Update app.py

app.py

@@ -11,17 +11,17 @@ from sklearn.preprocessing import LabelEncoder
 
 # Initialize LabelEncoder for gender encoding
 gender_encoder = LabelEncoder()
-gender_encoder.fit(['Female', 'Male'])  # Ensure that the labels are ordered
+gender_encoder.fit(['Female', 'Male'])  # Ensuring correct label mapping
 
 # Function to extract features from the image
 def extract_features(image):
     # Convert PIL image to NumPy array
-    image = np.array(image) 
+    image = np.array(image)
 
     # Extract RGB means
-    meanr = np.mean(image[:, :, 0])  # Red channel
-    meang = np.mean(image[:, :, 1])  # Green channel
-    meanb = np.mean(image[:, :, 2])  # Blue channel
+    meanr = np.mean(image[:, :, 0])  
+    meang = np.mean(image[:, :, 1])  
+    meanb = np.mean(image[:, :, 2])  
 
     # Convert to HSI and compute HHR
     hsv_image = rgb2hsv(image)
@@ -39,21 +39,12 @@ def extract_features(image):
     # Compute Brightness
     B = np.mean(gray_image)
 
-    # Sliding window for gray-level features
-    def g1_filter(window):
-        return window[4] - np.min(window)
-
-    def g2_filter(window):
-        return np.max(window) - window[4]
-
-    def g3_filter(window):
-        return window[4] - np.mean(window)
-
-    def g4_filter(window):
-        return np.std(window)
-
-    def g5_filter(window):
-        return window[4]
+    # Define sliding window filters
+    def g1_filter(window): return window[4] - np.min(window)
+    def g2_filter(window): return np.max(window) - window[4]
+    def g3_filter(window): return window[4] - np.mean(window)
+    def g4_filter(window): return np.std(window)
+    def g5_filter(window): return window[4]
 
     # Apply filters with 3x3 window
     g1 = generic_filter(gray_image, g1_filter, size=3).mean()
@@ -62,71 +53,56 @@ def extract_features(image):
     g4 = generic_filter(gray_image, g4_filter, size=3).mean()
     g5 = generic_filter(gray_image, g5_filter, size=3).mean()
 
-    # Return features
+    # Return extracted features
     return {
-        "meanr": meanr,
-        "meang": meang,
-        "meanb": meanb,
-        "HHR": HHR,
-        "Ent": Ent,
-        "B": B,
-        "g1": g1,
-        "g2": g2,
-        "g3": g3,
-        "g4": g4,
-        "g5": g5,
+        "meanr": meanr, "meang": meang, "meanb": meanb,
+        "HHR": HHR, "Ent": Ent, "B": B, "g1": g1,
+        "g2": g2, "g3": g3, "g4": g4, "g5": g5,
     }
 
-# Function to check if the image is a valid file format
-def check_image_format(filepath):
+# Function to predict hemoglobin value
+def predict_hemoglobin(age, gender, image):
     try:
-        # Try opening the image using PIL
-        with Image.open(filepath) as img:
-            img.verify()  # Verify if it's a valid image file
-        return True
-    except Exception as e:
-        print(f"Error opening image: {e}")
-        return False
-
-# Function to predict hemoglobin value with label encoding for gender
-def predict_hemoglobin(age, Gender, image):
-    try:
-        # Ensure the image is not None
+        # Validate image input
         if image is None:
             return "Error: No image uploaded. Please upload an image."
 
-        # Check if the image is valid
         if not isinstance(image, Image.Image):
             return "Error: Invalid image format. Please upload a valid image file."
 
+        # Validate gender input
+        if gender not in ["Male", "Female"]:
+            return "Error: Invalid gender selected."
+
+        print(f"Received Gender: {gender}")  # Debugging line
+
         # Extract features from the image
         features = extract_features(image)
 
-        # Use LabelEncoder to convert gender to numerical value
+        # Encode gender as a numerical value
         features['Gender'] = gender_encoder.transform([gender])[0]
         features['Age'] = age
 
-        # Create a DataFrame for features (do not include Hgb, as it's the predicted value)
+        # Convert to DataFrame
         features_df = pd.DataFrame([features])
 
-        # Load the trained model, scaler, and label encoder
-        svr_model = joblib.load('lgbm_model.pkl')  # Replace with the actual path to your model file
-        scaler = joblib.load('minmax_scaler.pkl')     # Replace with the actual path to your scaler file
+        # Load trained model and scaler
+        model = joblib.load('lgbm_model.pkl')  # Replace with actual path
+        scaler = joblib.load('minmax_scaler.pkl')  # Replace with actual path
 
-        # Ensure that features_df matches the expected training feature set (without 'Hgb')
+        # Ensure features match expected columns
         expected_columns = ['meanr', 'meang', 'meanb', 'HHR', 'Ent', 'B', 'g1', 'g2', 'g3', 'g4', 'g5', 'Age', 'Gender']
-
         for col in expected_columns:
             if col not in features_df:
-                features_df[col] = 0  # Or some default value to match the expected columns.
+                features_df[col] = 0  # Assign default value if missing
 
-        features_df = features_df[expected_columns]  # Ensure the correct order of columns
+        features_df = features_df[expected_columns]  # Ensure correct column order
 
-        # Apply scaling (do not include 'Hgb' as it is the target)
+        # Apply scaling
         features_df_scaled = scaler.transform(features_df)
 
-        # Predict hemoglobin using the trained SVR model
-        hemoglobin = svr_model.predict(features_df_scaled)[0]
+        # Predict hemoglobin
+        hemoglobin = model.predict(features_df_scaled)[0]
 
         return f"Predicted Hemoglobin Value: {hemoglobin:.2f}"
 
@@ -137,16 +113,16 @@ def predict_hemoglobin(age, Gender, image):
 # Gradio interface
 with gr.Blocks() as anemia_detection_app:
     gr.Markdown("# Hemoglobin Prediction App")
-    
+
     with gr.Row():
         age_input = gr.Number(label="Age", value=25)
-        gender_input = gr.Radio(label="Gender", choices=["Male", "Female"], value="Male")
+        gender_input = gr.Radio(label="Gender", choices=["Male", "Female"], value="Male", type="value")  # ✅ FIXED
     
     image_input = gr.Image(label="Upload Retinal Image", type="pil")
     output_text = gr.Textbox(label="Predicted Hemoglobin Value")
-    
+
     predict_button = gr.Button("Predict")
-    
+
     predict_button.click(
         fn=predict_hemoglobin,
         inputs=[age_input, gender_input, image_input],
@@ -155,4 +131,4 @@ with gr.Blocks() as anemia_detection_app:
 
 # Run the app
 if __name__ == "__main__":
-    anemia_detection_app.launch()
+    anemia_detection_app.launch(share=True)  # Enable public link