Configure Git LFS for PDFs and ignore FAISS index

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text

agents/academic_agent.py

@@ -403,15 +403,16 @@ class AcademicAgent:
 
         history_for_prompt = format_history_for_prompt(chat_history)
 
-        context_section = ""
-        if file_context:
-            context_section = f"---\nCONTEXT FROM UPLOADED FILE:\n{file_context}\n---"
+        context_section = f"---\nCONTEXT FROM KNOWLEDGE BASE:\n{file_context}\n---" if file_context else ""
+        # if file_context:
+        #     context_section = f"---\nCONTEXT FROM UPLOADED FILE:\n{file_context}\n---"
 
         prompt = f"""You are a helpful and knowledgeable AI pharmacy tutor for a student in India.
+        **CRITICAL INSTRUCTION FOR CITATIONS:** When you use information from the KNOWLEDGE BASE CONTEXT, you MUST cite the source at the end of the relevant sentence using the format `[Source: filename, Page: page_number]`.
 
 Your reasoning process must be:
 1. First, analyze the CONVERSATION HISTORY to understand the immediate context of the CURRENT QUESTION. This is especially important to understand what "this," "that," or "it" refers to.
-2. Once you understand the user's real question, check if the UPLOADED FILE context is relevant to the topic.
+2. Once you understand the user's real question, Check if the KNOWLEDGE BASE CONTEXT is relevant to the topic.
 3. Formulate your answer based on this reasoning, keeping an encouraging and professional tone.
 
 CONVERSATION HISTORY:

agents/drug_info_agent.py

@@ -56,7 +56,7 @@ class DrugInfoAgent:
                 }
         
         history_for_prompt = format_history_for_prompt(chat_history)
-        
+        context_section = f"---\nCONTEXT FROM KNOWLEDGE BASE:\n{file_context}\n---" if file_context else ""
         prompt = f"""You are a cautious AI Pharmacist Tutor providing educational information like ancient india's Chanakya advisor to Chandragupta Maurya.
 
 **CRITICAL SAFETY INSTRUCTION:** START EVERY RESPONSE with this disclaimer: "⚠️ **Disclaimer:** This information is for educational purposes ONLY and is not a substitute for professional medical advice."
@@ -68,6 +68,7 @@ Your reasoning process is:
 
 CONVERSATION HISTORY:
 {history_for_prompt}
+{context_section}
 CURRENT QUESTION:
 User: {query}
 

agents/mnemonic_agent.py

@@ -47,10 +47,12 @@ class MnemonicAgent:
 
         history_for_prompt = format_history_for_prompt(chat_history)
         topic = self._extract_topic(query)
-
+        context_section = f"---\nCONTEXT FROM KNOWLEDGE BASE:\n{file_context}\n---" if file_context else ""
         prompt = f"""You are "Mnemonic Master," a creative AI that creates memorable mnemonics for B.Pharmacy students.
-
+        **CRITICAL INSTRUCTION FOR CITATIONS:** When you use information from the KNOWLEDGE BASE CONTEXT, you MUST cite the source at the end of the relevant sentence using the format `[Source: filename, Page: page_number]`.
+get the topic from {topic}
 CONVERSATION HISTORY:
+{context_section}
 {history_for_prompt}
 CURRENT TASK:
 User: {query}

agents/quiz_agent.py

@@ -48,14 +48,16 @@ class QuizAgent:
 
         history_for_prompt = format_history_for_prompt(chat_history)
         topic = self._extract_topic(query)
+        context_section = f"---\nCONTEXT FROM KNOWLEDGE BASE:\n{file_context}\n---" if file_context else ""
         task_description = f"Generate a short quiz (3-5 questions) on the topic: **{topic.title()}**."
         if file_context:
             task_description += f"\nIf relevant, use text from the student's notes for context:\n---\n{file_context}\n---"
 
         prompt = f"""You are "Quiz Master," an AI that creates educational quizzes like Maryada Ramanna. Maryada Ramanna—he’s a legendary character from Indian (particularly South Indian) folklore, often associated with justice, integrity, and cleverness.
-
+**CRITICAL INSTRUCTION FOR CITATIONS:** When you use information from the KNOWLEDGE BASE CONTEXT, you MUST cite the source at the end of the relevant sentence using the format `[Source: filename, Page: page_number]`.
 CONVERSATION HISTORY:
 {history_for_prompt}
+{context_section}
 CURRENT TASK:
 {task_description}
 

agents/viva_agent.py

@@ -20,13 +20,13 @@ class VivaAgent:
         
         context_source = ""
         if file_context:
-            context_source = f"The question should be based on the following text from an uploaded document:\n---\n{file_context}\n---"
+            context_source =f"---\nCONTEXT FROM KNOWLEDGE BASE:\n{file_context}\n---" if file_context else ""
         else:
             context_source = f"The question should be about the B.Pharmacy topic: **{topic}**."
 
         prompt = f"""
 You are a "Viva Coach," a professional and encouraging AI examiner for B.Pharmacy students like Gurus from ancient India.
-
+**CRITICAL INSTRUCTION FOR CITATIONS:** When you use information from the KNOWLEDGE BASE CONTEXT, you MUST cite the source at the end of the relevant sentence using the format `[Source: filename, Page: page_number]`.
 **Your Task:**
 Generate a SINGLE, insightful, open-ended viva question.
 

app.py

@@ -646,7 +646,7 @@ app.config['SECRET_KEY'] = os.getenv('FLASK_SECRET_KEY', 'a-very-secret-key-for-
 model = None
 vector_store = None
 try:
-    GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')
+    GEMINI_API_KEY = os.getenv('GOOGLE_API_KEY')
     if GEMINI_API_KEY:
         genai.configure(api_key=GEMINI_API_KEY)
         model = genai.GenerativeModel('gemini-1.5-flash')
@@ -674,9 +674,21 @@ class MyPharmaAI:
         # 1. Search the permanent knowledge base for context.
         file_context = ""
         if self.vector_store:
-            relevant_docs = self.vector_store.similarity_search(query, k=3) # Get top 3 results
+            relevant_docs = self.vector_store.similarity_search(query, k=4) # Get top 4 results
             file_context = "\n".join(doc.page_content for doc in relevant_docs)
-        
+            context_with_sources = []
+            for doc in relevant_docs:
+                # Clean up the source path to just the filename
+                source_filename = os.path.basename(doc.metadata.get('source', 'Unknown Source'))
+                # Page numbers from PyPDF are 0-indexed, so we add 1 for readability
+                page_number = doc.metadata.get('page', -1) + 1
+                
+                context_with_sources.append(
+                    f"[Source: {source_filename}, Page: {page_number}]\n{doc.page_content}"
+                )
+            
+            file_context = "\n\n".join(context_with_sources)
+
         # 2. Pass the retrieved context to the multi-agent router system.
         return self.router.route_query(query, file_context, viva_state, chat_history)
 
@@ -690,6 +702,7 @@ def index():
 
 @app.route('/chat', methods=['POST'])
 def chat():
+    # This function is now the final, stable version.
     try:
         data = request.get_json()
         query = data.get('query', '').strip()
@@ -699,24 +712,44 @@ def chat():
         chat_history = session.get('chat_history', [])
         viva_state = session.get('viva_state', None)
         
-        # Call the main orchestrator
-        result = pharma_ai.process_query(query, viva_state, chat_history)
-
-        if result.get('success'):
+        # Get the result dictionary from the agent system
+        agent_result = pharma_ai.process_query(query, viva_state, chat_history)
+
+        # --- THIS IS THE FIX ---
+        # We now build the final JSON response to match what the JavaScript expects.
+        if "error" in agent_result.get('status', ''):
+            final_response = {
+                'success': False,
+                'error': agent_result.get('message', 'An unknown error occurred.'),
+                'agent_used': agent_result.get('agent_used', 'error')
+            }
+        else:
+            final_response = {
+                'success': True,
+                'message': agent_result.get('message', 'Sorry, I could not generate a response.'),
+                'agent_used': agent_result.get('agent_used', 'academic')
+            }
+        # --- END OF FIX ---
+
+        # Update chat history if the call was successful
+        if final_response.get('success'):
             chat_history.append({'role': 'user', 'parts': [query]})
-            chat_history.append({'role': 'model', 'parts': [result.get('message', '')]})
+            chat_history.append({'role': 'model', 'parts': [final_response.get('message', '')]})
             session['chat_history'] = chat_history[-10:]
         
-        if 'viva_state' in result:
-            session['viva_state'] = result.get('viva_state')
+        # Handle Viva state if present (no changes needed here)
+        if 'viva_state' in agent_result:
+            session['viva_state'] = agent_result.get('viva_state')
         
-        return jsonify(result)
+        return jsonify(final_response)
         
     except Exception as e:
-        print(f"Error in /chat endpoint: {e}")
-        return jsonify({'success': False, 'message': f'Server error: {e}', 'agent_used': 'error'}), 500
+        print(f"Critical Error in /chat endpoint: {e}")
+        return jsonify({'success': False, 'error': f'A critical server error occurred: {e}', 'agent_used': 'error'}), 500
+
 
 # --- Main Execution ---
 if __name__ == '__main__':
+    # app.run(host='127.0.0.1', port=5000, debug=True)
     port = int(os.environ.get('PORT', 7860))
     app.run(host='0.0.0.0', port=port)

faiss_index/index.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40247f25d66ec00e349df03dc9f81e3fddb9ef95c5cc184104268b9f8f0d4ce3
+size 653990

knowledge_base/PharmaLite.in Pharmaceutical Biotechnology (Thakur).pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf5f6857a39bf2c469c7fbd3d5c0de3ce5db3ba633c2c9dddddd20e8bf15e14e
+size 9284177

templates/index.html

@@ -111,7 +111,7 @@
             flex-direction: column;
             overflow: hidden;
             border: 3px solid var(--gold);
-            padding: 20px;
+            
         }
 
         .chat-header {
@@ -232,28 +232,16 @@
             cursor: not-allowed;
         }
 
-        /* Loading Animation */
+        /* --- CSS FOR THE LOADING INDICATOR --- */
         .loading {
-            display: none;
+            display: none; /* Hidden by default */
             text-align: center;
+            margin-top: 15px;
             color: var(--maroon);
-            font-style: italic;
+            font-weight: bold;
         }
-
         .loading.show {
-            display: block;
-        }
-
-        .loading::after {
-            content: "...";
-            animation: dots 1.5s infinite;
-        }
-
-        @keyframes dots {
-            0%, 20% { content: "..."; }
-            40% { content: "....."; }
-            60% { content: "......."; }
-            80%, 100% { content: "..."; }
+            display: block; /* Visible when .show class is added */
         }
 
         /* Quick Actions */
@@ -372,7 +360,7 @@
                     <button class="quick-btn" onclick="populateInput('Make a quiz on ')">❓ Create Quiz</button>
                     <button class="quick-btn" onclick="populateInput('Mnemonic for ')">🧠 Mnemonics</button>
                 </div>
-            </div>
+            
 
                 
                 <div class="input-container">
@@ -382,10 +370,12 @@
                            onkeypress="handleKeyPress(event)">
                     <button id="sendBtn" onclick="sendMessage()">Send 📨</button>
                 </div>
-                
+
                 <div class="loading" id="loading">
-                    🔄 Processing your query
+                    🔄 Processing your query...
                 </div>
+                
+            
             </div>
         </div>
     </div>
@@ -408,7 +398,7 @@
                     body: JSON.stringify({ query: message })
                 });
                 const data = await response.json();
-                showLoading(false);
+                showLoading(false); // Remove loading indicator *before* adding new message
                 if (data.success) {
                     addMessage(data.message, 'bot', data.agent_used);
                 } else {
@@ -424,7 +414,17 @@
             const messagesContainer = document.getElementById('chatMessages');
             const messageDiv = document.createElement('div');
             messageDiv.className = `message ${sender}`;
-            const agentIcons = { 'academic': '📚 Academic Agent', 'drug_info': '💊 Drug Info Agent', 'quiz_generation': '❓ Quiz Master' };
+            
+            // This dictionary now includes all agent types
+            const agentIcons = { 
+                'academic': '📚 Academic Agent', 
+                'drug_info': '💊 Drug Info Agent', 
+                'quiz_generation': '❓ Quiz Master',
+                'mnemonic_creation': '🧠 Memory Master',
+                'viva_practice': '🗣️ Viva Coach',
+                'error': '⚠️ System'
+            };
+
             const agentBadge = sender === 'bot' ? `<div class="agent-badge">${agentIcons[agentType] || '🤖 AI Assistant'}</div>` : '';
             const formattedText = marked.parse(text || 'Sorry, I received an empty response.');
             messageDiv.innerHTML = `<div class="message-bubble">${agentBadge}${formattedText}</div>`;
@@ -432,22 +432,20 @@
             messagesContainer.scrollTop = messagesContainer.scrollHeight;
         }
 
-        function showLoading(show) {
+        // This is the correct "typing indicator" loading function
+         function showLoading(show) {
+            isProcessing = show;
             document.getElementById('sendBtn').disabled = show;
-            const existingLoading = document.getElementById('loading-indicator');
-            if (existingLoading) existingLoading.remove();
+            const loadingElement = document.getElementById('loading');
             
             if (show) {
-                const messagesContainer = document.getElementById('chatMessages');
-                const loadingDiv = document.createElement('div');
-                loadingDiv.className = 'message bot';
-                loadingDiv.id = 'loading-indicator';
-                loadingDiv.innerHTML = `<div class="message-bubble"><div class="typing-indicator"><span></span><span></span><span></span></div></div>`;
-                messagesContainer.appendChild(loadingDiv);
-                messagesContainer.scrollTop = messagesContainer.scrollHeight;
+                loadingElement.classList.add('show');
+            } else {
+                loadingElement.classList.remove('show');
             }
         }
 
+
         function handleKeyPress(event) {
             if (event.key === 'Enter' && !event.shiftKey) {
                 event.preventDefault();

utils/helpers.py

@@ -12,7 +12,9 @@ from langchain_community.document_loaders import PyPDFDirectoryLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
 from langchain_community.vectorstores import FAISS
-
+from dotenv import load_dotenv
+load_dotenv()
+GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')
 def create_vector_store():
     """
     Checks if a vector store index exists. If not, it creates one from
@@ -40,7 +42,7 @@ def create_vector_store():
     docs = text_splitter.split_documents(documents)
     
     print(f"--- Creating embeddings and vector store. This may take a moment... ---")
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GEMINI_API_KEY)
     db = FAISS.from_documents(docs, embeddings)
     db.save_local(persist_directory)
     print("--- Knowledge base created successfully. ---")