app.py created

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+import tempfile
+import os
+import sys
+
+# Add project root to Python path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Import predictor class
+from src.predict_fracture import FracturePredictor
+
+# Get current script location
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(current_dir)  # Go up from app/ to project root
+
+# CORRECTED MODEL PATHS
+MODEL_PATH = 'models/fracture_detection_model.joblib'
+ENCODER_PATH = 'models/label_encoder.joblib'
+# Debugging output
+print(f"Project root: {project_root}")
+print(f"Model path: {MODEL_PATH}")
+print(f"Model exists: {os.path.exists(MODEL_PATH)}")
+print(f"Encoder exists: {os.path.exists(ENCODER_PATH)}")
+
+# Initialize predictor only if files exist
+if os.path.exists(MODEL_PATH) and os.path.exists(ENCODER_PATH):
+    predictor = FracturePredictor(model_path=MODEL_PATH, encoder_path=ENCODER_PATH)
+else:
+    print("ERROR: Model files not found. Please run training first.")
+    exit(1)
+
+def predict_fracture(img):
+    """Process uploaded image and return prediction results"""
+    try:
+        # Handle different input types
+        if isinstance(img, np.ndarray):
+            # Convert to BGR format for OpenCV
+            if img.shape[2] == 4:  # RGBA image
+                img_bgr = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR)
+            else:  # RGB image
+                img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+            
+            # Save to temp file
+            with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
+                tmp_path = tmp.name
+                cv2.imwrite(tmp_path, img_bgr)
+        else:
+            # Already a file path
+            tmp_path = img
+        
+        # Get prediction
+        label, confidence, vis_path = predictor.predict(tmp_path)
+        
+        # Read visualization image
+        vis_img = cv2.imread(vis_path)
+        if vis_img is not None:
+            vis_img = cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB)
+        
+        # Clean up temporary file
+        if isinstance(img, np.ndarray) and os.path.exists(tmp_path):
+            os.unlink(tmp_path)
+        
+        return label, f"{confidence:.4f}", vis_img
+    
+    except Exception as e:
+        print(f"Prediction error: {str(e)}")
+        return "Error", "N/A", None
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_fracture,
+    inputs=gr.Image(label="Upload X-Ray Image"),
+    outputs=[
+        gr.Label(label="Prediction Result"),
+        gr.Textbox(label="Confidence Score"),
+        gr.Image(label="Prediction Visualization")
+    ],
+    title="🦴 Bone Fracture Detection System",
+    description="Upload an X-ray image to detect bone fractures using GLCM features and SVM classifier",
+    examples=[
+        [os.path.join("samples", "fractured_1.jpg")],
+        [os.path.join("samples", "normal_1.jpg")]
+    ],
+    flagging_mode="never"
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)