Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+import numpy as np
+
+# 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")
+
+# 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]
+    })
+
+    # 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
+
+    # One-hot encode High_School_Stream
+    stream_encoded = encoder.transform(input_data[["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)
+
+    # Predict
+    predicted_field_encoded = model.predict(X)
+    predicted_field = field_encoder.inverse_transform(predicted_field_encoded)
+
+    return predicted_field[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")
+]
+
+outputs = gr.Textbox(label="Predicted Field")
+
+# Create Gradio app
+app = gr.Interface(fn=predict_field, inputs=inputs, outputs=outputs, title="Student Field Prediction")
+
+# Launch the app
+app.launch()