Update app.py

app.py

@@ -1,64 +1,77 @@
 import gradio as gr
 import pandas as pd
-import joblib
-import numpy as np
+from joblib import load
 
-# Load the model and encoders
-model = joblib.load("decision_tree_model.pkl")
-encoder = joblib.load("one_hot_encoder.pkl")
-field_encoder = joblib.load("label_encoder.pkl")
+# Load the saved model and encoders
+model = load("trained_model.joblib")
+encoder = load("one_hot_encoder.joblib")
+field_encoder = load("label_encoder.joblib")
 
-# Define the prediction function
-def predict_field(high_school_stream, physics, math, chemistry, biology, economics, geography, history, literature):
-    # Create a DataFrame from inputs
-    input_data = pd.DataFrame({
-        "High_School_Stream": [high_school_stream],
-        "Physics": [physics],
-        "Math": [math],
-        "Chemistry": [chemistry],
-        "Biology": [biology],
-        "Economics": [economics],
-        "Geography": [geography],
-        "History": [history],
-        "Literature": [literature]
-    })
+def preprocess_input(high_school_stream, physics, math, chemistry, biology, economics, geography, history, literature):
+    """
+    Preprocess user input for prediction.
+    """
+    # Create a dictionary for the input data
+    user_data = {
+        "High_School_Stream": high_school_stream,
+        "Physics": physics,
+        "Math": math,
+        "Chemistry": chemistry,
+        "Biology": biology,
+        "Economics": economics,
+        "Geography": geography,
+        "History": history,
+        "Literature": literature,
+    }
 
-    # Fill NaN with 0
-    input_data.fillna(0, inplace=True)
-
-    # Compute Performance_Score
-    input_data["Performance_Score"] = input_data.iloc[:, 1:9].sum(axis=1) / 100
+    # Convert to DataFrame
+    user_df = pd.DataFrame([user_data])
 
     # One-hot encode High_School_Stream
-    stream_encoded = encoder.transform(input_data[["High_School_Stream"]])
+    stream_encoded = encoder.transform(user_df[["High_School_Stream"]])
     stream_encoded_df = pd.DataFrame(stream_encoded, columns=encoder.get_feature_names_out(["High_School_Stream"]))
 
-    # Combine encoded features with Performance_Score
-    X = pd.concat([stream_encoded_df, input_data["Performance_Score"]], axis=1)
+    # Combine encoded features with subject scores
+    user_processed = pd.concat([stream_encoded_df, user_df.iloc[:, 1:]], axis=1)
+
+    return user_processed
+
+def predict(high_school_stream, physics, math, chemistry, biology, economics, geography, history, literature):
+    """
+    Make a prediction using the trained model.
+    """
+    # Preprocess input
+    user_processed = preprocess_input(high_school_stream, physics, math, chemistry, biology, economics, geography, history, literature)
 
-    # Predict
-    predicted_field_encoded = model.predict(X)
-    predicted_field = field_encoder.inverse_transform(predicted_field_encoded)
+    # Make prediction
+    y_pred_encoded = model.predict(user_processed)
+    y_pred = field_encoder.inverse_transform(y_pred_encoded)
 
-    return predicted_field[0]
+    return y_pred[0]
 
 # Define Gradio interface
 inputs = [
     gr.Dropdown(label="High School Stream", choices=["PCM", "PCB", "MEG", "MPG", "HGL"]),
-    gr.Number(label="Physics Score"),
-    gr.Number(label="Math Score"),
-    gr.Number(label="Chemistry Score"),
-    gr.Number(label="Biology Score"),
-    gr.Number(label="Economics Score"),
-    gr.Number(label="Geography Score"),
-    gr.Number(label="History Score"),
-    gr.Number(label="Literature Score")
+    gr.Number(label="Physics Score (0 if not applicable)"),
+    gr.Number(label="Math Score (0 if not applicable)"),
+    gr.Number(label="Chemistry Score (0 if not applicable)"),
+    gr.Number(label="Biology Score (0 if not applicable)"),
+    gr.Number(label="Economics Score (0 if not applicable)"),
+    gr.Number(label="Geography Score (0 if not applicable)"),
+    gr.Number(label="History Score (0 if not applicable)"),
+    gr.Number(label="Literature Score (0 if not applicable)"),
 ]
 
-outputs = gr.Textbox(label="Predicted Field")
+output = gr.Textbox(label="Predicted Field")
 
 # Create Gradio app
-app = gr.Interface(fn=predict_field, inputs=inputs, outputs=outputs, title="Student Field Prediction")
+app = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=output,
+    title="Student Field Prediction",
+    description="Enter your details to predict the recommended field of study.",
+)
 
 # Launch the app
 app.launch()