Delete app.py

app.py

@@ -1,104 +0,0 @@
-import gradio as gr
-
-# Import modules
-from models.image_analyzer import ImageAnalyzer
-from models.multimodal_fusion import MultimodalFusion
-from models.text_analyzer import TextAnalyzer
-from utils.visualization import create_combined_visualization
-
-
-def process_input(image, medical_report):
-    """Process the image and text inputs and provide combined analysis"""
-    # Initialize analyzers
-    image_analyzer = ImageAnalyzer()
-    text_analyzer = TextAnalyzer()
-    fusion_model = MultimodalFusion()
-
-    # Analyze inputs
-    image_results = image_analyzer.analyze(image)
-    text_results = text_analyzer.analyze(medical_report)
-
-    # Fuse insights
-    combined_results = fusion_model.fuse_insights(image_results, text_results)
-
-    # Visualize results
-    visualization = create_combined_visualization(
-        image, image_results, text_results, combined_results
-    )
-
-    # Format results for display
-    image_findings = "\n".join(
-        [
-            f"{k}: {v:.2f}" if isinstance(v, float) else f"{k}: {v}"
-            for k, v in image_results.items()
-        ]
-    )
-    text_findings = "\n".join(
-        [f"{k}: {v}" for k, v in text_results.items() if k != "Entities"]
-    )
-    entities = ", ".join(text_results.get("Entities", []))
-
-    recommendation = combined_results.get("Recommendation", "No recommendation")
-    confidence = combined_results.get("Confidence", "N/A")
-
-    return (
-        visualization,
-        image_findings,
-        text_findings,
-        entities,
-        recommendation,
-        confidence,
-    )
-
-
-# Create Gradio interface
-with gr.Blocks(title="MediSync: Multi-Modal Medical Analysis System") as demo:
-    gr.Markdown("# MediSync: Multi-Modal Medical Analysis System")
-    gr.Markdown(
-        "Upload a chest X-ray image and provide a medical report for comprehensive analysis"
-    )
-
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(type="pil", label="Upload X-ray Image")
-            text_input = gr.Textbox(lines=10, label="Enter Medical Report")
-            submit_btn = gr.Button("Analyze")
-
-        with gr.Column():
-            output_image = gr.Image(type="pil", label="Visualization")
-
-            with gr.Tab("Summary"):
-                recommendation_output = gr.Textbox(label="Recommendation")
-                confidence_output = gr.Textbox(label="Confidence Score")
-
-            with gr.Tab("Detailed Results"):
-                image_output = gr.Textbox(label="Image Analysis")
-                text_output = gr.Textbox(label="Text Analysis")
-                entities_output = gr.Textbox(label="Detected Medical Entities")
-
-    submit_btn.click(
-        process_input,
-        inputs=[image_input, text_input],
-        outputs=[
-            output_image,
-            image_output,
-            text_output,
-            entities_output,
-            recommendation_output,
-            confidence_output,
-        ],
-    )
-
-    gr.Markdown("""
-    ## About MediSync
-    
-    MediSync is an AI-powered healthcare solution that combines X-ray image analysis with patient report text processing 
-    to provide comprehensive medical insights.
-    
-    **Note:** This system is designed as a support tool and should not replace professional medical diagnosis. 
-    Always consult with healthcare professionals for medical decisions.
-    """)
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch()