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# 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
# Load the model and tokenizer
@st.cache_resource
def load_model_and_tokenizer():
model_name = "microsoft/DialoGPT-medium" # You can replace with any Hugging Face conversational model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
return tokenizer, model
tokenizer, model = load_model_and_tokenizer()
# Streamlit App Title
st.title("General Chatbot")
st.markdown("This chatbot is powered by an open-source model from Hugging Face. Feel free to ask me anything!")
# Initialize the session state for conversation history
if "chat_history" not in st.session_state:
st.session_state["chat_history"] = ""
# User Input Section
user_input = st.text_input("You:", placeholder="Type your message here...", key="user_input")
if st.button("Send") and user_input:
# Add user input to the conversation history
st.session_state["chat_history"] += f"User: {user_input}\n"
# Tokenize the input with conversation history
input_ids = tokenizer.encode(st.session_state["chat_history"], return_tensors="pt")
# Generate a response
chat_history_ids = model.generate(
input_ids,
max_length=1500, # Allow long responses
min_length=200, # Ensure responses are not too short
temperature=1.0, # Adjust for creativity
top_p=0.9, # Nucleus sampling for focused responses
repetition_penalty=1.2, # Penalize repeated phrases
pad_token_id=tokenizer.eos_token_id
)
# Decode the model's response
response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
# Add the response to the conversation history
st.session_state["chat_history"] += f"Bot: {response}\n"
# Display the conversation
st.markdown(f"**You:** {user_input}")
st.markdown(f"**Bot:** {response}")
# Display Full Conversation History
st.divider()
st.subheader("Conversation History:")
st.text(st.session_state["chat_history"])
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