Update modules/prompts.py

modules/prompts.py

@@ -2,11 +2,10 @@
 """
 Central repository for all Gemini prompt engineering.
 This is the "soul" of the AI, defining its persona, tasks, and output structure.
-Each prompt is a carefully crafted contract for the AI's behavior.
+Each prompt is a carefully crafted contract for the AI's behavior. (v1.1)
 """
 
 # The non-negotiable disclaimer that precedes every major output.
-# It is designed to be clear, unambiguous, and always present.
 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. "
@@ -16,18 +15,44 @@ DISCLAIMER = (
 )
 
 
+def get_query_correction_prompt(user_text: str) -> str:
+    """
+    (V1.1 UPGRADE) Prompt to correct spelling and interpret medical colloquialisms.
+    This acts as a "Medical Translator" before any database searches.
+    """
+    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.
+
+    Example 1: "I have a pin in me abdomian"
+    Response: "I have a pain in my abdomen"
+
+    Example 2: "head hurt bad, feel sick"
+    Response: "Severe headache and nausea"
+    
+    Example 3: "whats the latest on alzhimers"
+    Response: "What are the latest treatments for Alzheimer's disease?"
+
+    User Query: "{user_text}"
+    Response:
+    """
+
+
 def get_term_extraction_prompt(user_text: str) -> str:
-    """Prompt to pull structured medical concepts from unstructured user text."""
+    """Prompt to pull structured medical concepts from a corrected user text."""
     return f"""
-    From the user's text below, extract the most relevant medical concepts, symptoms, or conditions.
-    Focus on nouns and medical terminology. Ignore conversational filler.
+    From the user's corrected query below, extract the most relevant medical concepts, symptoms, or conditions.
+    Focus on nouns and medical terminology.
     Return ONLY a Python-style list of strings. Do not add any explanation or preamble.
 
-    Example 1: "I have a pounding headache and my stomach feels sick."
-    Response: ["Headache", "Nausea"]
+    Example 1: "I have a pain in my abdomen"
+    Response: ["Abdominal Pain"]
 
-    Example 2: "Tell me about the latest treatments for type 2 diabetes."
-    Response: ["Type 2 Diabetes"]
+    Example 2: "Severe headache and nausea"
+    Response: ["Severe Headache", "Nausea"]
 
     User Text: "{user_text}"
     Response:
@@ -47,43 +72,39 @@ def get_synthesis_prompt(user_query: str, concepts: list, pubmed_data: str, tria
     You are Asclepius, an expert medical information synthesizer AI. Your purpose is to empower users by organizing and explaining complex medical information from trusted public sources. You never diagnose, give advice, or make recommendations. Your role is to be an objective, evidence-based illuminator.
 
     **User's Original Query:** "{user_query}"
-    **Extracted Key Concepts:** {concepts}
+    **Interpreted Key Concepts:** {concepts}
 
     You have been provided with raw data from several US National Library of Medicine and FDA APIs. Your task is to synthesize this data into a single, clear, structured report for a layperson.
 
     **YOUR REPORT MUST FOLLOW THIS STRUCTURE EXACTLY:**
 
-    1.  **Overview:** Start with a brief summary acknowledging the user's query and the key concepts identified. If an image was analyzed, mention it here.
+    1.  **Overview:** Start with a brief summary acknowledging the user's query and the key concepts you've interpreted from it.
 
     2.  **Evidence-Based Insights from Research (PubMed):**
         -   Summarize the key findings from the provided PubMed review articles.
-        -   Use bullet points for clarity.
-        -   Directly reference the articles by title and provide their URL for verification.
-        -   If no data was found, state: "No relevant review articles were found on PubMed for this specific query."
+        -   If no data was found, state: "No specific review articles were found on PubMed for this query."
 
     3.  **Relevant Clinical Trials (ClinicalTrials.gov):**
         -   List the actively recruiting clinical trials found.
-        -   For each trial, provide its Title, Status, and a direct URL.
-        -   This section shows the user what research is currently happening.
         -   If no data was found, state: "No actively recruiting clinical trials were found matching this query."
 
     4.  **Related Safety Information (OpenFDA):**
         -   Summarize the most common adverse events reported for treatments related to the concepts.
-        -   List any serious, ongoing recalls if found.
-        -   This provides crucial real-world context.
-        -   If no data was found, state: "No specific adverse event or recall data was found for this query."
+        -   If no data was found, state: "No specific adverse event data was found for this query."
 
     {vision_section}
 
     **CRITICAL RULES:**
-    -   Your entire response MUST begin with the mandatory disclaimer provided below.
+    -   Your entire response MUST begin with the mandatory disclaimer provided below. **Do not add your own preamble or a second disclaimer.**
     -   Adhere strictly to the provided data. DO NOT invent information or speculate beyond what is given.
-    -   Use clear, simple, and empathetic language. Avoid overly technical jargon.
+    -   **(V1.1 UPGRADE) If all data sources are empty,** state that specific research for the query was not found, and then provide a brief, general overview of the interpreted key concepts based on your general medical knowledge. This ensures you are always helpful.
+    -   Use clear, simple, and empathetic language.
     -   Format the output beautifully using Markdown (headings, bold, lists).
 
     ---
     **RAW DATA FOR SYNTHESIS:**
-
+    (You will base your report on the data below. If a section is empty, state that no information was found for that category.)
+    
     **[PubMed Data]**
     {pubmed_data if pubmed_data else "No data provided."}
 
@@ -100,6 +121,7 @@ def get_synthesis_prompt(user_query: str, concepts: list, pubmed_data: str, tria
 
 def get_drug_interaction_synthesis_prompt(drug_names: list[str], interaction_data: str, safety_data: str) -> str:
     """The master prompt for the Drug Interaction & Safety Analyzer."""
+    # This prompt remains unchanged from the previous version
     return f"""
     You are a specialist AI focused on drug information analysis. Your task is to interpret raw API data about drug interactions and safety and present it as a clear, cautious, and easy-to-understand report for a user. You do not give advice.