Update placement.py

placement.py

@@ -49,38 +49,26 @@ pipeline.fit(X_train, y_train)
 # Evaluate the model
 accuracy = pipeline.score(X_test, y_test)
 print('Accuracy:', accuracy)
-joblib.dump(pipeline, 'student_placement_model.joblib')
+input_type = 'csv'
+output_type = 'label'
 
-# Define Streamlit API
-def predict_placement(internships, cgpa, history_of_backlogs, stream):
-    # Load the trained pipeline
-    pipeline = joblib.load('student_placement_model.joblib')
+# Define the function to make predictions using the trained model
+def predict_placement(Internships, CGPA, HistoryOfBacklogs):
+    # Create a DataFrame from the input data
+    input_df = pd.DataFrame({'Internships': [Internships], 'CGPA': [CGPA], 'HistoryOfBacklogs': [HistoryOfBacklogs]})
     
-    # Prepare input data
-    input_data = pd.DataFrame({'internships': [internships],
-                                'cgpa': [cgpa],
-                                'history_of_backlogs': [history_of_backlogs],
-                                'stream': [stream]})
+    # Make a prediction using the trained model
+    prediction = pipeline.predict(input_df)[0]
     
-    # Make prediction
-    prediction = pipeline.predict(input_data)
-    
-    return prediction[0]
+    # Return the predicted label
+    return 'Placed' if prediction else 'Not Placed'
 
-# Define Streamlit web app
-def streamlit_app():
-    title('Student Placement Prediction')
-    internships = number_input('Number of internships:', min_value=0, max_value=10, step=1)
-    cgpa = number_input('CGPA:', min_value=0.0, max_value=10.0, step=0.1)
-    history_of_backlogs = number_input('Number of history of backlogs:', min_value=0, max_value=10, step=1)
-    stream = selectbox('Stream:', options=['Science', 'Commerce', 'Arts'])
-    prediction = predict_placement(internships, cgpa, history_of_backlogs, stream)
-    if prediction == 1:
-        result = 'Placed'
-    else:
-        result = 'Not Placed'
-    button('Predict Placement')
-    write(f'Result: {result}')
+# Create the Gradio interface
+iface = gr.Interface(fn=predict_placement, 
+                     inputs=input_type, 
+                     outputs=output_type, 
+                     title='Student Job Placement Predictor', 
+                     description='Predicts whether a student will be placed in a job or not based on internships, CGPA, and history of backlogs.')
 
-if __name__ == '__main__':
-    streamlit_app()
+# Launch the Gradio interface
+iface.launch()