Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+import joblib
+import pandas as pd
+
+# Load the model and encoders
+model = joblib.load('random_forest_model.joblib')
+venue_encoder = joblib.load('venue_encoder.joblib')
+match_type_encoder = joblib.load('match_type_encoder.joblib')
+team_batting_encoder = joblib.load('team_batting_encoder.joblib')
+team_bowling_encoder = joblib.load('team_bowling_encoder.joblib')
+
+def predict_score(venue, match_type, team_batting, team_bowling):
+    # Preprocess inputs
+    venue_encoded = venue_encoder.transform([venue])[0]
+    match_type_encoded = match_type_encoder.transform([match_type])[0]
+    team_batting_encoded = team_batting_encoder.transform([team_batting])[0]
+    team_bowling_encoded = team_bowling_encoder.transform([team_bowling])[0]
+    
+    # Prepare input for the model
+    input_data = pd.DataFrame({
+        'Venue': [venue_encoded],
+        'Match_Type': [match_type_encoded],
+        'Team_Batting': [team_batting_encoded],
+        'Team_Bowling': [team_bowling_encoded]
+    })
+    
+    # Predict and return score
+    predicted_score = model.predict(input_data)[0]
+    return round(predicted_score)
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict_score, 
+    inputs=[
+        gr.inputs.Dropdown(['MCG', 'Eden Gardens', 'Wankhede'], label='Venue'),
+        gr.inputs.Dropdown(['ODI', 'T20'], label='Match Type'),
+        gr.inputs.Dropdown(['India', 'Australia', 'England'], label='Team Batting'),
+        gr.inputs.Dropdown(['Australia', 'India', 'England'], label='Team Bowling')
+    ], 
+    outputs=gr.outputs.Textbox(label="Predicted Score"),
+    title="Cricket Match Score Predictor",
+    description="Enter match details to predict the final score."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()