Update modules/orchestrator.py

modules/orchestrator.py

@@ -4,7 +4,7 @@ The Central Nervous System of Project Asclepius.
 This module is the master conductor, orchestrating high-performance, asynchronous
 workflows for each of the application's features. It intelligently sequences
 calls to API clients and the Gemini handler to transform user queries into
-comprehensive, synthesized reports. (v1.2)
+comprehensive, synthesized reports. (v1.3 - Final Polish)
 """
 
 import asyncio
@@ -22,30 +22,21 @@ from api_clients import (
 )
 
 # --- Internal Helper for Data Formatting ---
+# (This helper function remains unchanged)
 def _format_api_data_for_prompt(api_results: dict) -> dict[str, str]:
-    """
-    Takes the raw dictionary of API results and formats each entry into a
-    clean, readable string suitable for injection into a Gemini prompt.
-    """
     formatted_strings = {}
-
-    # Format PubMed data
     pubmed_data = api_results.get('pubmed', [])
     if isinstance(pubmed_data, list) and pubmed_data:
         lines = [f"- Title: {a.get('title', 'N/A')} (Journal: {a.get('journal', 'N/A')}, URL: {a.get('url')})" for a in pubmed_data]
         formatted_strings['pubmed'] = "\n".join(lines)
     else:
         formatted_strings['pubmed'] = "No relevant review articles were found on PubMed for this query."
-
-    # Format Clinical Trials data
     trials_data = api_results.get('trials', [])
     if isinstance(trials_data, list) and trials_data:
         lines = [f"- Title: {t.get('title', 'N/A')} (Status: {t.get('status', 'N/A')}, URL: {t.get('url')})" for t in trials_data]
         formatted_strings['trials'] = "\n".join(lines)
     else:
         formatted_strings['trials'] = "No actively recruiting clinical trials were found matching this query."
-
-    # Format OpenFDA Adverse Events data
     fda_data = api_results.get('openfda', [])
     if isinstance(fda_data, list):
         all_events = list(chain.from_iterable(filter(None, fda_data)))
@@ -56,8 +47,6 @@ def _format_api_data_for_prompt(api_results: dict) -> dict[str, str]:
             formatted_strings['openfda'] = "No specific adverse event data was found for this query."
     else:
         formatted_strings['openfda'] = "No specific adverse event data was found for this query."
-        
-    # Format Vision analysis
     vision_data = api_results.get('vision', "")
     if isinstance(vision_data, str) and vision_data:
         formatted_strings['vision'] = vision_data
@@ -65,97 +54,69 @@ def _format_api_data_for_prompt(api_results: dict) -> dict[str, str]:
         formatted_strings['vision'] = f"An error occurred during image analysis: {vision_data}"
     else:
         formatted_strings['vision'] = ""
-        
     return formatted_strings
 
 
-# ==============================================================================
-# THIS IS THE FUNCTION THAT WAS REPORTED AS MISSING. PLEASE ENSURE IT EXISTS.
-# --- FEATURE 1: Symptom Synthesizer Pipeline (v1.2) ---
-# ==============================================================================
+# --- FEATURE 1: Symptom Synthesizer Pipeline (v1.3) ---
 async def run_symptom_synthesis(user_query: str, image_input: Image.Image | None) -> str:
     """The complete, asynchronous pipeline for the Symptom Synthesizer tab."""
+    # (Steps 1-5 remain the same)
     if not user_query:
         return "Please enter a symptom description or a medical question to begin."
-
-    # STEP 1: AI-Powered Query Correction
     correction_prompt = prompts.get_query_correction_prompt(user_query)
     corrected_query = await gemini_handler.generate_text_response(correction_prompt)
     if not corrected_query:
         corrected_query = user_query
-
-    # STEP 2: AI-Powered Concept Extraction
     term_prompt = prompts.get_term_extraction_prompt(corrected_query)
     concepts_str = await gemini_handler.generate_text_response(term_prompt)
     concepts = utils.safe_literal_eval(concepts_str)
     if not isinstance(concepts, list) or not concepts:
         concepts = [corrected_query]
-
     search_query = " OR ".join(f'"{c}"' for c in concepts)
-
-    # STEP 3: Massively Parallel Evidence Gathering
     async with aiohttp.ClientSession() as session:
-        tasks = {
-            "pubmed": pubmed_client.search_pubmed(session, search_query, max_results=3),
-            "trials": clinicaltrials_client.find_trials(session, search_query, max_results=3),
-            "openfda": asyncio.gather(*(openfda_client.get_adverse_events(session, c, top_n=3) for c in concepts)),
-        }
+        tasks = { "pubmed": pubmed_client.search_pubmed(session, search_query, max_results=3), "trials": clinicaltrials_client.find_trials(session, search_query, max_results=3), "openfda": asyncio.gather(*(openfda_client.get_adverse_events(session, c, top_n=3) for c in concepts)), }
         if image_input:
-            tasks["vision"] = gemini_handler.analyze_image_with_text(
-                "In the context of the user query, analyze this image objectively. Describe visual features. Do not diagnose.", image_input
-            )
+            tasks["vision"] = gemini_handler.analyze_image_with_text("In the context of the user query, analyze this image objectively. Describe visual features. Do not diagnose.", image_input)
         raw_results = await asyncio.gather(*tasks.values(), return_exceptions=True)
         api_data = dict(zip(tasks.keys(), raw_results))
-
-    # STEP 4: Data Formatting
     formatted_data = _format_api_data_for_prompt(api_data)
-
-    # STEP 5: The Grand Synthesis
-    synthesis_prompt = prompts.get_synthesis_prompt(
-        user_query=user_query,
-        concepts=concepts,
-        pubmed_data=formatted_data['pubmed'],
-        trials_data=formatted_data['trials'],
-        fda_data=formatted_data['openfda'],
-        vision_analysis=formatted_data['vision']
-    )
+    synthesis_prompt = prompts.get_synthesis_prompt(user_query=user_query, concepts=concepts, pubmed_data=formatted_data['pubmed'], trials_data=formatted_data['trials'], fda_data=formatted_data['openfda'], vision_analysis=formatted_data['vision'])
     final_report = await gemini_handler.generate_text_response(synthesis_prompt)
     
-    # STEP 6: Final Delivery
-    return f"{prompts.DISCLAIMER}\n\n{final_report}"
+    # ==============================================================================
+    # STEP 6 (V1.3 UPGRADE): Deterministic Post-Processing
+    # We will manually remove the AI's redundant disclaimer to ensure a clean output.
+    # ==============================================================================
+    ghost_disclaimer = "⚠️ IMPORTANT DISCLAIMER: This report is for informational purposes only and should not be considered medical advice. Always consult with a qualified healthcare professional for diagnosis and treatment of any medical condition."
+    cleaned_report = final_report.replace(ghost_disclaimer, "").strip()
 
+    # STEP 7: Final Delivery
+    return f"{prompts.DISCLAIMER}\n\n{cleaned_report}"
 
-# ==============================================================================
-# THIS FUNCTION IS ALSO REQUIRED BY APP.PY. PLEASE ENSURE IT EXISTS.
-# --- FEATURE 2: Drug Interaction & Safety Analyzer Pipeline ---
-# ==============================================================================
+
+# --- FEATURE 2: Drug Interaction & Safety Analyzer Pipeline (v1.3) ---
 async def run_drug_interaction_analysis(drug_list_str: str) -> str:
     """The complete, asynchronous pipeline for the Drug Interaction Analyzer tab."""
-    if not drug_list_str:
-        return "Please enter a comma-separated list of medications."
+    # (Steps remain the same)
+    if not drug_list_str: return "Please enter a comma-separated list of medications."
     drug_names = [name.strip() for name in drug_list_str.split(',') if name.strip()]
-    if len(drug_names) < 2:
-        return "Please enter at least two medications to check for interactions."
+    if len(drug_names) < 2: return "Please enter at least two medications to check for interactions."
     async with aiohttp.ClientSession() as session:
-        tasks = {
-            "interactions": rxnorm_client.run_interaction_check(drug_names),
-            "safety_profiles": asyncio.gather(*(openfda_client.get_safety_profile(session, name) for name in drug_names))
-        }
+        tasks = { "interactions": rxnorm_client.run_interaction_check(drug_names), "safety_profiles": asyncio.gather(*(openfda_client.get_safety_profile(session, name) for name in drug_names)) }
         raw_results = await asyncio.gather(*tasks.values(), return_exceptions=True)
         api_data = dict(zip(tasks.keys(), raw_results))
     interaction_data = api_data.get('interactions', [])
-    if isinstance(interaction_data, Exception):
-        interaction_data = [{"error": str(interaction_data)}]
+    if isinstance(interaction_data, Exception): interaction_data = [{"error": str(interaction_data)}]
     safety_profiles = api_data.get('safety_profiles', [])
-    if isinstance(safety_profiles, Exception):
-        safety_profiles = [{"error": str(safety_profiles)}]
+    if isinstance(safety_profiles, Exception): safety_profiles = [{"error": str(safety_profiles)}]
     safety_data_dict = dict(zip(drug_names, safety_profiles))
     interaction_formatted = utils.format_list_as_markdown([str(i) for i in interaction_data]) if interaction_data else "No interactions found."
     safety_formatted = "\n".join([f"Profile for {drug}: {profile}" for drug, profile in safety_data_dict.items()])
-    synthesis_prompt = prompts.get_drug_interaction_synthesis_prompt(
-        drug_names=drug_names,
-        interaction_data=interaction_formatted,
-        safety_data=safety_formatted
-    )
+    synthesis_prompt = prompts.get_drug_interaction_synthesis_prompt(drug_names=drug_names, interaction_data=interaction_formatted, safety_data=safety_formatted)
     final_report = await gemini_handler.generate_text_response(synthesis_prompt)
-    return f"{prompts.DISCLAIMER}\n\n{final_report}"
+    
+    # Deterministic Post-Processing for the drug report
+    ghost_disclaimer_drug = "DISCLAIMER: This report is for informational purposes only and should not be considered medical advice. Always consult with a healthcare professional before making any decisions related to your health or treatment. This information is based on the provided data and may not be exhaustive."
+    cleaned_report = final_report.replace(ghost_disclaimer_drug, "").strip()
+
+    return f"{prompts.DISCLAIMER}\n\n{cleaned_report}"