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| # modules/orchestrator.py | |
| """ | |
| The Central Nervous System of Project Asclepius. | |
| (v2.0 - The "Clinical Insight Engine" Upgrade) | |
| This version uses a smarter post-processing function to guarantee clean output. | |
| """ | |
| import asyncio | |
| import aiohttp | |
| from itertools import chain | |
| from PIL import Image | |
| from . import gemini_handler, prompts, utils | |
| from api_clients import ( | |
| pubmed_client, clinicaltrials_client, openfda_client, rxnorm_client | |
| ) | |
| # --- Internal Helper for Data Formatting (Unchanged) --- | |
| def _format_api_data_for_prompt(api_results: dict) -> dict[str, str]: | |
| # This function is unchanged. | |
| formatted_strings = {} | |
| 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." | |
| 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." | |
| 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." | |
| 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 | |
| # ============================================================================== | |
| # V2.0 UPGRADE: A robust function to remove any AI-generated preamble/disclaimer. | |
| # ============================================================================== | |
| def _clean_ai_preamble(report_text: str) -> str: | |
| """Intelligently removes redundant disclaimers or preambles added by the AI.""" | |
| lines = report_text.strip().split('\n') | |
| # AI disclaimers are often short, in the first few lines, and contain specific keywords. | |
| # We find the first line that looks like real content (starts with '##' for our format). | |
| start_index = 0 | |
| for i, line in enumerate(lines): | |
| if line.strip().startswith('##'): | |
| start_index = i | |
| break | |
| # Failsafe for the first 5 lines if no '##' is found | |
| if i > 5: | |
| break | |
| return '\n'.join(lines[start_index:]) | |
| # --- FEATURE 1: Symptom Synthesizer Pipeline (v2.0) --- | |
| async def run_symptom_synthesis(user_query: str, image_input: Image.Image | None) -> str: | |
| # (Steps 1-4 remain the same) | |
| if not user_query: return "Please enter a symptom description or a medical question to begin." | |
| 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 | |
| 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) | |
| 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...", image_input) | |
| raw_results = await asyncio.gather(*tasks.values(), return_exceptions=True) | |
| api_data = dict(zip(tasks.keys(), raw_results)) | |
| formatted_data = _format_api_data_for_prompt(api_data) | |
| # STEP 5: The Grand Synthesis (using new v2.0 prompt) | |
| 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: Intelligent Post-Processing | |
| cleaned_report = _clean_ai_preamble(final_report) | |
| # STEP 7: Final Delivery | |
| return f"{prompts.DISCLAIMER}\n\n{cleaned_report}" | |
| # --- FEATURE 2: Drug Interaction & Safety Analyzer Pipeline (v2.0) --- | |
| async def run_drug_interaction_analysis(drug_list_str: str) -> str: | |
| # (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." | |
| 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 (using new v2.0 prompt) | |
| 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) | |
| # Intelligent Post-Processing | |
| cleaned_report = _clean_ai_preamble(final_report) | |
| return f"{prompts.DISCLAIMER}\n\n{cleaned_report}" |