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 with queue
demo = gr.Interface(
    fn=generate_clinical_report,
    inputs=[
        gr.Textbox(
            lines=8,
            placeholder="Enter clinical notes here...",
            label="Clinical Notes",
            elem_id="input-box"
        )
    ],
    outputs=[
        gr.Textbox(
            lines=8,
            label="Generated Clinical Report",
            elem_id="output-box"
        )
    ],
    title="Clinical Report Generator",
    description="Generate professional clinical reports from clinical notes using a 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."]
    ],
    theme=gr.themes.Soft(),
    css="""
        #input-box { background-color: #f6f6f6; }
        #output-box { background-color: #f0f7ff; }
    """,
    flagging_mode="never"
)

# Enable queue
demo.queue()

# Launch the app with optimized configuration for Hugging Face Spaces
if __name__ == "__main__":
    demo.queue(concurrency_count=1, max_size=20)  # Configure queue for better performance
    demo.launch(
        server_name="0.0.0.0",
        share=True,
        show_error=True
    )