app.py

import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load the base T5 model and tokenizer
model = T5ForConditionalGeneration.from_pretrained('t5-small')
tokenizer = T5Tokenizer.from_pretrained('t5-small')

def generate_clinical_report(input_text):
    """
    Generate a clinical report from the input text using the fine-tuned T5 model.
    """
    # Prepare input text
    input_ids = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)
    
    # Generate report
    outputs = model.generate(
        input_ids,
        max_length=256,
        num_beams=4,
        no_repeat_ngram_size=3,
        length_penalty=2.0,
        early_stopping=True,
        bad_words_ids=[[tokenizer.encode(word, add_special_tokens=False)[0]] 
                      for word in ['http', 'www', '.com', '.org']]
    )
    
    # Decode and return the generated report
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Create Gradio interface
demo = gr.Interface(
    fn=generate_clinical_report,
    inputs=gr.Textbox(
        lines=8,
        placeholder="Enter clinical notes here...",
        label="Clinical Notes"
    ),
    outputs=gr.Textbox(
        lines=8,
        label="Generated Clinical Report"
    ),
    title="Clinical Report Generator",
    description="Generate professional clinical reports from clinical notes using a fine-tuned T5 model.",
    examples=[
        ["Patient presented with severe abdominal pain in the lower right quadrant. Temperature 38.5°C, BP 130/85."],
        ["Follow-up visit for diabetes management. Blood sugar levels have been stable with current medication regimen."]
    ]
)

# Launch the app
if __name__ == "__main__":
    demo.launch()