Update medicine.py

medicine.py

@@ -1,193 +1,189 @@
-import os
-import io
-from flask import Flask, request, jsonify
-from PIL import Image
-from dotenv import load_dotenv
-import google.generativeai as genai
-import json
-
-# --- INITIAL SETUP ---
-
-# Load environment variables from the .env file
-load_dotenv()
-
-# Configure the Gemini API with your key
-api_key = os.getenv("GOOGLE_API_KEY")
-if not api_key:
-    raise ValueError("GOOGLE_API_KEY not found. Please set it in your .env file.")
-genai.configure(api_key=api_key)
-
-# Initialize the Flask application
-app = Flask(__name__)
-
-# --- CONFIGURATION ---
-TEXT_FILES_DIR = "Text_Files"
-# Allowed file extensions for image uploads
-ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
-
-# Get a list of available knowledge base files
-try:
-    AVAILABLE_FILES = [f for f in os.listdir(TEXT_FILES_DIR) if f.endswith('.txt')]
-    if not AVAILABLE_FILES:
-        raise FileNotFoundError("No .txt files found in the 'Text_Files' directory.")
-except FileNotFoundError:
-    print("Warning: 'Text_Files' directory not found. The API will not have a knowledge base.")
-    AVAILABLE_FILES = []
-
-# --- HELPER FUNCTIONS ---
-
-def allowed_file(filename):
-    return '.' in filename and \
-           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
-
-def find_relevant_file(topic: str) -> str | None:
-    """
-    Uses Gemini to determine the most relevant file for a given topic.
-    This is more robust than simple keyword matching.
-    """
-    if not AVAILABLE_FILES:
-        return None
-        
-    try:
-        model = genai.GenerativeModel('gemini-2.5-flash-lite')
-        prompt = f"""
-        From the following list of files, which one is the most relevant for a query about "{topic}"?
-        Respond with only the single, most relevant filename dont include any other text.
-
-        File List:
-        {', '.join(AVAILABLE_FILES)}
-        """
-        response = model.generate_content(prompt)
-        # Clean up the response to get just the filename
-        filename = response.text.strip().replace("`", "")
-        
-        if filename in AVAILABLE_FILES:
-            print(f"Gemini identified relevant file: {filename} for topic: {topic}")
-            return filename
-        else:
-            print(f"Warning: Gemini suggested a file that doesn't exist: {filename}")
-            return None
-    except Exception as e:
-        print(f"Error in find_relevant_file: {e}")
-        return None
-
-def get_context_from_file(filename: str) -> str | None:
-    """Reads and returns the content of a specified text file."""
-    filepath = os.path.join(TEXT_FILES_DIR, filename)
-    try:
-        with open(filepath, 'r', encoding='utf-8') as f:
-            return f.read()
-    except FileNotFoundError:
-        return None
-
-# --- CORE API LOGIC ---
-
-@app.route('/api/query', methods=['POST'])
-def handle_query():
-    """
-    Main API endpoint to handle user queries.
-    Accepts form data with 'query' (required) and 'file' (optional image upload).
-    """
-    # 1. Get and validate the request data
-    form_data = request.form
-    if not form_data or 'query' not in form_data:
-        return jsonify({"error": "Missing 'query' in request"}), 400
-
-    user_query = form_data.get('query')
-    medicine_topic = None
-    
-    # 2. Handle File Upload (if provided)
-    if 'file' in request.files:
-        file = request.files['file']
-        if file.filename == '':
-            return jsonify({"error": "No selected file"}), 400
-            
-        if file and allowed_file(file.filename):
-            try:
-                print("Image file received. Identifying medicine from image...")
-                # Read the uploaded file directly
-                img = Image.open(file.stream)
-                
-                # Use the vision model to identify the medicine
-                vision_model = genai.GenerativeModel('gemini-2.5-flash')
-                prompt = ["""Identify the specific formula or Rx or medicine name or  primary subject from this image.""", img]
-                response = vision_model.generate_content(prompt)
-                
-                medicine_topic = response.text.strip()
-                print(f"Medicine identified from image: {medicine_topic}")
-
-            except Exception as e:
-                print(f"Error processing image: {e}")
-                return jsonify({"error": "Failed to process the uploaded image."}), 500
-        else:
-            return jsonify({"error": f"Invalid file type. Allowed types: {', '.join(ALLOWED_EXTENSIONS)}"}), 400
-    
-    # 3. Handle Text-Only Input (or use the topic identified from the image)
-    if not medicine_topic:
-        print("No image provided. Identifying topic from text query...")
-        try:
-            model = genai.GenerativeModel('gemini-2.5-flash')
-            prompt = f"""
-            From the user query '{user_query}', identify the main medicine or medical topic.
-            Respond with only the name of the topic or medicine (e.g., 'Ibuprofen', 'Antacids', 'Cough Suppressants').
-
-            """
-            response = model.generate_content(prompt)
-            medicine_topic = response.text.strip()
-            print(f"Topic identified from query: {medicine_topic}")
-        except Exception as e:
-            print(f"Error identifying topic from query: {e}")
-            return jsonify({"error": "Failed to understand the query topic."}), 500
-
-    # 4. Find the Relevant Knowledge Base File
-    relevant_filename = find_relevant_file(medicine_topic)
-    if not relevant_filename:
-        return jsonify({"error": f"Could not find a relevant information file for '{medicine_topic}'."}), 404
-
-    # 5. Get the Context from the File
-    context = get_context_from_file(relevant_filename)
-    if not context:
-        return jsonify({"error": "Failed to read the content of the relevant file."}), 500
-
-    # 6. Generate the Final Response Using the Context
-    try:
-        model = genai.GenerativeModel('gemini-2.5-flash-lite')
-        final_prompt = f"""
-        You are a helpful medical information assistant.
-        Your task is to answer the user's question based ONLY on the provided context from the guide.
-        Generate response in same language as user query.
-        If there have no information about any medicine then prepare response using given context and your knowlage base make sure there have satisfied answer.
-        if there have any relevent medicine of provided medicine in context then prepare answer using that context.
-        Answer should be in simple language and short not more than 200 words.
-        If the answer cannot be found in the provided context, then you have to prepare response using your knowlage base make sure there have satisfied answer.
-        ---important---
-        Dont tell user to i have no information about that medicine. inplace of that prepare answer using given context and your knowlage base make sure there have satisfied answer.
-        user is also provide the medicine name and description of the medicine.
-        name:{medicine_topic}
-        ---important---
-        
-        --- CONTEXT FROM THE GUIDE ---
-        {context}
-        --- END OF CONTEXT ---
-
-        USER'S QUESTION: {user_query}
-
-        YOUR ANSWER:
-        """
-        
-        final_response = model.generate_content(final_prompt)
-        
-        # 7. Return the final, context-aware response
-        return jsonify({
-            "response": final_response.text.strip(),
-            "identified_topic": medicine_topic,
-            "source_file": relevant_filename
-        })
-
-    except Exception as e:
-        print(f"Error generating final response: {e}")
-        return jsonify({"error": "An error occurred while generating the response."}), 500
-
-if __name__ == '__main__':
-    # Runs the Flask server
+import os
+import io
+from flask import Flask, request, jsonify
+from PIL import Image
+import google.generativeai as genai
+import json
+
+# --- INITIAL SETUP ---
+
+# Configure the Gemini API with your key
+api_key = os.getenv("GOOGLE_API_KEY")
+if not api_key:
+    raise ValueError("GOOGLE_API_KEY not found. Please set it in your .env file.")
+genai.configure(api_key=api_key)
+
+# Initialize the Flask application
+app = Flask(__name__)
+
+# --- CONFIGURATION ---
+TEXT_FILES_DIR = "Text_Files"
+# Allowed file extensions for image uploads
+ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
+
+# Get a list of available knowledge base files
+try:
+    AVAILABLE_FILES = [f for f in os.listdir(TEXT_FILES_DIR) if f.endswith('.txt')]
+    if not AVAILABLE_FILES:
+        raise FileNotFoundError("No .txt files found in the 'Text_Files' directory.")
+except FileNotFoundError:
+    print("Warning: 'Text_Files' directory not found. The API will not have a knowledge base.")
+    AVAILABLE_FILES = []
+
+# --- HELPER FUNCTIONS ---
+
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def find_relevant_file(topic: str) -> str | None:
+    """
+    Uses Gemini to determine the most relevant file for a given topic.
+    This is more robust than simple keyword matching.
+    """
+    if not AVAILABLE_FILES:
+        return None
+        
+    try:
+        model = genai.GenerativeModel('gemini-2.5-flash-lite')
+        prompt = f"""
+        From the following list of files, which one is the most relevant for a query about "{topic}"?
+        Respond with only the single, most relevant filename dont include any other text.
+
+        File List:
+        {', '.join(AVAILABLE_FILES)}
+        """
+        response = model.generate_content(prompt)
+        # Clean up the response to get just the filename
+        filename = response.text.strip().replace("`", "")
+        
+        if filename in AVAILABLE_FILES:
+            print(f"Gemini identified relevant file: {filename} for topic: {topic}")
+            return filename
+        else:
+            print(f"Warning: Gemini suggested a file that doesn't exist: {filename}")
+            return None
+    except Exception as e:
+        print(f"Error in find_relevant_file: {e}")
+        return None
+
+def get_context_from_file(filename: str) -> str | None:
+    """Reads and returns the content of a specified text file."""
+    filepath = os.path.join(TEXT_FILES_DIR, filename)
+    try:
+        with open(filepath, 'r', encoding='utf-8') as f:
+            return f.read()
+    except FileNotFoundError:
+        return None
+
+# --- CORE API LOGIC ---
+
+@app.route('/api/query', methods=['POST'])
+def handle_query():
+    """
+    Main API endpoint to handle user queries.
+    Accepts form data with 'query' (required) and 'file' (optional image upload).
+    """
+    # 1. Get and validate the request data
+    form_data = request.form
+    if not form_data or 'query' not in form_data:
+        return jsonify({"error": "Missing 'query' in request"}), 400
+
+    user_query = form_data.get('query')
+    medicine_topic = None
+    
+    # 2. Handle File Upload (if provided)
+    if 'file' in request.files:
+        file = request.files['file']
+        if file.filename == '':
+            return jsonify({"error": "No selected file"}), 400
+            
+        if file and allowed_file(file.filename):
+            try:
+                print("Image file received. Identifying medicine from image...")
+                # Read the uploaded file directly
+                img = Image.open(file.stream)
+                
+                # Use the vision model to identify the medicine
+                vision_model = genai.GenerativeModel('gemini-2.5-flash')
+                prompt = ["""Identify the specific formula or Rx or medicine name or  primary subject from this image.""", img]
+                response = vision_model.generate_content(prompt)
+                
+                medicine_topic = response.text.strip()
+                print(f"Medicine identified from image: {medicine_topic}")
+
+            except Exception as e:
+                print(f"Error processing image: {e}")
+                return jsonify({"error": "Failed to process the uploaded image."}), 500
+        else:
+            return jsonify({"error": f"Invalid file type. Allowed types: {', '.join(ALLOWED_EXTENSIONS)}"}), 400
+    
+    # 3. Handle Text-Only Input (or use the topic identified from the image)
+    if not medicine_topic:
+        print("No image provided. Identifying topic from text query...")
+        try:
+            model = genai.GenerativeModel('gemini-2.5-flash')
+            prompt = f"""
+            From the user query '{user_query}', identify the main medicine or medical topic.
+            Respond with only the name of the topic or medicine (e.g., 'Ibuprofen', 'Antacids', 'Cough Suppressants').
+
+            """
+            response = model.generate_content(prompt)
+            medicine_topic = response.text.strip()
+            print(f"Topic identified from query: {medicine_topic}")
+        except Exception as e:
+            print(f"Error identifying topic from query: {e}")
+            return jsonify({"error": "Failed to understand the query topic."}), 500
+
+    # 4. Find the Relevant Knowledge Base File
+    relevant_filename = find_relevant_file(medicine_topic)
+    if not relevant_filename:
+        return jsonify({"error": f"Could not find a relevant information file for '{medicine_topic}'."}), 404
+
+    # 5. Get the Context from the File
+    context = get_context_from_file(relevant_filename)
+    if not context:
+        return jsonify({"error": "Failed to read the content of the relevant file."}), 500
+
+    # 6. Generate the Final Response Using the Context
+    try:
+        model = genai.GenerativeModel('gemini-2.5-flash-lite')
+        final_prompt = f"""
+        You are a helpful medical information assistant.
+        Your task is to answer the user's question based ONLY on the provided context from the guide.
+        Generate response in same language as user query.
+        If there have no information about any medicine then prepare response using given context and your knowlage base make sure there have satisfied answer.
+        if there have any relevent medicine of provided medicine in context then prepare answer using that context.
+        Answer should be in simple language and short not more than 200 words.
+        If the answer cannot be found in the provided context, then you have to prepare response using your knowlage base make sure there have satisfied answer.
+        ---important---
+        Dont tell user to i have no information about that medicine. inplace of that prepare answer using given context and your knowlage base make sure there have satisfied answer.
+        user is also provide the medicine name and description of the medicine.
+        name:{medicine_topic}
+        ---important---
+        
+        --- CONTEXT FROM THE GUIDE ---
+        {context}
+        --- END OF CONTEXT ---
+
+        USER'S QUESTION: {user_query}
+
+        YOUR ANSWER:
+        """
+        
+        final_response = model.generate_content(final_prompt)
+        
+        # 7. Return the final, context-aware response
+        return jsonify({
+            "response": final_response.text.strip(),
+            "identified_topic": medicine_topic,
+            "source_file": relevant_filename
+        })
+
+    except Exception as e:
+        print(f"Error generating final response: {e}")
+        return jsonify({"error": "An error occurred while generating the response."}), 500
+
+if __name__ == '__main__':
+    # Runs the Flask server
     app.run(host='0.0.0.0', port=5002, debug=True)