Masha_Chatbot / app.py
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Update app.py
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import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
# Configure device (CPU/GPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the model and tokenizer
@st.cache_resource
def load_model():
model = GPT2LMHeadModel.from_pretrained("microsoft/DialoGPT-medium").to(device)
tokenizer = GPT2Tokenizer.from_pretrained("microsoft/DialoGPT-medium")
return model, tokenizer
model, tokenizer = load_model()
# Function to generate chatbot response
def get_response(conversation_history, user_input):
input_text = " ".join(conversation_history) + " " + user_input + tokenizer.eos_token
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model.generate(
inputs,
max_length=75,
num_return_sequences=1,
no_repeat_ngram_size=2,
top_k=40,
top_p=0.8,
temperature=0.7
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Streamlit app interface
st.title("Chatbot with Hugging Face Model")
st.write("### Chat with the chatbot powered by DialoGPT. Type your message below!")
# Initialize conversation history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat history
for message in st.session_state.messages:
st.markdown(f"{message['role']}: {message['content']}")
# User input
user_input = st.text_input("You: ", key="user_input")
# Generate response if user submits a message
if user_input:
# Add user input to conversation history
st.session_state.messages.append({"role": "User", "content": user_input})
# Prepare context for the chatbot
history = [msg["content"] for msg in st.session_state.messages[-3:] if msg["role"] == "User"]
# Generate chatbot response
response = get_response(history, user_input)
st.session_state.messages.append({"role": "Chatbot", "content": response})
# Clear input box for new input
st.text_input("You: ", key="user_input", value="")