Update app.py

app.py

@@ -1,79 +1,91 @@
-import os
-import warnings
-import numpy as np
-import pandas as pd
-from tensorflow.keras.models import load_model
-import pickle
-from dotenv import load_dotenv
-import gradio as gr
-
-# Suppress TensorFlow and other warnings
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-warnings.filterwarnings("ignore")
-
-# Load environment variables
-load_dotenv()
-
-# Load the trained model and scaler
-model = None
-scaler = None
-
-def load_model_and_scaler():
-    global model, scaler
-    try:
-        model = load_model('final_marks_predictor_model.h5')
-        with open('scaler.pkl', 'rb') as f:
-            scaler = pickle.load(f)
-    except Exception as e:
-        print(f"Error loading model or scaler: {e}")
-
-# Load model and scaler when the application starts
-load_model_and_scaler()
-
-def predict_new_input(age, year1_marks, year2_marks, studytime, failures):
-    try:
-        feature_names = ['age', 'year1_marks', 'year2_marks', 'studytime', 'failures']
-        new_input_df = pd.DataFrame([[age, year1_marks, year2_marks, studytime, failures]], columns=feature_names)
-
-        if model is None or scaler is None:
-            return "Model or scaler is not loaded."
-        
-        new_input_scaled = scaler.transform(new_input_df)
-        predicted_marks = model.predict(new_input_scaled, verbose=0)
-        return round(float(predicted_marks[0][0]), 2)
-    except Exception as e:
-        print(f"Error during prediction: {e}")
-        return "Error during prediction"
-
-# Define Gradio Interface
-def gradio_predict(age, year1_marks, year2_marks, studytime, failures):
-    return predict_new_input(age, year1_marks, year2_marks, studytime, failures)
-
-if __name__ == '__main__':
-    # Create the Gradio interface
-    with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 600px; margin: auto; padding: 20px; border: 1px solid #e0e0e0; border-radius: 10px; box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);}") as interface:
-        with gr.Column():
-            gr.Markdown("## Student Performance Prediction")
-            gr.Markdown("Please fill in all the required fields and click 'Predict' to see your final predicted marks.")
-            
-            # Create form fields
-            with gr.Row():
-                age = gr.Number(label="Age", interactive=True, elem_id="age-input")
-            with gr.Row():
-                year1_marks = gr.Number(label="First Year Marks", interactive=True,  elem_id="year1-input")
-            with gr.Row():
-                year2_marks = gr.Number(label="Second Year Marks", interactive=True,  elem_id="year2-input")
-            with gr.Row():
-                studytime = gr.Number(label="Study Time (hours/week)", interactive=True, elem_id="studytime-input")
-            with gr.Row():
-                failures = gr.Number(label="Number of Failures", interactive=True,  elem_id="failures-input")
-            
-            submit_button = gr.Button("Predict", elem_id="predict-button", variant="primary")
-            
-            # Create output display
-            output = gr.Textbox(label="Predicted Final Marks", interactive=False, elem_id="output-box")
-        
-        # Button action
-        submit_button.click(gradio_predict, inputs=[age, year1_marks, year2_marks, studytime, failures], outputs=output)
-
-        interface.launch(share=False)
+import os
+import warnings
+import numpy as np
+import pandas as pd
+from tensorflow.keras.models import load_model
+import pickle
+from dotenv import load_dotenv
+import gradio as gr
+
+# Suppress TensorFlow and other warnings
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+warnings.filterwarnings("ignore")
+
+# Load environment variables
+load_dotenv()
+
+# Load the trained model and scaler
+model = None
+scaler = None
+
+def load_model_and_scaler():
+    global model, scaler
+    try:
+        model_path = 'final_marks_predictor_model.h5'
+        scaler_path = 'scaler.pkl'
+        
+        if not os.path.exists(model_path):
+            print(f"Model file not found: {model_path}")
+            return
+        
+        if not os.path.exists(scaler_path):
+            print(f"Scaler file not found: {scaler_path}")
+            return
+        
+        model = load_model(model_path)
+        with open(scaler_path, 'rb') as f:
+            scaler = pickle.load(f)
+        
+        print("Model and scaler loaded successfully")
+    except Exception as e:
+        print(f"Error loading model or scaler: {e}")
+
+# Load model and scaler when the application starts
+load_model_and_scaler()
+
+def predict_new_input(age, year1_marks, year2_marks, studytime, failures):
+    try:
+        feature_names = ['age', 'year1_marks', 'year2_marks', 'studytime', 'failures']
+        new_input_df = pd.DataFrame([[age, year1_marks, year2_marks, studytime, failures]], columns=feature_names)
+
+        if model is None or scaler is None:
+            return "Model or scaler is not loaded."
+        
+        new_input_scaled = scaler.transform(new_input_df)
+        predicted_marks = model.predict(new_input_scaled, verbose=0)
+        return round(float(predicted_marks[0][0]), 2)
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+        return "Error during prediction"
+
+# Define Gradio Interface
+def gradio_predict(age, year1_marks, year2_marks, studytime, failures):
+    return predict_new_input(age, year1_marks, year2_marks, studytime, failures)
+
+if __name__ == '__main__':
+    # Create the Gradio interface
+    with gr.Blocks(theme=gr.themes.Soft()) as interface:
+        with gr.Column():
+            gr.Markdown("## Student Performance Prediction")
+            gr.Markdown("Please fill in all the required fields and click 'Predict' to see your final predicted marks.")
+            
+            # Create form fields
+            with gr.Row():
+                age = gr.Number(label="Age", interactive=True, placeholder="Enter Age")
+            with gr.Row():
+                year1_marks = gr.Number(label="First Year Marks", interactive=True, placeholder="Enter Year 1 Marks")
+            with gr.Row():
+                year2_marks = gr.Number(label="Second Year Marks", interactive=True, placeholder="Enter Year 2 Marks")
+            with gr.Row():
+                studytime = gr.Number(label="Study Time (hours/week)", interactive=True, placeholder="Enter Study Time")
+            with gr.Row():
+                failures = gr.Number(label="Number of Failures", interactive=True, placeholder="Enter Number of Failures")
+            
+            submit_button = gr.Button("Predict", variant="primary")
+            output = gr.Textbox(label="Predicted Final Marks", interactive=False)
+        
+        # Button action
+        submit_button.click(gradio_predict, inputs=[age, year1_marks, year2_marks, studytime, failures], outputs=output)
+
+        interface.launch(share=False)
+