app.py

import gradio
import torch
from transformers import AutoModelWithLMHead, AutoTokenizer

# Load model directly


loaded_tokenizer = AutoTokenizer.from_pretrained("raghavdw/finedtuned_gpt2_medQA_model")
loaded_model = AutoModelWithLMHead.from_pretrained("raghavdw/finedtuned_gpt2_medQA_model")

# Function for response generation

def generate_query_response(prompt, max_length=200):

    model = loaded_model
    tokenizer = loaded_tokenizer

    input_ids = tokenizer.encode(prompt, return_tensors="pt")

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    input_ids = input_ids.to(device)
    attention_mask = torch.ones_like(input_ids)
    pad_token_id = tokenizer.eos_token_id

    output = model.generate(input_ids, 
                            max_length=max_length, 
                            num_return_sequences=1, 
                            attention_mask=attention_mask,
                            pad_token_id=pad_token_id)
    
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

# Gradio elements

# Input from user
in_prompt = gradio.Textbox(label="Enter your prompt")

# Output response
in_max_length = 200


# Output response
out_response = gradio.Textbox(label="Generated Response")

# Gradio
iface = gradio.Interface(fn=generate_query_response,
                         inputs=[in_prompt],
                         outputs=out_response,
                         title =  "Medical Summary",
                         description = "using fine-tune medQA gpt-2 model")


# YOUR CODE HERE to launch the interface
iface.launch(share = True)