app.py

import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel, PeftConfig

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
tokenizer = AutoTokenizer.from_pretrained("BeastGokul/Bio-Mistral-7B-finetuned")
base_model = AutoModelForCausalLM.from_pretrained("BioMistral/BioMistral-7B")
base_model.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(base_model, "BeastGokul/Bio-Mistral-7B-finetuned")

def generate_response(user_query):
    inputs = tokenizer(user_query, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=100)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Define the Gradio interface
with gr.Blocks() as demo:
    user_input = gr.Textbox(placeholder="Enter your biomedical query...", label="Your Query")
    response = gr.Textbox(label="Response", interactive=False)
    
    user_input.submit(fn=generate_response, inputs=user_input, outputs=response)

demo.launch()