app.py

# 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 AutoModelForCausalLM, AutoTokenizer

st.title("🤖 Improved Chatbot")

# Initialize model and tokenizer
@st.cache_resource
def load_model():
    model_name = "microsoft/DialoGPT-medium"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    return model, tokenizer

model, tokenizer = load_model()

# Initialize chat history
if "history" not in st.session_state:
    st.session_state.history = []

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

# User input
if prompt := st.chat_input("Type your message..."):
    # Add user message to history
    st.session_state.history.append({"role": "user", "content": prompt})
    
    # Prepare context for the model
    input_ids = tokenizer.encode(
        "\n".join([f"{msg['role']}: {msg['content']}" for msg in st.session_state.history[-5:]]) + "\nassistant:",
        return_tensors="pt"
    )
    
    # Generate response
    with st.spinner("Thinking..."):
        output = model.generate(
            input_ids,
            max_length=1000,
            pad_token_id=tokenizer.eos_token_id,
            no_repeat_ngram_size=3,
            do_sample=True,
            top_k=50,
            top_p=0.95,
            temperature=0.7
        )
        
        response = tokenizer.decode(output[0], skip_special_tokens=True).split("assistant:")[-1].strip()

    # Add assistant response to history
    st.session_state.history.append({"role": "assistant", "content": response})
    
    st.rerun()