Fix: chat_history addition

agents/academic_agent.py

@@ -290,13 +290,63 @@ class AcademicAgent:
             with open(knowledge_file, 'r') as f: return json.load(f)
         except: return {}
 
-    def process_with_ai(self, query, file_context=""):
-        """Use Gemini AI to provide comprehensive, context-aware answers."""
-        if not self.model: return None
+    # def process_with_ai(self, query, file_context=""):
+    #     """Use Gemini AI to provide comprehensive, context-aware answers."""
+    #     if not self.model: return None
+    #     try:
+    #         # (Content of this method is unchanged)
+    #         context_section = f"UPLOADED FILE CONTEXT:\n{file_context}" if file_context else ""
+    #         prompt = f"You are an expert pharmacy educator... STUDENT QUESTION: {query}\n{context_section} ..."
+    #         response = self.model.generate_content(prompt)
+    #         return response.text
+    #     except Exception as e:
+    #         print(f"Gemini API error in Academic Agent: {e}")
+    #         return None
+    # In agents/academic_agent.py -> class AcademicAgent
+
+    def process_with_ai(self, query, file_context="", chat_history=None):
+        """Use Gemini AI with conversation history and file context."""
+        if not self.model: 
+            return None
+
+        # --- NEW HISTORY AND PROMPT LOGIC ---
+
+        # Format the past conversation for the prompt
+        history_for_prompt = ""
+        if chat_history:
+            for turn in chat_history:
+                # Ensure 'parts' is a list and not empty before accessing
+                if turn.get('parts') and isinstance(turn.get('parts'), list):
+                    role = "User" if turn['role'] == 'user' else "AI"
+                    history_for_prompt += f"{role}: {turn['parts'][0]}\n"
+
+        # Format the file context
+        context_section = ""
+        if file_context:
+            context_section = f"""
+    ---
+    CONTEXT FROM UPLOADED FILE:
+    {file_context}
+    ---
+    Use the context from the uploaded file above to answer the user's current question if it is relevant.
+    """
+
+        # The new prompt structure
+        prompt = f"""You are a helpful and knowledgeable AI pharmacy tutor for a student in India.
+
+    CONVERSATION HISTORY:
+    {history_for_prompt}
+    {context_section}
+    CURRENT QUESTION:
+    User: {query}
+
+    Please provide a helpful and accurate answer to the user's CURRENT QUESTION.
+    - If the question is a follow-up, use the CONVERSATION HISTORY to understand the context.
+    - If the question relates to the UPLOADED FILE, prioritize information from that context.
+    - Keep the tone encouraging and professional.
+    """
         try:
-            # (Content of this method is unchanged)
-            context_section = f"UPLOADED FILE CONTEXT:\n{file_context}" if file_context else ""
-            prompt = f"You are an expert pharmacy educator... STUDENT QUESTION: {query}\n{context_section} ..."
+            # This is a more direct and robust way to send the complete context
             response = self.model.generate_content(prompt)
             return response.text
         except Exception as e:
@@ -335,7 +385,7 @@ class AcademicAgent:
     
     # --- THIS IS THE ONLY METHOD THAT CHANGES ---
 
-    def process_query(self, query: str, file_context: str = ""):
+    def process_query(self, query: str, file_context: str = "",chat_history: list = None):
         """
         Main method to process academic queries.
         It now returns a standardized dictionary.
@@ -344,7 +394,7 @@ class AcademicAgent:
         try:
             # Priority 1: Use AI for a comprehensive response if available.
             if self.model:
-                ai_response = self.process_with_ai(query, file_context)
+                ai_response = self.process_with_ai(query, file_context,chat_history)
                 if ai_response:
                     response_message = f"🤖 **AI-Powered Response**\n\n{ai_response}"
             

agents/drug_info_agent.py

@@ -32,7 +32,7 @@ class DrugInfoAgent:
         # Clean up any extra whitespace
         return drug_name.strip().title() # Capitalize for better recognition
 
-    def process_query(self, query: str, file_context: str = ""):
+    def process_query(self, query: str, file_context: str = "", chat_history: list = None):
         """
         Processes a query to retrieve information about a specific drug.
 
@@ -84,7 +84,7 @@ Use clear headings (like "🔬 Mechanism of Action") and bullet points for reada
 
         try:
             # Generate content using the AI model
-            ai_response = self.model.generate_content(prompt)
+            ai_response = self.model.generate_content(prompt, chat_history)
             return {
                 'message': ai_response.text,
                 'agent_used': 'drug_info',

agents/mnemonic_agent.py

@@ -31,7 +31,7 @@ class MnemonicAgent:
         # Clean up any extra whitespace
         return topic.strip()
 
-    def process_query(self, query: str, file_context: str = ""):
+    def process_query(self, query: str, file_context: str = "",chat_history: list = None):
         """
         Processes a query to generate a mnemonic.
 
@@ -98,7 +98,7 @@ Keep up the great work! You've got this! 💪
 
         try:
             # Generate content using the AI model
-            ai_response = self.model.generate_content(prompt)
+            ai_response = self.model.generate_content(prompt,chat_history)
             return {
                 'message': ai_response.text,
                 'agent_used': 'mnemonic_creation',

agents/quiz_agent.py

@@ -32,7 +32,7 @@ class QuizAgent:
         # Clean up any extra whitespace
         return topic.strip()
 
-    def process_query(self, query: str, file_context: str = ""):
+    def process_query(self, query: str, file_context: str = "",chat_history: list = None):
         """
         Processes a query to generate a quiz. The agent prioritizes file_context if provided.
 
@@ -103,7 +103,7 @@ Let's test your knowledge! Good luck! 🌟
 
         try:
             # Generate content using the AI model
-            ai_response = self.model.generate_content(prompt)
+            ai_response = self.model.generate_content(prompt, chat_history)
             return {
                 'message': ai_response.text,
                 'agent_used': 'quiz_generation',

agents/router_agent.py

@@ -295,7 +295,7 @@ class RouterAgent:
         self.viva_agent = VivaAgent(gemini_model)
         self.default_agent = self.academic_agent
 
-    def route_query(self, query: str, file_context: str = "", viva_state: dict = None):
+    def route_query(self, query: str, file_context: str = "", viva_state: dict = None, chat_history: list = None):
         """
         Determines the user's intent and routes the query to the correct agent.
         
@@ -336,4 +336,4 @@ class RouterAgent:
 
         # 5. Default to Academic Agent
         # If no other intent is detected, it's likely a general academic question.
-        return self.academic_agent.process_query(query, file_context)
+        return self.academic_agent.process_query(query, file_context,chat_history)

app.py

@@ -443,7 +443,7 @@ class MyPharmaAI:
         self.quotes = load_quotes()
         self.file_processor = FileProcessor()
 
-    def process_query(self, query, user_name="Student", viva_state=None, uploaded_files=None):
+    def process_query(self, query, user_name="Student", viva_state=None, uploaded_files=None,chat_history=None):
         """Routes a user's query to the appropriate agent, handling context."""
         try:
             file_context = ""
@@ -454,7 +454,7 @@ class MyPharmaAI:
             print(f"--- DEBUG: Here is the text extracted from the file ---\n{file_context[:1000]}\n--- END DEBUG ---")
             
             # Pass all context to the router
-            response_data = self.router.route_query(query, file_context, viva_state)
+            response_data = self.router.route_query(query, file_context, viva_state, chat_history)
             
             return {
                 'success': True,
@@ -512,13 +512,28 @@ def chat():
         query = data.get('query', '').strip()
         if not query:
             return jsonify({'success': False, 'error': 'Empty query'}), 400
+        
+         # --- HISTORY MANAGEMENT START ---
+
+        # Get the conversation history from the session (or start a new one)
+        chat_history = session.get('chat_history', [])
 
         # Get current viva state from session for the Viva Agent
         viva_state = session.get('viva_state', None)
         uploaded_files = session.get('uploaded_files', None)
 
         # Process the query through the main orchestrator
-        result = pharma_ai.process_query(query, viva_state=viva_state, uploaded_files=uploaded_files)
+        result = pharma_ai.process_query(query, viva_state=viva_state, uploaded_files=uploaded_files,chathistory=chat_history)
+        # If the query was successful, update the history
+        if result.get('success'):
+            # Add the user's query and the AI's message to the history
+            chat_history.append({'role': 'user', 'parts': [query]})
+            chat_history.append({'role': 'model', 'parts': [result.get('message', '')]})
+
+            # Keep the history from getting too long (e.g., last 10 exchanges)
+            session['chat_history'] = chat_history[-20:]
+
+        # --- HISTORY MANAGEMENT END ---
 
         # If the Viva agent returns an updated state, save it to the session
         if 'viva_state' in result: