Update modules/orchestrator.py

modules/orchestrator.py

@@ -0,0 +1,80 @@
+# modules/orchestrator.py
+"""
+The main conductor. This module sequences the calls to APIs and the AI model.
+It's the heart of the application's logic.
+"""
+import asyncio
+import aiohttp
+import ast
+from . import gemini_handler, prompts
+from .api_clients import umls_client, pubmed_client
+
+async def run_symptom_synthesis(user_query: str, image_input=None):
+    """
+    The complete pipeline for the Symptom Synthesizer tab.
+    """
+    if not user_query:
+        return "Please enter your symptoms or query."
+
+    # --- Step 1: Extract Key Concepts with Gemini ---
+    term_extraction_prompt = prompts.get_term_extraction_prompt(user_query)
+    concepts_str = await gemini_handler.generate_gemini_response(term_extraction_prompt)
+    try:
+        # Safely evaluate the string representation of the list
+        concepts = ast.literal_eval(concepts_str)
+        if not isinstance(concepts, list):
+            concepts = [user_query] # Fallback
+    except (ValueError, SyntaxError):
+        concepts = [user_query] # Fallback if Gemini doesn't return a perfect list
+
+    search_query = " AND ".join(concepts)
+
+    # --- Step 2: Gather Evidence Asynchronously ---
+    async with aiohttp.ClientSession() as session:
+        # Create a UMLS client instance for this session
+        umls = umls_client.UMLSClient(session)
+
+        # Define all async tasks
+        tasks = {
+            "pubmed": pubmed_client.search_pubmed(session, search_query, max_results=3),
+            "umls_cui": umls.get_cui_for_term(concepts[0] if concepts else user_query),
+            # Add other clients here as they are built e.g.,
+            # "trials": clinicaltrials_client.find_trials(session, search_query),
+            # "fda": openfda_client.get_adverse_events(session, concepts)
+        }
+        
+        # Run all tasks concurrently
+        results = await asyncio.gather(*tasks.values(), return_exceptions=True)
+        
+        # Map results back to their keys, handling potential errors
+        api_data = dict(zip(tasks.keys(), results))
+        for key, value in api_data.items():
+            if isinstance(value, Exception):
+                print(f"Error fetching data from {key}: {value}")
+                api_data[key] = None # Nullify data if fetch failed
+
+    # --- Step 3: Format Data for the Synthesis Prompt ---
+    # Convert raw JSON/list data into clean, readable strings for the AI
+    pubmed_formatted = "\n".join([f"- Title: {a.get('title', 'N/A')}, PMID: {a.get('uid', 'N/A')}" for a in api_data.get('pubmed', [])])
+    
+    # In a real implementation, you'd format trials and fda data here too
+    trials_formatted = "Trial data fetching is not yet fully implemented in this demo."
+    fda_formatted = "FDA data fetching is not yet fully implemented in this demo."
+
+
+    # --- Step 4: The Grand Synthesis with Gemini ---
+    synthesis_prompt = prompts.get_synthesis_prompt(
+        user_query,
+        concepts,
+        pubmed_data=pubmed_formatted,
+        trials_data=trials_formatted,
+        fda_data=fda_formatted
+    )
+    
+    final_report = await gemini_handler.generate_gemini_response(synthesis_prompt)
+
+    # --- Step 5: Prepend Disclaimer and Return ---
+    return f"{prompts.DISCLAIMER}\n\n{final_report}"
+
+# You would create similar orchestrator functions for other tabs
+# e.g., async def run_drug_interaction_analysis(drug_list): ...