Update app.py

app.py

@@ -82,7 +82,7 @@ def extract_features(image_path):
     }
 
 # Function to make predictions
-def predict_hemoglobin(Age, Gender, image):
+def predict_hemoglobin(age, gender, image):
     print(f"Image path: {image}")  # Debugging line to check the image path
     
     # Check if the image file is valid
@@ -90,8 +90,8 @@ def predict_hemoglobin(Age, Gender, image):
         return "Error: The uploaded image file is not recognized or is corrupt."
     
     features = extract_features(image)
-    features['Age'] = Age
-    features['Gender'] = 1 if Gender.lower() == 'male' else 0
+    features['Age'] = age
+    features['Gender'] = 1 if gender.lower() == 'male' else 0
     features_df = pd.DataFrame([features])
 
     # Load models
@@ -100,9 +100,9 @@ def predict_hemoglobin(Age, Gender, image):
     label_encoder = joblib.load('label_encoder.pkl')
 
     features_df_scaled = scaler.transform(features_df)
-    Hgb = svr_model.predict(features_df_scaled)[0]
+    hemoglobin = svr_model.predict(features_df_scaled)[0]
     
-    return f"Predicted Hemoglobin Value: {Hgb:.2f}"
+    return f"Predicted Hemoglobin Value: {hemoglobin:.2f}"
 
 # Gradio Interface
 def gradio_interface():
@@ -110,8 +110,8 @@ def gradio_interface():
         gr.Markdown("## Hemoglobin Prediction from Image Features")
         
         with gr.Row():
-            Age = gr.Number(label="Age", value=25)
-            Gender = gr.Dropdown(choices=["Male", "Female"], label="Gender", value="Male")
+            age = gr.Number(label="Age", value=25)
+            gender = gr.Dropdown(choices=["Male", "Female"], label="Gender", value="Male")
         
         image_input = gr.Image(type="filepath", label="Upload Image (Path to Image)", interactive=True)
         
@@ -120,7 +120,7 @@ def gradio_interface():
         # Prediction button
         predict_btn = gr.Button("Predict Hemoglobin")
         
-        predict_btn.click(fn=predict_hemoglobin, inputs=[Age, Gender, image_input], outputs=output)
+        predict_btn.click(fn=predict_hemoglobin, inputs=[age, gender, image_input], outputs=output)
     
     return demo