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import streamlit as st
pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
st.set_page_config(page_title="Hugging Face Chatbot", layout="centered")
st.title("Hugging Face Chatbot")
@st.cache_resource
def load_model():
# Load tokenizer and model from Hugging Face
tokenizer = AutoTokenizer.from_pretrained("Grandediw/lora_model_finetuned", use_fast=True)
model = AutoModelForCausalLM.from_pretrained("Grandediw/lora_model_finetuned", device_map="auto", trust_remote_code=True)
chat_pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_length=512,
temperature=0.7,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id
)
return chat_pipeline
chat_pipeline = load_model()
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# User input
if prompt := st.chat_input("Ask me anything:"):
# Display user message and store it
st.chat_message("user").markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
# Generate response
with st.spinner("Thinking..."):
# Using the pipeline to generate a response
response = chat_pipeline(prompt)[0]["generated_text"]
# The model may return the prompt + response concatenated, so you might need
# to extract only the response part. This depends on how the model is trained.
# Here we assume the model returns the full text and we just remove the original prompt from it:
if response.startswith(prompt):
response = response[len(prompt):].strip()
# Display and store assistant response
with st.chat_message("assistant"):
st.markdown(response)
st.session_state.messages.append({"role": "assistant", "content": response})
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