohere_embed(["hello", "world"])
-return f"Cohere OK ✅ (model={COHERE_MODEL_PRIMARY})" if vecs else "Cohere reachable."
+return f"Cohere OK (model={COHERE_MODEL_PRIMARY})" if vecs else "Cohere reachable."
 except Exception as e:
 return f"Cohere ping failed: {e}"
 def handle(user_msg: str, files: list, yield_update) -> str:
 try:
-# Safety filter on incoming message
+Safety filter on incoming message
 safe_in, blocked_in, reason_in = safety_filter(user_msg, mode="input")
 if blocked_in:
 return refusal_reply(reason_in)
-code
-Code
-# Optional PHI redaction for prompts sent to an external LLM
-    redacted_in = safe_in
-    if PHI_MODE and REDACT_BEFORE_LLM:
-        redacted_in = redact_phi(safe_in)
-
-    file_paths: List[str] = [getattr(f, "name", None) or f for f in (files or [])]
-
-    if file_paths:
-        # CSV analysis path
-        dataframes, schema_parts = [], []
-        for i, p in enumerate(file_paths):
-            if p.endswith(".csv"):
-                try:
-                    df = pd.read_csv(p)
-                except UnicodeDecodeError:
-                    df = pd.read_csv(p, encoding="latin1")
-                dataframes.append(df)
-                
-                # --- IMPROVEMENT: ENRICHED SCHEMA CONTEXT ---
-                schema_buffer = io.StringIO()
-                df.info(buf=schema_buffer)
-                schema_info = schema_buffer.getvalue()
-                schema_parts.append(
-                    f"""DataFrame `dfs[{i}]` (`{os.path.basename(p)}`):
-Head
-{df.head().to_markdown()}
-Schema and Data Types
-code
-Code
-{schema_info}
-Summary Statistics
-{df.describe(include='all').to_markdown()}
-"""
+Optional PHI redaction for prompts sent to an external LLM
+redacted_in = safe_in
+if PHI_MODE and REDACT_BEFORE_LLM:
+redacted_in = redact_phi(safe_in)
+file_paths: List[str] = [
+getattr(f, "name", None) or f for f in (files or [])
+]
+if file_paths:
+CSV analysis path
+dataframes, schema_parts = [], []
+for i, p in enumerate(file_paths):
+if p.endswith(".csv"):
+try:
+df = pd.read_csv(p)
+except UnicodeDecodeError:
+df = pd.read_csv(p, encoding="latin1")
+dataframes.append(df)
+--- IMPROVEMENT: ENRICHED SCHEMA CONTEXT ---
+schema_buffer = io.StringIO()
+df.info(buf=schema_buffer)
+schema_info = schema_buffer.getvalue()
+schema_parts.append(
+f"""DataFrame dfs[{i}] ({os.path.basename(p)}):\n\nHead\n{df.head().to_markdown()}\n\nSchema and Data Types\n\n{schema_info}\n\n\nSummary Statistics\n{df.describe(include='all').to_markdown()}\n"""
 )
-code
-Code
 if not dataframes:
-            return "Please upload at least one CSV file."
-
-        schema_context = "\n".join(schema_parts)
-
-        # If external PHI is not allowed, use redacted prompt; otherwise use original
-        prompt_for_code = redacted_in if (PHI_MODE and not ALLOW_EXTERNAL_PHI) else safe_in
-
-        yield_update("""```
-🧠 Generating aligned analysis script...
-code
-""")
+return "Please upload at least one CSV file."
+schema_context = "\n".join(schema_parts)
+If external PHI is not allowed, use redacted prompt; otherwise use original
+prompt_for_code = (
+redacted_in if (PHI_MODE and not ALLOW_EXTERNAL_PHI) else safe_in
+)
+yield_update("Generating aligned analysis script...")
 analysis_script = _create_python_script(prompt_for_code, schema_context)
-
-            yield_update("""```
-⚙️ Executing script to extract raw data...
-```""")
-            execution_namespace = {"dfs": dataframes, "pd": pd, "re": re, "json": json}
-            output_buffer = io.StringIO()
-
-            try:
-                with redirect_stdout(output_buffer):
-                    exec(analysis_script, execution_namespace)
-                raw_data_output = output_buffer.getvalue()
-            except Exception as e:
-                return (
-                    f"An error occurred executing the script: {e}\n\nGenerated Script:\n"
-                    f"```python\n{analysis_script}\n```"
-                )
-
-            yield_update("""```
-✍️ Synthesizing final comprehensive report...
-```""")
-            writer_input = redacted_in if (PHI_MODE and not ALLOW_EXTERNAL_PHI) else safe_in
-            final_report = _generate_final_report(writer_input, raw_data_output)
-            return _sanitize_text(final_report)
-        else:
-            # Pure chat path
-            chat_input = redacted_in if (PHI_MODE and not ALLOW_EXTERNAL_PHI) else safe_in
-            prompt = f"{GENERAL_CONVERSATION_PROMPT}\n\nUser: {chat_input}\nAssistant:"
-            return _sanitize_text(cohere_chat(prompt) or "How can I help further?")
-
-    except Exception as e:
-        tb = traceback.format_exc()
-        safe_log("app_error", {"err": str(e)})
-        return "A critical error occurred. Please contact your administrator." if PHI_MODE else f"A critical error occurred: {e}"
-
-
+yield_update("Executing script to extract raw data...")
+execution_namespace = {"dfs": dataframes, "pd": pd, "re": re, "json": json}
+output_buffer = io.StringIO()
+try:
+with redirect_stdout(output_buffer):
+exec(analysis_script, execution_namespace)
+raw_data_output = output_buffer.getvalue()
+except Exception as e:
+return (
+f"An error occurred executing the script: {e}\n\nGenerated Script:\n"
+f"python\n{analysis_script}\n"
+)
+yield_update("Synthesizing final comprehensive report...")
+writer_input = (
+redacted_in if (PHI_MODE and not ALLOW_EXTERNAL_PHI) else safe_in
+)
+final_report = _generate_final_report(writer_input, raw_data_output)
+return _sanitize_text(final_report)
+else:
+Pure chat path
+chat_input = (
+redacted_in if (PHI_MODE and not ALLOW_EXTERNAL_PHI) else safe_in
+)
+prompt = f"{GENERAL_CONVERSATION_PROMPT}\n\nUser: {chat_input}\nAssistant:"
+return _sanitize_text(cohere_chat(prompt) or "How can I help further?")
+except Exception as e:
+tb = traceback.format_exc()
+safe_log("app_error", {"err": str(e)})
+return "A critical error occurred. Please contact your administrator." if PHI_MODE else f"A critical error occurred: {e}"
 PRIVACY_POLICY_TEXT = load_markdown_text("privacy_policy.md")
 TERMS_OF_SERVICE_TEXT = load_markdown_text("terms_of_service.md")
-
-
-# ---------------------- Sleek UI assets (CSS/JS only) ----------------------
-
+---------------------- Sleek UI assets (CSS/JS only) ----------------------
 SLEEK_CSS = """
 /* Full-bleed, modern look */
 :root, body, #root, .gradio-container { height: 100%; }
 .gradio-container { padding: 0 !important; }
 .block { padding: 0 !important; }
-
 /* Header */
 .header {
-  padding: 20px 28px;
-  background: linear-gradient(135deg, #0e1726, #1d2a44 60%, #243a5e);
-  color: #fff;
-  display: flex; align-items: center; justify-content: space-between;
-  gap: 16px;
+padding: 20px 28px;
+background: linear-gradient(135deg, #0e1726, #1d2a44 60%, #243a5e);
+color: #fff;
+display: flex; align-items: center; justify-content: space-between;
+gap: 16px;
 }
 .header h1 { margin: 0; font-size: 22px; letter-spacing: 0.3px; font-weight: 600; }
 .header .badge { font-size: 12px; opacity: 0.9; background:#ffffff22; padding:6px 10px; border-radius: 999px; }
-
 /* Main layout */
 .main {
-  display: grid;
-  grid-template-columns: 420px 1fr;
-  gap: 16px;
-  padding: 16px;
-  height: calc(100vh - 72px);
-  box-sizing: border-box;
+display: grid;
+grid-template-columns: 420px 1fr;
+gap: 16px;
+padding: 16px;
+height: calc(100vh - 72px);
+box-sizing: border-box;
 }
 .left, .right {
-  background: #0b1020;
-  color: #e9edf3;
-  border-radius: 16px;
-  border: 1px solid #1c2642;
+background: #0b1020;
+color: #e9edf3;
+border-radius: 16px;
+border: 1px solid #1c2642;
 }
 .left { padding: 16px; display: flex; flex-direction: column; gap: 12px; }
 .right { padding: 0; display: flex; flex-direction: column; }
-
 /* Panels */
 .panel-title { font-size: 14px; font-weight: 600; color: #aeb8cc; margin-bottom: 6px; }
 .helper { font-size: 12px; color: #97a3bb; margin-bottom: 8px; }
-
 /* Sticky actions */
 .actions {
-  display: flex; gap: 8px; align-items: center; justify-content: stretch;
+display: flex; gap: 8px; align-items: center; justify-content: stretch;
 }
 .actions .gr-button { flex: 1; }
-
 /* Tabs full height */
 .right .tabs { height: 100%; display: flex; flex-direction: column; }
 .right .tabitem { flex: 1; display: flex; flex-direction: column; }
 #chatbot_container { flex: 1; }
 #chatbot_container .gr-chatbot { height: 100%; }
-
 /* Tiny separators */
 .hr { height: 1px; background: #16203b; margin: 10px 0; }
-
 /* Voice hint */
 .voice-hint { font-size: 12px; color:#9fb0cc; margin-top: 4px; }
 """
-
 VOICE_STT_HTML = """
 <script>
 let __rs_rec = null;
 function rs_toggle_stt(elemId){
-  const SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
-  if (!SpeechRecognition){
-    alert("This browser does not support Speech Recognition. Try Chrome or Edge.");
-    return;
-  }
-  if (__rs_rec){ __rs_rec.stop(); __rs_rec = null; return; }
-  __rs_rec = new SpeechRecognition();
-  __rs_rec.lang = "en-US";
-  __rs_rec.interimResults = true;
-  __rs_rec.continuous = true;
-
-  const box = document.querySelector(`#${elemId} textarea`);
-  if (!box){ alert("Prompt box not found."); return; }
-  let base = box.value || "";
-
-  __rs_rec.onresult = (ev) => {
-    let t = "";
-    for (let i = ev.resultIndex; i < ev.results.length; i++){
-      t += ev.results[i][0].transcript;
-    }
-    box.value = (base + " " + t).trim();
-    box.dispatchEvent(new Event("input", { bubbles: true }));
-  };
-  __rs_rec.onend = () => { __rs_rec = null; };
-  __rs_rec.start();
+const SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
+if (!SpeechRecognition){
+alert("This browser does not support Speech Recognition. Try Chrome or Edge.");
+return;
+}
+if (__rs_rec){ __rs_rec.stop(); __rs_rec = null; return; }
+__rs_rec = new SpeechRecognition();
+__rs_rec.lang = "en-US";
+__rs_rec.interimResults = true;
+__rs_rec.continuous = true;
+
+const box = document.querySelector(`#${elemId} textarea`);
+if (!box){ alert("Prompt box not found."); return; }
+let base = box.value || "";
+
+__rs_rec.onresult = (ev) => {
+let t = "";
+for (let i = ev.resultIndex; i < ev.results.length; i++){
+t += ev.results[i][0].transcript;
+}
+box.value = (base + " " + t).trim();
+box.dispatchEvent(new Event("input", { bubbles: true }));
+};
+__rs_rec.onend = () => { __rs_rec = null; };
+__rs_rec.start();
 }
 </script>
 """
-
-
-# ---------------------- Sleek UI (with fixed State wiring) ----------------------
-
+---------------------- Sleek UI (with fixed State wiring) ----------------------
 with gr.Blocks(theme=gr.themes.Soft(), css=SLEEK_CSS, fill_width=True) as demo:
-    # Persistent in-memory history component (fixes list/_id error)
-    assessment_history = gr.State([])
-
-    # Header
-    with gr.Row(elem_classes=["header"]):
-        gr.Markdown("<h1>Clarity Ops Augemented Decision Support</h1>")
-        pill = "PHI Mode ON · history off" if (PHI_MODE and not PERSIST_HISTORY) else \
-               "PHI Mode ON" if PHI_MODE else "PHI Mode OFF"
-        gr.Markdown(f"<span class='badge'>{pill}</span>")
-
-    # Main layout
-    with gr.Row(elem_classes=["main"]):
-        # Left panel
-        with gr.Column(elem_classes=["left"]):
-            gr.Markdown("<div class='panel-title'>New Assessment</div>")
-            gr.Markdown("<div class='helper'>Upload CSVs for analysis, or enter a prompt. Voice works in modern browsers.</div>")
-            files_input = gr.Files(
-                label="Upload Data Files (.csv)",
-                file_count="multiple",
-                type="filepath",
-                file_types=[".csv"],
-            )
-            prompt_input = gr.Textbox(
-                label="Prompt",
-                placeholder="Paste your scenario or question here...",
-                lines=12,
-                elem_id="prompt_box",
-                autofocus=True,
-            )
-
-            with gr.Row(elem_classes=["actions"]):
-                send_btn = gr.Button("▶️ Run Analysis", variant="primary")
-                clear_btn = gr.Button("🧹 Clear")
-                voice_btn = gr.Button("🎙️ Voice")
-
-            gr.Markdown("<div class='voice-hint'>Click Voice to start/stop dictation into the prompt box.</div>")
-            ping_btn = gr.Button("🔌 Ping Cohere")
-            ping_out = gr.Markdown()
-
-            gr.Markdown("<div class='hr'></div>")
-            if PHI_MODE:
-                gr.Markdown(
-                    "⚠️ **PHI Mode:** History persistence is disabled by default. Avoid unnecessary identifiers."
-                )
-
-            with gr.Accordion("Privacy & Terms", open=False):
-                gr.Markdown(PRIVACY_POLICY_TEXT)
-                gr.Markdown("<div class='hr'></div>")
-                gr.Markdown(TERMS_OF_SERVICE_TEXT)
-
-        # Right panel
-        with gr.Column(elem_classes=["right"]):
-            with gr.Tabs(elem_classes=["tabs"]):
-                with gr.TabItem("Current Assessment", id=0, elem_classes=["tabitem"]):
-                    with gr.Column(elem_id="chatbot_container"):
-                        chat_history_output = gr.Chatbot(label="Analysis Output", type="messages")
-                with gr.TabItem("Assessment History", id=1, elem_classes=["tabitem"]):
-                    gr.Markdown("### Review Past Assessments")
-                    history_dropdown = gr.Dropdown(label="Select an assessment to review", choices=[])
-                    history_display = gr.Markdown(label="Selected Assessment Details")
-
-    # Inject voice-to-text helper
-    gr.HTML(VOICE_STT_HTML)
-
-    # --------- Event logic (unchanged analysis flow) ----------
-
-    def run_analysis_wrapper(prompt, files, chat_history_list, history_state_list):
-        if not prompt:
-            gr.Warning("Please enter a prompt.")
-            yield chat_history_list, history_state_list, gr.update()
-            return
-
-        # Append user's message
-        chat_with_user_msg = _append_msg(chat_history_list, "user", prompt)
-
-        # Optional progress callback (not streaming in this UI)
-        def dummy_update(message: str):
-            pass
-
-        # Thinking bubble
-        thinking_message = _append_msg(
-            chat_with_user_msg,
-            "assistant",
-            """```
-🧠 Generating and executing analysis... Please wait.
-```""",
-        )
-        yield thinking_message, history_state_list, gr.update()
-
-        # Run analysis/chat
-        ai_response_text = handle(prompt, files, dummy_update)
-
-        # Append final assistant response
-        final_chat = _append_msg(chat_with_user_msg, "assistant", ai_response_text)
-        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-        # Capture filenames (if any)
-        file_names: List[str] = []
-        if files:
-            file_names = [
-                os.path.basename(f.name if hasattr(f, "name") else f) for f in files
-            ]
-
-        # Build history record
-        new_entry = {
-            "id": timestamp,
-            "prompt": prompt,
-            "files": file_names,
-            "response": ai_response_text,
-            "chat_history": final_chat,
-        }
-
-        # Respect PHI/history flags
-        if PERSIST_HISTORY and (not PHI_MODE or (PHI_MODE and HISTORY_TTL_DAYS > 0)):
-            updated_history: List[Dict[str, Any]] = (history_state_list or []) + [new_entry]
-        else:
-            updated_history = history_state_list or []
-
-        history_labels = [f"{item['id']} - {item['prompt'][:40]}..." for item in updated_history]
-
-        yield final_chat, updated_history, gr.update(choices=history_labels)
-
-    def view_history(selection: str, history_state_list: List[Dict[str, Any]]) -> str:
-        if not selection or not history_state_list:
-            return ""
-        try:
-            selected_id = selection.split(" - ", 1)[0]
-        except Exception:
-            selected_id = selection
-
-        selected_assessment = next(
-            (item for item in history_state_list if item.get("id") == selected_id), None
-        )
-        if not selected_assessment:
-            return "Could not find the selected assessment."
-
-        file_list = selected_assessment.get("files", [])
-        file_list_md = "\n- ".join(file_list) if file_list else "*(no files uploaded)*"
-
-        chat_entries = selected_assessment.get("chat_history", [])
-        chat_md_lines = []
-        for msg in chat_entries:
-            role = msg.get("role", "").capitalize()
-            content = msg.get("content", "")
-            chat_md_lines.append(f"**{role}:** {content}")
-        chat_md = "\n\n".join(chat_md_lines)
-
-        return f"""### Assessment from: {selected_assessment['id']}
-**Files Used:**
-- {file_list_md}
----
-**Original Prompt:**
-> {selected_assessment['prompt']}
----
-**AI Generated Response:**
+Persistent in-memory history component (fixes list/_id error)
+assessment_history = gr.State([])
+Header
+with gr.Row(elem_classes=["header"]):
+gr.Markdown("Clarity Ops Augmented Decision Support")
+pill = (
+"PHI Mode ON · history off"
+if (PHI_MODE and not PERSIST_HISTORY)
+else "PHI Mode ON"
+if PHI_MODE
+else "PHI Mode OFF"
+)
+gr.Markdown(f"{pill}")
+Main layout
+with gr.Row(elem_classes=["main"]):
+Left panel
+with gr.Column(elem_classes=["left"]):
+gr.Markdown("New Assessment")
+gr.Markdown(
+"Upload CSVs for analysis, or enter a prompt. Voice works in modern browsers."
+)
+files_input = gr.Files(
+label="Upload Data Files (.csv)",
+file_count="multiple",
+type="filepath",
+file_types=[".csv"],
+)
+prompt_input = gr.Textbox(
+label="Prompt",
+placeholder="Paste your scenario or question here...",
+lines=12,
+elem_id="prompt_box",
+autofocus=True,
+)
+with gr.Row(elem_classes=["actions"]):
+send_btn = gr.Button("Run Analysis", variant="primary")
+clear_btn = gr.Button("Clear")
+voice_btn = gr.Button("Voice")
+gr.Markdown(
+"Click Voice to start/stop dictation into the prompt box."
+)
+ping_btn = gr.Button("Ping Cohere")
+ping_out = gr.Markdown()
+gr.Markdown("")
+if PHI_MODE:
+gr.Markdown(
+"Warning: PHI Mode: History persistence is disabled by default. Avoid unnecessary identifiers."
+)
+with gr.Accordion("Privacy & Terms", open=False):
+gr.Markdown(PRIVACY_POLICY_TEXT)
+gr.Markdown("")
+gr.Markdown(TERMS_OF_SERVICE_TEXT)
+Right panel
+with gr.Column(elem_classes=["right"]):
+with gr.Tabs(elem_classes=["tabs"]):
+with gr.TabItem("Current Assessment", id=0, elem_classes=["tabitem"]):
+with gr.Column(elem_id="chatbot_container"):
+chat_history_output = gr.Chatbot(
+label="Analysis Output", type="messages"
+)
+with gr.TabItem("Assessment History", id=1, elem_classes=["tabitem"]):
+gr.Markdown("### Review Past Assessments")
+history_dropdown = gr.Dropdown(
+label="Select an assessment to review", choices=[]
+)
+history_display = gr.Markdown(label="Selected Assessment Details")
+Inject voice-to-text helper
+gr.HTML(VOICE_STT_HTML)
+--------- Event logic (unchanged analysis flow) ----------
+def run_analysis_wrapper(
+prompt, files, chat_history_list, history_state_list
+):
+if not prompt:
+gr.Warning("Please enter a prompt.")
+yield chat_history_list, history_state_list, gr.update()
+return
+Append user's message
+chat_with_user_msg = _append_msg(chat_history_list, "user", prompt)
+Thinking bubble
+thinking_message = _append_msg(
+chat_with_user_msg,
+"assistant",
+"Generating and executing analysis... Please wait.",
+)
+yield thinking_message, history_state_list, gr.update()
+Run analysis/chat
+def dummy_update(message: str):
+pass
+ai_response_text = handle(prompt, files, dummy_update)
+Append final assistant response
+final_chat = _append_msg(chat_with_user_msg, "assistant", ai_response_text)
+timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+Capture filenames (if any)
+file_names: List[str] = []
+if files:
+file_names = [
+os.path.basename(f.name if hasattr(f, "name") else f) for f in files
+]
+Build history record
+new_entry = {
+"id": timestamp,
+"prompt": prompt,
+"files": file_names,
+"response": ai_response_text,
+"chat_history": final_chat,
+}
+Respect PHI/history flags
+if PERSIST_HISTORY and (not PHI_MODE or (PHI_MODE and HISTORY_TTL_DAYS > 0)):
+updated_history: List[Dict[str, Any]] = (history_state_list or []) + [
+new_entry
+]
+else:
+updated_history = history_state_list or []
+history_labels = [
+f"{item['id']} - {item['prompt'][:40]}..."
+for item in updated_history
+]
+yield final_chat, updated_history, gr.update(choices=history_labels)
+def view_history(selection: str, history_state_list: List[Dict[str, Any]]) -> str:
+if not selection or not history_state_list:
+return ""
+try:
+selected_id = selection.split(" - ", 1)[0]
+except Exception:
+selected_id = selection
+selected_assessment = next(
+(item for item in history_state_list if item.get("id") == selected_id), None
+)
+if not selected_assessment:
+return "Could not find the selected assessment."
+file_list = selected_assessment.get("files", [])
+file_list_md = "\n- ".join(file_list) if file_list else "(no files uploaded)"
+chat_entries = selected_assessment.get("chat_history", [])
+chat_md_lines = []
+for msg in chat_entries:
+role = msg.get("role", "").capitalize()
+content = msg.get("content", "")
+chat_md_lines.append(f"{role}: {content}")
+chat_md = "\n\n".join(chat_md_lines)
+return f"""### Assessment from: {selected_assessment['id']}
+Files Used:
+
+{file_list_md}
+
+
+Original Prompt:
+{selected_assessment['prompt']}
+
+AI Generated Response:
 {selected_assessment['response']}
----
-**Chat Transcript:**
+Chat Transcript:
 {chat_md}
 """
-
-    # Wire events (using proper gr.State component for history)
-    send_btn.click(
-        run_analysis_wrapper,
-        inputs=[prompt_input, files_input, chat_history_output, assessment_history],
-        outputs=[chat_history_output, assessment_history, history_dropdown],
-    )
-    history_dropdown.change(
-        view_history,
-        inputs=[history_dropdown, assessment_history],
-        outputs=[history_display],
-    )
-    clear_btn.click(
-        lambda: (None, None, []),
-        outputs=[prompt_input, files_input, chat_history_output],
-    )
-    ping_btn.click(ping_cohere, outputs=[ping_out])
-    voice_btn.click(None, [], [], js="rs_toggle_stt('prompt_box')")
-
-
-if __name__ == "__main__":
-    if not os.getenv("COHERE_API_KEY"):
-        print("🔴 COHERE_API_KEY environment variable not set. Application may not function correctly.")
-    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))
+Wire events (using proper gr.State component for history)
+send_btn.click(
+run_analysis_wrapper,
+inputs=[prompt_input, files_input, chat_history_output, assessment_history],
+outputs=[chat_history_output, assessment_history, history_dropdown],
+)
+history_dropdown.change(
+view_history,
+inputs=[history_dropdown, assessment_history],
+outputs=[history_display],
+)
+clear_btn.click(
+lambda: (None, None, []),
+outputs=[prompt_input, files_input, chat_history_output],
+)
+ping_btn.click(ping_cohere, outputs=[ping_out])
+voice_btn.click(None, [], [], js="rs_toggle_stt('prompt_box')")
+if name == "main":
+if not os.getenv("COHERE_API_KEY"):
+print(
+"COHERE_API_KEY environment variable not set. Application may not function correctly."
+)
+demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))