app.py

import gradio as gr
import torch
from transformers import pipeline

# Load the NER model
model_name = "AventIQ-AI/bert-medical-entity-extraction"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

print("Loading model...")
ner_pipeline = pipeline("ner", model=model_name, tokenizer=model_name, aggregation_strategy="simple", device=0 if torch.cuda.is_available() else -1)

# Define entity mapping based on README
entity_mapping = {
    "LABEL_1": "Symptom",
    "LABEL_2": "Disease",
    "LABEL_3": "Medication",
    "LABEL_4": "Treatment",
    "LABEL_5": "Anatomy",
    "LABEL_6": "Medical Procedure"
}

def extract_medical_entities(text):
    """Extract relevant medical entities from the input text."""
    if not text.strip():
        return "⚠️ Please enter a valid medical text."

    print(f"Processing: {text}")
    entities = ner_pipeline(text)

    # Filter out non-entity labels (e.g., "O" or punctuation)
    relevant_entities = [
        f"📌 **{entity['word'].replace('##', '')}** → `{entity_mapping.get(entity['entity_group'], entity['entity_group'])}`"
        for entity in entities if entity['entity_group'] in entity_mapping
    ]

    response = "\n".join(relevant_entities) if relevant_entities else "⚠️ No relevant medical entities detected."
    print(f"Response: {response}")
    return response

# Create Gradio Interface
iface = gr.Interface(
    fn=extract_medical_entities,
    inputs=gr.Textbox(label="📝 Enter Medical Text", placeholder="Type or paste a medical report...", lines=3),
    outputs=gr.Textbox(label="🏥 Extracted Medical Entities", placeholder="Detected medical terms will appear here...", lines=5),
    title="🔬 Medical Entity Extraction",
    description="💉 Enter a medical-related text, and the AI will extract **diseases, symptoms, medications, and treatments.**",
    theme="compact",
    allow_flagging="never",
    examples=[
        ["The patient is diagnosed with diabetes and prescribed metformin."],
        ["Symptoms include fever, sore throat, and fatigue."],
        ["He underwent a knee replacement surgery at Mayo Clinic."]
    ],
)

if __name__ == "__main__":
    iface.launch()