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import streamlit as st
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
# Define the Streamlit app
st.title("Mistral Model Integration")
# Create a text input for the user to enter their prompt
instruction = st.text_area("Enter your prompt:")
# Function to interact with Mistral Model
def mistral_model(prompt, token_limit):
# Your model loading and inference code here (from the code you provided)
# ...
return responses
# Check if the user entered a prompt
if instruction:
# Add a slider for selecting the token limit
token_limit = st.slider("Select token limit", min_value=10, max_value=500, value=250)
# Create a button to trigger model inference
if st.button("Generate Response"):
responses = mistral_model(instruction, token_limit)
st.write("Generated Responses:")
for response in responses:
st.write(response)
# # Finally, run the Streamlit app
# if __name__ == "__main__":
# st.run()
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