app.py

import gradio as gr
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer

# Assuming you have loaded your model and tokenizer
# Replace this with your actual model and tokenizer


# Define the model function for Gradio
def generate_summary(input_text):
    # # Tokenize the input text
    # inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)

    # # Generate summary using the model
    # outputs = model.generate(**inputs)

    # # Decode the generated summary
    # summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
    # return summary


    # Create a text generation pipeline
    # text_generation_pipeline = pipeline("Falconsai/medical_summarization", model=model, tokenizer=tokenizer)

    tokenizer = AutoTokenizer.from_pretrained("Shariar00/medical_summarization_finetune_medical_qa")
    model = AutoModelForSeq2SeqLM.from_pretrained("Shariar00/medical_summarization_finetune_medical_qa")
    text_generation_pipeline = pipeline("summarization", model=model, tokenizer=tokenizer)

    # Generate text using the pipeline
    prompt = "Hello, I am feeling very pain on my leg, I can not walk properly. I have some knee pain also. what can I do now?"
    output = text_generation_pipeline(input_text, max_length=512, num_return_sequences=1)

    # Print the generated text
    generated_text = output[0]


    return generated_text

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_summary,
    inputs="text",
    outputs="text",
     # Set to True for live updates without restarting the server
)

# Launch the Gradio interface
iface.launch()