Update app.py

app.py

@@ -325,5 +325,116 @@ with gr.Blocks() as app:
                             book, book_reason, character, character_reason, blog_text,
                             transcript_data, learning_output],
                      outputs=output_summary)
+# Add these new imports at the top
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from openai import OpenAI  # Make sure to install with pip install openai
+
+# ========== AI CHATBOT SETUP ==========
+# Initialize DeepSeek model for information retrieval
+deepseek_model_name = "deepseek-ai/deepseek-llm-7b"
+deepseek_tokenizer = AutoTokenizer.from_pretrained(deepseek_model_name)
+deepseek_model = AutoModelForCausalLM.from_pretrained(deepseek_model_name, torch_dtype=torch.float16)
+
+# Initialize ChatGPT (you'll need an OpenAI API key)
+client = OpenAI(api_key="your-openai-api-key")  # Replace with your actual API key
+
+def retrieve_information_with_deepseek(query, student_profile):
+    # Prepare context from student profile
+    profile_context = f"""
+    Student Profile:
+    Name: {student_profile.get('name', 'N/A')}
+    Age: {student_profile.get('age', 'N/A')}
+    Grade Level: {student_profile.get('transcript', {}).get('grade_level', 'N/A')}
+    GPA: {student_profile.get('transcript', {}).get('gpa', {}).get('unweighted', 'N/A')} (Unweighted)
+    Learning Style: {student_profile.get('learning_style', 'N/A')}
+    Interests: {student_profile.get('interests', 'N/A')}
+    """
+    
+    # Format the prompt for DeepSeek
+    prompt = f"""
+    [CONTEXT]
+    {profile_context}
+    
+    [QUERY]
+    {query}
+    
+    Based on the student profile and educational context, provide the most accurate and relevant information to answer the query.
+    """
+    
+    # Generate response with DeepSeek
+    inputs = deepseek_tokenizer(prompt, return_tensors="pt")
+    outputs = deepseek_model.generate(**inputs, max_new_tokens=200)
+    accurate_response = deepseek_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return accurate_response
+
+def generate_chat_response_with_chatgpt(message, history, student_profile):
+    # First retrieve accurate information with DeepSeek
+    accurate_info = retrieve_information_with_deepseek(message, student_profile)
+    
+    # Prepare conversation history
+    chat_history = "\n".join([f"User: {h[0]}\nAI: {h[1]}" for h in history])
+    
+    # Create ChatGPT prompt
+    prompt = f"""
+    You are a personalized teaching assistant. Use the following accurate information to craft a natural, helpful response:
+    
+    [ACCURATE INFORMATION]
+    {accurate_info}
+    
+    [CONVERSATION HISTORY]
+    {chat_history}
+    
+    [NEW MESSAGE]
+    User: {message}
+    
+    Respond in a friendly, conversational tone while ensuring all factual information remains accurate.
+    """
+    
+    # Get response from ChatGPT
+    response = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a helpful teaching assistant."},
+            {"role": "user", "content": prompt}
+        ],
+        temperature=0.7
+    )
+    
+    return response.choices[0].message.content
+
+# ========== UPDATE GRADIO INTERFACE ==========
+# Add this new tab to your existing with gr.Blocks() as app:
+with gr.Blocks() as app:
+    # ... (keep all your existing tabs) ...
+    
+    with gr.Tab("🤖 AI Teaching Assistant"):
+        gr.Markdown("## Your Personalized Learning Assistant")
+        gr.Markdown("Chat with your AI assistant for personalized learning support")
+        
+        chatbot = gr.ChatInterface(
+            fn=lambda message, history: generate_chat_response_with_chatgpt(
+                message, 
+                history, 
+                student_profile=gr.State()
+            ),
+            examples=[
+                "How should I study for my math test?",
+                "Can you explain this concept to me in a way that matches my learning style?",
+                "What are some good study strategies based on my GPA?",
+                "How can I improve my grades in science?"
+            ],
+            additional_inputs=[transcript_data, learning_output]
+        )
+        
+        # This connects the profile data to the chatbot
+        @app.load
+        def load_profile():
+            profile_path = os.path.join("student_profiles", "student_profile.json")
+            if os.path.exists(profile_path):
+                with open(profile_path, "r") as f:
+                    return json.load(f)
+            return {}
 
 app.launch()