Update app.py

app.py

@@ -36,28 +36,7 @@ class SummaryRequest(BaseModel):
     chat_history: list  # List of messages
 
 
-# Load Local LLM (Mistral or Llama)
-model_name = "mistralai/Mistral-7B-Instruct-v0.3"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
 
-def generate_local_emotional_response(user_input, questions):
-    """Generate emotional responses locally using LLaMA/Mistral."""
-    prompt = f"User: {user_input}\n\nBased on this, respond in an empathetic way before asking each question:\n1. {questions[0]}\n2. {questions[1]}\n3. {questions[2]}"
-    inputs = tokenizer(prompt, return_tensors="pt")
-    output = model.generate(**inputs, max_length=200)
-    return tokenizer.decode(output[0], skip_special_tokens=True).split("\n")
-
-@app.post("/get_questions")
-def get_recommended_questions(request: ChatRequest):
-    input_embedding = embedding_model.encode([request.message], convert_to_numpy=True)
-    distances, indices = question_index.search(input_embedding, 3)
-    retrieved_questions = [questions_df["Questions"].iloc[i] for i in indices[0]]
-
-    # Generate dynamic emotional responses locally
-    enhanced_responses = generate_local_emotional_response(request.message, retrieved_questions)
-
-    return {"questions": enhanced_responses}
 
 @app.post("/summarize_chat")
 def summarize_chat(request: SummaryRequest):