Update app.txt

app.txt

@@ -1,124 +1,104 @@
 import gradio as gr
 import joblib
-import pandas as pd
+import numpy as np
 
 # Load the model
-model = joblib.load('accident_prediction_model_Final.m5')
-
-# Load the encoder
-encoder = joblib.load('encoder.pkl')
-
-# Define classes for accident prediction
-classes = ["No", "Yes"]
+model = joblib.load('cricket_score_prediction_model.pkl')
 
 # Create the inputs list with dropdown menus and sliders
 inputs = [
     gr.Dropdown(
-        choices=['Sunny/Clear', 'Rainy', 'Hail/Sleet', 'Foggy/Misty', 'Others'],
-        label="Weather Conditions"
-    ),
-    gr.Dropdown(
-        choices=['Pedestrian', 'Bicycles', 'Two Wheelers', 'Auto Rickshaws', 'Cars, Taxis, Vans & LMV', 'Trucks, Lorries', 'Buses', 'Non-motorized Vehicles', 'Others'],
-        label="Impact Type"
-    ),
-    gr.Dropdown(
-        choices=['Speeding', 'Jumping Red Light', 'Distracted Driving', 'Drunk Driving', 'Other'],
-        label="Traffic Violations"
+        choices=['Afghanistan', 'Australia', 'Bangladesh', 'England', 'India', 'Ireland', 'New Zealand', 'Pakistan',
+                 'South Africa', 'Sri Lanka', 'West Indies', 'Zimbabwe'],
+        label="Batting Team"
     ),
     gr.Dropdown(
-        choices=['Straight Road', 'Curved Road', 'Bridge', 'Culvert', 'Pot Holes', 'Steep Grade', 'Ongoing Road Works/Under Construction', 'Others'],
-        label="Road Features"
+        choices=['Afghanistan', 'Australia', 'Bangladesh', 'England', 'India', 'Ireland', 'New Zealand', 'Pakistan',
+                 'South Africa', 'Sri Lanka', 'West Indies', 'Zimbabwe'],
+        label="Bowling Team"
     ),
-    gr.Dropdown(
-        choices=['T-Junction', 'Y-Junction', 'Four arm Junction', 'Staggered Junction', 'Round about Junction', 'Others'],
-        label="Junction Types"
-    ),
-    gr.Dropdown(
-        choices=['Traffic Light Signal', 'Police Controlled', 'Stop Sign', 'Flashing Signal/Blinker', 'Uncontrolled', 'Others'],
-        label="Traffic Controls"
-    ),
-    gr.Dropdown(
-        choices=['morning', 'afternoon', 'evening', 'night'],
-        label="Time of Day"
-    ),
-    gr.Dropdown(
-        choices=['13-17', '18-25', '26-40', '41-60', '60-80', '80 above'],
-        label="Age Group"
-    ),
-    gr.Dropdown(
-        choices=['Killed', 'Grievously Injured', 'Minor Injury'],
-        label="Injury Type"
-    ),
-    gr.Dropdown(
-        choices=['Yes', 'No'],
-        label="Safety Features"
-    ),
-    gr.Slider(minimum=-90, maximum=90, label="Latitude"),
-    gr.Slider(minimum=-180, maximum=180, label="Longitude"),
-    gr.Slider(minimum=1, maximum=10, step= 1, label="Person Count"),
+    gr.Slider(minimum=0, maximum=400, step=1, label="Total Runs"),
+    gr.Slider(minimum=0, maximum=11, step=1, label="Total Wickets"),
+    gr.Slider(minimum=0.0, maximum=19.6, step=0.1, label="Overs"),
+    gr.Slider(minimum=0, maximum=200, step=1, label="Runs last 5 overs"),
+    gr.Slider(minimum=0, maximum=11,step=1, label="Wickets last 5 overs"),
 ]
 
-# Define output label
-output_label = gr.Label(num_top_classes=4)
 
 # Create a function to make predictions
 def predict_accident(
-    weather_conditions,
-    impact_type,
-    traffic_violations,
-    road_features,
-    junction_types,
-    traffic_controls,
-    time_day,
-    age_group,
-    injury,
-    safety_features,
-    Latitude,
-    Longitude,
-    person_count
+    batting_team,
+    bowling_team,
+    total_runs,
+    total_wickets,
+    overs,
+    runs_last_5_overs,
+    wickets_last_5_overs
 ):
-    data = {
-        'selectedWeatherCondition': weather_conditions,
-        'selectedImpactType': impact_type,
-        'selectedTrafficViolationType': traffic_violations,
-        'selectedRoadFeaturesType': road_features,
-        'selectedRoadJunctionType': junction_types,
-        'selectedTrafficControl': traffic_controls,
-        'selectedTimeOfDay': time_day,
-        'selectedAge': age_group,
-        'selectedInjuryType': injury,
-        'selectedSafetyFeature': safety_features,
-        'Latitude': Latitude,
-        'Longitude': Longitude,
-        'personCount': person_count
-    }
-    
-    num_input = {'Latitude': data['Latitude'], 'Longitude': data['Longitude'], 'person_count': data['personCount']}
-    cat_input = {'weather_conditions': data['selectedWeatherCondition'], 'impact_type': data['selectedImpactType'],
-                 'traffic_voilations': data['selectedTrafficViolationType'],
-                 'road_features': data['selectedRoadFeaturesType'],
-                 'junction_types': data['selectedRoadJunctionType'],
-                 'traffic_controls': data['selectedTrafficControl'], 'time_day': data['selectedTimeOfDay'],
-                 'age_group': data['selectedAge'], 'safety_features': data['selectedSafetyFeature'],
-                 'injury': data['selectedInjuryType']}
-
-    input_df = pd.DataFrame([cat_input])
 
-    encoded_input = encoder['encoder'].transform(input_df)
-    encoded_input_df = pd.DataFrame(encoded_input, columns=encoder['encoded_columns'])
+    prediction_array = []
+    # Batting Team
+    if batting_team == 'Afghanistan':
+        prediction_array = prediction_array + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    elif batting_team == 'Australia':
+        prediction_array = prediction_array + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    elif batting_team == 'Bangladesh':
+        prediction_array = prediction_array + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    elif batting_team == 'England':
+        prediction_array = prediction_array + [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
+    elif batting_team == 'India':
+        prediction_array = prediction_array + [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
+    elif batting_team == 'Ireland':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
+    elif batting_team == 'New Zealand':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+    elif batting_team == 'Pakistan':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
+    elif batting_team == 'South Africa':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
+    elif batting_team == 'Sri Lanka':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
+    elif batting_team == 'West Indies':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
+    elif batting_team == 'Zimbabwe':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
+    # Bowling Team
+    if bowling_team == 'Afghanistan':
+        prediction_array = prediction_array + [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    elif bowling_team == 'Australia':
+        prediction_array = prediction_array + [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    elif bowling_team == 'Bangladesh':
+        prediction_array = prediction_array + [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    elif bowling_team == 'England':
+        prediction_array = prediction_array + [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
+    elif bowling_team == 'India':
+        prediction_array = prediction_array + [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
+    elif bowling_team == 'Ireland':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
+    elif bowling_team == 'New Zealand':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+    elif bowling_team == 'Pakistan':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
+    elif bowling_team == 'South Africa':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
+    elif bowling_team == 'Sri Lanka':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
+    elif bowling_team == 'West Indies':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
+    elif bowling_team == 'Zimbabwe':
+        prediction_array = prediction_array + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
 
-    num_df = pd.DataFrame([num_input])
-    input_with_coords = pd.concat([num_df, encoded_input_df], axis=1)
+    prediction_array = prediction_array + [total_runs, total_wickets, overs, runs_last_5_overs, wickets_last_5_overs]
+    prediction_array = np.array([prediction_array])
+    prediction = model.predict(prediction_array)
 
-    # Make a prediction using the trained model
-    prediction = model.predict(input_with_coords)
-
-    label = f"Accident Prediction: {classes[int(prediction[0])]}"
+    label = f"Score Prediction: {(prediction[0])}"
     return label
 
+
 # Create the Gradio interface
-title = "Accident Prediction"
-description = "Predict the severity of an accident based on input features."
+title = "T20i Score Prediction"
+description = "Predict the score of a T20i match."
 output_label = [gr.Label(num_top_classes=4)]
 gr.Interface(
     fn=predict_accident,