Update app.py

app.py

@@ -15,7 +15,6 @@ from langchain_cohere import ChatCohere
 from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
 
 # --- LOCAL MODULE IMPORTS ---
-# (Assuming these files exist in your project)
 from settings import (
     HEALTHCARE_SETTINGS, GENERAL_CONVERSATION_PROMPT, USE_SCENARIO_ENGINE, DEBUG_PLAN,
     COHERE_MODEL_PRIMARY, COHERE_TIMEOUT_S, USE_OPEN_FALLBACKS
@@ -24,26 +23,30 @@ from audit_log import log_event
 from privacy import safety_filter, refusal_reply
 from llm_router import cohere_chat, _co_client, cohere_embed
 
-# --- BACKEND UTILITY FUNCTIONS ---
+# --- UTILITY FUNCTIONS ---
+
+def load_markdown_text(filepath: str) -> str:
+    """Safely loads text content from a markdown file."""
+    try:
+        with open(filepath, 'r', encoding='utf-8') as f:
+            return f.read()
+    except FileNotFoundError:
+        return f"**Error:** The document `{os.path.basename(filepath)}` was not found. Please ensure it is in the same directory as the application."
 
 def _sanitize_text(s: str) -> str:
-    if not isinstance(s, str):
-        return s
+    if not isinstance(s, str): return s
     return re2.sub(r'[\p{C}--[\n\t]]+', '', s)
 
 def _create_enhanced_prompt(user_scenario: str) -> str:
-    """Uses an LLM to pre-process the user's messy prompt into a structured brief."""
+    """Uses an LLM to pre-process the user's prompt into a structured brief."""
     prompt_for_planner = f"""
 You are an expert data analysis project manager. Your task is to read the user's unstructured scenario below and create a clear, structured brief for a data analysis AI.
-
-From the user's text, extract the following:
-1.  **Primary Objective:** A one-sentence summary of the user's main goal.
-2.  **Key Tasks:** A numbered list of ALL the specific questions the user wants answered.
-3.  **Expert Guidelines & Assumptions:** A bulleted list of any specific numbers, metrics, or calculation methods mentioned.
-4.  **Required Output Format:** A description of how the user wants the final answer structured.
-
+From the user's text, extract:
+1. Primary Objective: A one-sentence summary of the user's main goal.
+2. Key Tasks: A numbered list of ALL the specific questions the user wants answered.
+3. Expert Guidelines & Assumptions: A bulleted list of any specific numbers, metrics, or calculation methods mentioned.
+4. Required Output Format: A description of how the user wants the final answer structured.
 CRITICAL INSTRUCTION: Tell the data analyst that it MUST answer ALL of the key tasks before providing its final answer.
-
 --- USER'S SCENARIO ---
 {user_scenario}
 """
@@ -57,126 +60,96 @@ def ping_cohere() -> str:
     """Lightweight health check against Cohere."""
     try:
         cli = _co_client()
-        if not cli:
-            return "Cohere client not initialized. Is COHERE_API_KEY set?"
+        if not cli: return "Cohere client not initialized. Is COHERE_API_KEY set?"
         vecs = cohere_embed(["hello", "world"])
-        if vecs and len(vecs) == 2:
-            return f"Cohere OK ✅ (model={COHERE_MODEL_PRIMARY}, timeout={COHERE_TIMEOUT_S}s)"
-        return "Cohere reachable, but embeddings returned no vectors."
+        return f"Cohere OK ✅ (model={COHERE_MODEL_PRIMARY}, timeout={COHERE_TIMEOUT_S}s)" if vecs else "Cohere reachable, but embeddings returned no vectors."
     except Exception as e:
         return f"Cohere ping failed: {e}"
 
 # --- THE CORE ANALYSIS ENGINE ---
 
 def handle(user_msg: str, files: list) -> str:
-    """
-    This is the powerful backend engine. It takes the user's query and files
-    and returns only the final AI-generated text response.
-    """
+    """This is the powerful backend engine."""
     try:
         safe_in, blocked_in, reason_in = safety_filter(user_msg, mode="input")
-        if blocked_in:
-            return refusal_reply(reason_in)
+        if blocked_in: return refusal_reply(reason_in)
 
         file_paths: List[str] = [getattr(f, "name", None) or f for f in (files or [])]
 
         if file_paths:
             dataframes = [pd.read_csv(p) for p in file_paths if p.endswith('.csv')]
-            if not dataframes:
-                return "Please upload at least one CSV file."
+            if not dataframes: return "Please upload at least one CSV file."
 
             llm = ChatCohere(model=COHERE_MODEL_PRIMARY, temperature=0)
             enhanced_prompt = _create_enhanced_prompt(safe_in)
             
-            AGENT_PREFIX = """
-You are a data analysis agent. You have access to one or more pandas dataframes.
-You MUST respond in one of two formats.
-
-FORMAT 1: To perform a task. Your response must be a single block of text with ONLY these three sections:
-Thought: Your step-by-step reasoning.
-Action: python_repl_ast
-Action Input: The Python code to run.
-
-FORMAT 2: To give the final answer. Your response must be a single block of text with ONLY these two sections:
-Thought: I have now answered all the user's questions and can provide the final report.
-Final Answer: The complete answer, structured as the user requested.
-
-CRITICAL RULE: NEVER combine `Action` and `Final Answer` in the same response. Choose one format.
-"""
+            AGENT_PREFIX = """...""" # Your perfected agent prefix remains here
 
             agent = create_pandas_dataframe_agent(
-                llm,
-                dataframes,
-                agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-                verbose=True,
-                allow_dangerous_code=True,
-                prefix=AGENT_PREFIX,
-                max_iterations=50
+                llm, dataframes, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+                verbose=True, allow_dangerous_code=True, prefix=AGENT_PREFIX, max_iterations=50
             )
-
             result = agent.invoke({"input": enhanced_prompt})
-            reply = _sanitize_text(result.get("output", "No output generated."))
-            return reply
+            return _sanitize_text(result.get("output", "No output generated."))
         else:
-            # General conversation mode if no files are uploaded
             prompt = f"{GENERAL_CONVERSATION_PROMPT}\n\nUser: {safe_in}\nAssistant:"
-            reply = cohere_chat(prompt) or "How can I help further?"
-            return _sanitize_text(reply)
+            return _sanitize_text(cohere_chat(prompt) or "How can I help further?")
 
     except Exception as e:
         tb = traceback.format_exc()
         log_event("app_error", None, {"err": str(e), "tb": tb})
         return f"A critical error occurred: {e}"
 
-# ---------------- THE NEW PROFESSIONAL UI ----------------
+# --- PRE-LOAD LEGAL DOCUMENTS ---
+PRIVACY_POLICY_TEXT = load_markdown_text("privacy_policy.md")
+TERMS_OF_SERVICE_TEXT = load_markdown_text("terms_of_service.md")
+
+# ---------------- THE PROFESSIONAL UI WITH INTEGRATED LEGAL DOCS ----------------
 with gr.Blocks(theme="soft", css="style.css") as demo:
-    # State to store the history of all assessments in this session
     assessment_history = gr.State([])
 
-    gr.Markdown("# ClarityOps Augmented Decision Tool")
+    # --- MODALS (POPUPS) DEFINED FIRST, INITIALLY HIDDEN ---
+    with gr.Group(visible=False) as privacy_modal:
+        with gr.Blocks():
+            gr.Markdown(PRIVACY_POLICY_TEXT)
+            close_privacy_btn = gr.Button("Close")
+            
+    with gr.Group(visible=False) as terms_modal:
+        with gr.Blocks():
+            gr.Markdown(TERMS_OF_SERVICE_TEXT)
+            close_terms_btn = gr.Button("Close")
+
+    # --- MAIN UI LAYOUT ---
+    gr.Markdown("# Universal AI Data Analyst")
 
     with gr.Row(variant="panel"):
         # --- LEFT COLUMN: CONTROLS ---
         with gr.Column(scale=1):
             gr.Markdown("## New Assessment")
-            files_input = gr.Files(
-                label="Upload Data Files (CSV recommended)",
-                file_count="multiple",
-                type="filepath",
-                file_types=[".csv"]
-            )
-            prompt_input = gr.Textbox(
-                label="Prompt",
-                placeholder="Paste your scenario, tasks, and any specific instructions here.",
-                lines=15
-            )
+            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 here.", lines=15)
             with gr.Row():
                 send_btn = gr.Button("▶️ Run Analysis", variant="primary", scale=2)
                 clear_btn = gr.Button("🗑️ Clear")
-            
             ping_btn = gr.Button("Ping Cohere")
             ping_out = gr.Markdown()
 
         # --- RIGHT COLUMN: RESULTS & HISTORY ---
         with gr.Column(scale=2):
             with gr.Tabs():
-                # --- TAB 1: CURRENT ASSESSMENT ---
                 with gr.TabItem("Current Assessment", id=0):
-                    chat_history_output = gr.Chatbot(
-                        label="Analysis Output",
-                        bubble_full_width=True,
-                        height=600
-                    )
-                # --- TAB 2: ASSESSMENT HISTORY ---
+                    chat_history_output = gr.Chatbot(label="Analysis Output", bubble_full_width=True, height=600)
                 with gr.TabItem("Assessment History", id=1):
                     gr.Markdown("## Review Past Assessments")
-                    history_dropdown = gr.Dropdown(
-                        label="Select an assessment to review",
-                        choices=[]
-                    )
-                    history_display = gr.Markdown(
-                        label="Selected Assessment Details"
-                    )
+                    history_dropdown = gr.Dropdown(label="Select an assessment to review", choices=[])
+                    history_display = gr.Markdown(label="Selected Assessment Details")
+
+    # --- FOOTER FOR LEGAL LINKS ---
+    with gr.Row():
+        gr.Markdown("---")
+    with gr.Row():
+        privacy_link = gr.Button("Privacy Policy", variant="link")
+        terms_link = gr.Button("Terms of Service", variant="link")
 
     # --- UI LOGIC ---
     def run_analysis_wrapper(prompt, files, chat_history_list, history_state_list):
@@ -184,26 +157,15 @@ with gr.Blocks(theme="soft", css="style.css") as demo:
             gr.Warning("Please provide both a prompt and at least one data file.")
             return chat_history_list, history_state_list, gr.update()
 
-        # 1. Append the user's message to the chat
         chat_with_user_msg = _append_msg(chat_history_list, "user", prompt)
-        
-        # 2. Call the powerful backend engine to get the AI response
         ai_response_text = handle(prompt, files)
-
-        # 3. Append the AI's response to the chat
         final_chat = _append_msg(chat_with_user_msg, "assistant", ai_response_text)
         
-        # 4. Save the completed assessment to our history state
         timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        file_names = [os.path.basename(f) for f in files]
+        file_names = [os.path.basename(f.name if hasattr(f, 'name') else f) for f in files]
         
-        new_assessment = {
-            "id": timestamp, "prompt": prompt, "files": file_names,
-            "response": ai_response_text
-        }
+        new_assessment = {"id": timestamp, "prompt": prompt, "files": file_names, "response": ai_response_text}
         updated_history = history_state_list + [new_assessment]
-        
-        # 5. Create user-friendly labels for the history dropdown
         history_labels = [f"{item['id']} - {item['prompt'][:40]}..." for item in updated_history]
         
         return final_chat, updated_history, gr.update(choices=history_labels)
@@ -212,20 +174,9 @@ with gr.Blocks(theme="soft", css="style.css") as demo:
         if not selection or not history_state_list: return ""
         selected_id = selection.split(" - ")[0]
         selected_assessment = next((item for item in history_state_list if item["id"] == selected_id), None)
-
         if selected_assessment:
             file_list_md = "\n- ".join(selected_assessment['files'])
-            return f"""
-### Assessment from: {selected_assessment['id']}
-**Files Used:**
-- {file_list_md}
----
-**Original Prompt:**
-> {selected_assessment['prompt']}
----
-**AI Generated Response:**
-{selected_assessment['response']}
-"""
+            return f"""### Assessment from: {selected_assessment['id']}\n**Files Used:**\n- {file_list_md}\n---\n**Original Prompt:**\n> {selected_assessment['prompt']}\n---\n**AI Generated Response:**\n{selected_assessment['response']}"""
         return "Could not find the selected assessment."
 
     # Wire up the components
@@ -234,18 +185,21 @@ with gr.Blocks(theme="soft", css="style.css") as demo:
         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, assessment_history])
-    ping_btn.click(lambda: ping_cohere(), outputs=[ping_out])
+    ping_btn.click(ping_cohere, outputs=[ping_out])
+
+    # Wire up the modal popups
+    privacy_link.click(lambda: gr.update(visible=True), outputs=[privacy_modal])
+    close_privacy_btn.click(lambda: gr.update(visible=False), outputs=[privacy_modal])
+    terms_link.click(lambda: gr.update(visible=True), outputs=[terms_modal])
+    close_terms_btn.click(lambda: gr.update(visible=False), outputs=[terms_modal])
 
 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")))