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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()
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