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app/main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import pickle
+import pandas as pd
+import numpy as np
+import xgboost
+from xgboost import XGBRegressor
+
+# Load the pre-trained pipeline
+pipe = pickle.load(open('pipe.pkl', 'rb'))
+
+# Define the list of teams and cities
+teams = ['Australia', 'India', 'Bangladesh', 'New Zealand', 'South Africa', 'England',
+         'West Indies', 'Afghanistan', 'Pakistan', 'Sri Lanka']
+
+cities = ['Colombo', 'Mirpur', 'Johannesburg', 'Dubai', 'Auckland', 'Cape Town', 'London',
+          'Pallekele', 'Barbados', 'Sydney', 'Melbourne', 'Durban', 'St Lucia', 'Wellington',
+          'Lauderhill', 'Hamilton', 'Centurion', 'Manchester', 'Abu Dhabi', 'Mumbai',
+          'Nottingham', 'Southampton', 'Mount Maunganui', 'Chittagong', 'Kolkata', 'Lahore',
+          'Delhi', 'Nagpur', 'Chandigarh', 'Adelaide', 'Bangalore', 'St Kitts', 'Cardiff',
+          'Christchurch', 'Trinidad']
+
+# Initialize FastAPI
+app = FastAPI()
+
+# Define the request model
+class ScorePredictionRequest(BaseModel):
+    batting_team: str
+    bowling_team: str
+    city: str
+    current_score: int
+    overs: float
+    wickets: int
+    last_five: int
+
+# Define the response model
+class ScorePredictionResponse(BaseModel):
+    predicted_score: int
+
+# Ensure input values are valid
+def validate_input(data: ScorePredictionRequest):
+    if data.batting_team not in teams:
+        raise HTTPException(status_code=400, detail="Invalid batting team")
+    if data.bowling_team not in teams:
+        raise HTTPException(status_code=400, detail="Invalid bowling team")
+    if data.city not in cities:
+        raise HTTPException(status_code=400, detail="Invalid city")
+    if not (0 <= data.overs <= 20):
+        raise HTTPException(status_code=400, detail="Overs must be between 0 and 20")
+    if not (0 <= data.wickets <= 10):
+        raise HTTPException(status_code=400, detail="Wickets must be between 0 and 10")
+    if data.current_score < 0:
+        raise HTTPException(status_code=400, detail="Current score must be non-negative")
+    if data.last_five < 0:
+        raise HTTPException(status_code=400, detail="Runs scored in last 5 overs must be non-negative")
+
+# Define the prediction endpoint
+@app.post("/predict", response_model=ScorePredictionResponse)
+def predict_score(data: ScorePredictionRequest):
+    # Validate input
+    validate_input(data)
+
+    # Calculate additional features
+    balls_left = 120 - (data.overs * 6)
+    wickets_left = 10 - data.wickets
+    crr = data.current_score / data.overs if data.overs > 0 else 0
+
+    # Create input dataframe for the model
+    input_df = pd.DataFrame(
+        {'batting_team': [data.batting_team], 'bowling_team': [data.bowling_team], 'city': [data.city],
+         'current_score': [data.current_score], 'balls_left': [balls_left], 'wickets_left': [wickets_left],
+         'crr': [crr], 'last_five': [data.last_five]}
+    )
+
+    # Predict the score
+    result = pipe.predict(input_df)
+    predicted_score = int(result[0])
+
+    # Return the prediction
+    return ScorePredictionResponse(predicted_score=predicted_score)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

app/pipe.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4cf3d0d97c298361ae8c0539456bfb5896aa499a0b00ed17ffabb2b19501a245
+size 29835654