Update modules/orchestrator.py

modules/orchestrator.py

@@ -1,108 +1,161 @@
-# modules/prompts.py
+# modules/orchestrator.py
 """
-Central repository for all Gemini prompt engineering.
-This is the "soul" of the AI, defining its persona, tasks, and output structure.
-(v1.2 - The "Insight Engine" Upgrade)
+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)
 """
 
-# The non-negotiable disclaimer that precedes every major output.
-DISCLAIMER = (
-    "**⚠️ IMPORTANT DISCLAIMER: This is an AI-powered informational tool and NOT a substitute for professional medical advice.** "
-    "The information provided is for educational and research purposes only. "
-    "It is generated by synthesizing publicly available data and may contain inaccuracies or be incomplete. "
-    "**ALWAYS consult a qualified healthcare professional for diagnosis, treatment, or any medical concerns.** "
-    "Never disregard professional medical advice because of something you have read here."
+import asyncio
+import aiohttp
+from itertools import chain
+from PIL import Image
+
+# Import all our specialized tools
+from . import gemini_handler, prompts, utils
+from api_clients import (
+    pubmed_client,
+    clinicaltrials_client,
+    openfda_client,
+    rxnorm_client
 )
 
-
-# (This function remains the same)
-def get_query_correction_prompt(user_text: str) -> str:
-    return f"""
-    You are an expert medical transcriptionist. Your task is to correct and clarify the following user query for a medical database search.
-    - Correct all spelling and grammatical errors.
-    - Translate colloquialisms or typos into proper medical terminology (e.g., "pin" -> "pain", "abdomian" -> "abdomen").
-    - Rephrase as a clear statement or question.
-    - Do not answer the question. Only return the corrected and clarified query.
-    User Query: "{user_text}"
-    Response:
+# --- Internal Helper for Data Formatting ---
+def _format_api_data_for_prompt(api_results: dict) -> dict[str, str]:
     """
-
-# (This function remains the same)
-def get_term_extraction_prompt(user_text: str) -> str:
-    return f"""
-    From the user's corrected query below, extract the most relevant medical concepts, symptoms, or conditions.
-    Return ONLY a Python-style list of strings.
-    User Text: "{user_text}"
-    Response:
+    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)))
+        if all_events:
+            lines = [f"- {evt['term']} (Reported {evt['count']} times)" for evt in all_events]
+            formatted_strings['openfda'] = "\n".join(lines)
+        else:
+            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
+    elif isinstance(vision_data, Exception):
+        formatted_strings['vision'] = f"An error occurred during image analysis: {vision_data}"
+    else:
+        formatted_strings['vision'] = ""
+        
+    return formatted_strings
 
 
 # ==============================================================================
-# V1.2 UPGRADE: The Symptom Synthesis prompt is now a "Narrative Briefing"
+# THIS IS THE FUNCTION THAT WAS REPORTED AS MISSING. PLEASE ENSURE IT EXISTS.
+# --- FEATURE 1: Symptom Synthesizer Pipeline (v1.2) ---
 # ==============================================================================
-def get_synthesis_prompt(user_query: str, concepts: list, pubmed_data: str, trials_data: str, fda_data: str, vision_analysis: str = "") -> str:
+async def run_symptom_synthesis(user_query: str, image_input: Image.Image | None) -> str:
+    """The complete, asynchronous pipeline for the Symptom Synthesizer tab."""
+    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)),
+        }
+        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
+            )
+        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']
+    )
+    final_report = await gemini_handler.generate_text_response(synthesis_prompt)
     
-    # ==============================================================================
-    # CORRECTED LINE: The f-string logic is now handled correctly.
-    vision_section = f"## Analysis of Uploaded Image\n{vision_analysis}" if vision_analysis else ""
-    # ==============================================================================
-
-    return f"""
-    You are Asclepius, an expert medical information analyst. Your task is to transform raw medical data into a coherent, insightful, and beautifully formatted narrative report for a user.
-
-    **YOUR DIRECTIVES:**
-    1.  **START IMMEDIATELY with the provided mandatory disclaimer.** DO NOT add any other preamble, introduction, or disclaimer of your own. Your response must begin with "⚠️ IMPORTANT DISCLAIMER...".
-    2.  **WRITE A NARRATIVE, NOT A LIST.** Do not use "1.", "2.", "3." to structure the main report. Use Markdown headings (##) for each section.
-    3.  **SYNTHESIZE, DON'T JUST LIST.** For each section, provide a short introductory sentence that gives context, then present the data.
-    4.  **BE HELPFUL WHEN DATA IS EMPTY.** If a data source is empty, state that no specific data was found and then provide a brief, high-level overview of the concept from your general knowledge.
-
-    **REPORT STRUCTURE:**
-
-    ## Overview
-    (Start with a short, empathetic paragraph acknowledging the user's query about "{user_query}" and explaining that you have searched public health databases for information on the interpreted concepts: {concepts}.)
-
-    ## Insights from Medical Research
-    (Introduce this section by explaining you've looked for recent review articles on PubMed. Then, summarize the findings or state that none were found and give a general overview.)
-    {pubmed_data if pubmed_data else "No specific review articles were found on PubMed for this query."}
-
-    ## Current Clinical Trials
-    (Introduce this section by explaining these are active studies from ClinicalTrials.gov. Then, list the trials or state that none were found.)
-    {trials_data if trials_data else "No actively recruiting clinical trials were found matching this query."}
-
-    ## Related Drug & Safety Data
-    (Introduce this section by explaining this data comes from OpenFDA. Then, list the findings or state that none were found.)
-    {fda_data if fda_data else "No specific adverse event data was found for this query."}
-
-    {vision_section}
-
-    **Begin your report now. Adhere strictly to these directives.**
-    """
+    # STEP 6: Final Delivery
+    return f"{prompts.DISCLAIMER}\n\n{final_report}"
 
 
 # ==============================================================================
-# V1.2 UPGRADE: The Drug Interaction prompt is now an "Executive Safety Briefing"
+# THIS FUNCTION IS ALSO REQUIRED BY APP.PY. PLEASE ENSURE IT EXISTS.
+# --- FEATURE 2: Drug Interaction & Safety Analyzer Pipeline ---
 # ==============================================================================
-def get_drug_interaction_synthesis_prompt(drug_names: list[str], interaction_data: str, safety_data: str) -> str:
-    return f"""
-    You are a specialist AI focused on drug safety analysis. Your task is to act as a clear, cautious, and organized pharmacist, explaining raw API data to a user.
-
-    **YOUR DIRECTIVES:**
-    1.  **START IMMEDIATELY with the provided mandatory disclaimer.** DO NOT add any other preamble, introduction, or second disclaimer.
-    2.  **WRITE A HUMAN-READABLE BRIEFING.** Do not use sterile numbering ("1.", "2.", "3."). Use descriptive Markdown headings (##).
-    3.  **PROVIDE CONTEXT AND INSIGHT.** Your job is to explain what the data *means* in simple terms.
-
-    **BRIEFING STRUCTURE:**
-
-    ## Executive Summary
-    (Write a concise, 1-2 sentence summary of the most important findings. For example: "A review of {', '.join(drug_names)} found no direct drug-drug interactions, but did identify several commonly reported side effects for each medication." or "A potentially significant interaction was identified between Drug A and Drug B. Details are provided below.")
-
-    ## Drug-Drug Interaction Analysis
-    (If interactions exist, list them here. For each interaction, **explain the consequence in simple terms.** For example: "Taking these together may increase the risk of...". If none, state clearly: "No direct drug-drug interactions were found among the provided list of medications based on the data available.")
-    {interaction_data if interaction_data else "No direct drug-drug interactions were found."}
-
-    ## Individual Drug Safety Profiles
-    (Create a subsection for each drug using `### Drug Name`. Under each, summarize the data found in a user-friendly way.)
-    {safety_data if safety_data else "No individual safety profiles were found."}
-    
-    **Begin your safety briefing now. Adhere strictly to these directives.**
-    """
+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."
+    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."
+    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))
+        }
+        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)}]
+    safety_profiles = api_data.get('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
+    )
+    final_report = await gemini_handler.generate_text_response(synthesis_prompt)
+    return f"{prompts.DISCLAIMER}\n\n{final_report}"