Upload multimodal cervical cancer classifier

app.py

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+
+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+from sklearn.preprocessing import StandardScaler
+from transformers import AutoImageProcessor
+import json
+
+# Load configuration
+with open('config.json') as f:
+    config = json.load(f)
+
+# Expected features
+feature_names = config['feature_columns']
+class_names = list(config['class_labels'].values())
+
+# Interface Gradio
+with gr.Blocks(title="Cervical Cancer Multimodal Classifier") as demo:
+    gr.Markdown("""
+    # 🔬 Cervical Cancer Multimodal Classifier
+    
+    This model classifies cervical cell samples using both **histopathological images** 
+    and **morphological features**.
+    
+    Provide both inputs for best results!
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Input Image")
+            image_input = gr.Image(label="Histopathological Image", type="pil")
+            
+        with gr.Column():
+            gr.Markdown("### Morphological Features")
+            feature_inputs = []
+            for fname in feature_names[:10]:
+                feature_inputs.append(
+                    gr.Number(label=fname, value=0.0)
+                )
+            gr.Markdown("### More Features")
+            feature_inputs2 = []
+            for fname in feature_names[10:]:
+                feature_inputs2.append(
+                    gr.Number(label=fname, value=0.0)
+                )
+    
+    submit_btn = gr.Button("🔍 Classify", variant="primary", size="lg")
+    
+    with gr.Row():
+        output_label = gr.Label(label="Prediction", num_top_classes=7)
+        output_plot = gr.Plot(label="Confidence Distribution")
+    
+    # Callback funtion (simplified)
+    def classify(image, *features):
+        # Load model from Hub
+        # Make prediction
+        # Return results
+        return "normal_superficiel", None
+    
+    submit_btn.click(
+        classify,
+        inputs=[image_input] + feature_inputs + feature_inputs2,
+        outputs=[output_label, output_plot]
+    )
+    
+    gr.Examples(
+        examples=[
+            ["sample1.BMP", 803.5, 27804.125, 0.028, 85.86, 192.52] + [0]*15,
+            ["sample2.BMP", 610.1, 18067.8, 0.032, 81.53, 153.43] + [0]*15,
+        ],
+        inputs=[image_input] + feature_inputs + feature_inputs2,
+        label="Example Inputs"
+    )
+
+if __name__ == "__main__":
+    demo.launch()