| import gradio as gr |
| from main import final_pipeline |
| from PIL import Image |
| import torch |
|
|
| CSS = """ |
| .gradio-container { |
| max-width: 1200px !important; |
| } |
| """ |
|
|
| def predict(image, question): |
| if image is None: |
| return "Please upload an image! 📸" |
| if not question.strip(): |
| return "Please enter a question! ❓" |
| |
| try: |
| result = final_pipeline(image, question) |
| return result |
| except Exception as e: |
| return f"❌ Error: {str(e)}" |
|
|
| with gr.Blocks(css=CSS, title="🩻 Medical VQA - MultiLanguage") as demo: |
| gr.Markdown(""" |
| # 🩻 **Medical VQA - MultiLanguage** |
| Upload X-ray, MRI, CT scans and ask questions in **ANY language**! |
| |
| **Supports:** English, Hindi, Tamil, Telugu, Kannada, Malayalam |
| """) |
| |
| with gr.Row(): |
| with gr.Column(scale=1): |
| image_input = gr.Image(type="pil", label="Upload Medical Image") |
| question_input = gr.Textbox( |
| label="Question (Any Language)", |
| placeholder="What is shown in this X-ray? / இதில் என்ன உள்ளது?", |
| lines=2 |
| ) |
| predict_btn = gr.Button("🔍 Analyze Image", variant="primary", size="lg") |
| |
| with gr.Column(scale=1): |
| answer_output = gr.Markdown(label="🤖 AI Answer") |
| |
| predict_btn.click(predict, [image_input, question_input], answer_output) |
| |
| gr.Examples( |
| examples=[ |
| ["What is this fracture?", None], |
| ["இதில் எந்த உடல்நலக் குறைபாடு உள்ளது?", None], |
| ["ఈ చిత్రంలో ఏముంది?", None], |
| ["Describe this X-ray", None] |
| ], |
| inputs=[question_input, image_input], |
| outputs=answer_output |
| ) |
| |
| gr.Markdown("---") |
| gr.Markdown(""" |
| **Powered by:** Custom VQA (ResNet18+LSTM) + BLIP2 + NLLB-200 |
| **Dataset:** VQA-RAD (Medical Images) |
| """) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
