# import streamlit as st
# from transformers import AutoModelForCausalLM, AutoTokenizer

# # Load the model and tokenizer
# @st.cache_resource
# def load_model_and_tokenizer():
#     model_name = "microsoft/DialoGPT-medium"  # Replace with your chosen model
#     tokenizer = AutoTokenizer.from_pretrained(model_name)
#     model = AutoModelForCausalLM.from_pretrained(model_name)
#     return tokenizer, model

# tokenizer, model = load_model_and_tokenizer()

# # Streamlit App
# st.title("General Chatbot")
# st.write("A chatbot powered by an open-source model from Hugging Face.")

# # Initialize the conversation
# if "conversation_history" not in st.session_state:
#     st.session_state["conversation_history"] = []

# # Input box for user query
# user_input = st.text_input("You:", placeholder="Ask me anything...", key="user_input")

# if st.button("Send") and user_input:
#     # Append user input to history
#     st.session_state["conversation_history"].append({"role": "user", "content": user_input})
    
#     # Tokenize and generate response
#     input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
#     chat_history_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
#     response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)

#     # Append model response to history
#     st.session_state["conversation_history"].append({"role": "assistant", "content": response})

# # Display the conversation
# for message in st.session_state["conversation_history"]:
#     if message["role"] == "user":
#         st.write(f"**You:** {message['content']}")
#     else:
#         st.write(f"**Bot:** {message['content']}")
import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM

st.title("🤖 Smart Chatbot")

@st.cache_resource
def load_model():
    model_name =  "facebook/blenderbot-3B"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    return pipeline("text2text-generation", model=model, tokenizer=tokenizer)

chatbot = load_model()

if "conversation" not in st.session_state:
    st.session_state.conversation = []

# Display chat history
for msg in st.session_state.conversation:
    with st.chat_message(msg["role"]):
        st.markdown(msg["content"])

if prompt := st.chat_input("Ask me anything"):
    # Add user message
    st.session_state.conversation.append({"role": "user", "content": prompt})
    
    # Format context
    context = "\n".join([f"{msg['role']}: {msg['content']}" for msg in st.session_state.conversation[-3:]])
    
    try:
        with st.spinner("Thinking..."):
            response = chatbot(
                context,
                max_length=200,
                temperature=0.9,
                top_k=60,
                top_p=0.9,
                num_beams=5,
                no_repeat_ngram_size=3
            )[0]['generated_text']
            
            # Clean response
            response = response.split("assistant:")[-1].strip()
            
            # Ensure meaningful response
            if not response or response.lower() in ["i don't know", "i'm not sure"]:
                response = "I need to learn more about that. Could you clarify?"
                
    except Exception as e:
        response = "Let me check my knowledge sources and get back to you on that."

    st.session_state.conversation.append({"role": "assistant", "content": response})
    st.rerun()