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Dockerfile

@@ -2,5 +2,4 @@ FROM python:3.9-slim
 WORKDIR /app
 COPY . .
 RUN pip install --no-cache-dir -r requirements.txt
-# Update CMD to point to extraalearn_api instead of superkart_api
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:extraalearn_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]

app.py

@@ -3,8 +3,8 @@ import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
 
-# Initialize Flask app for ExtraaLearn
-extraalearn_api = Flask(__name__)
+# Initialize Flask app
+app = Flask(__name__)
 
 # Load the model
 model = joblib.load('Extraalearn.joblib')
@@ -19,11 +19,11 @@ EXPECTED_FEATURES = [
     'educational_channels_Yes', 'referral_Yes'
 ]
 
-@extraalearn_api.get('/')
+@app.get('/')
 def home():
     return 'ExtraaLearn Lead Conversion API is Running'
 
-@extraalearn_api.post('/v1/predict')
+@app.post('/v1/predict')
 def predict():
     try:
         data = request.get_json()
@@ -46,10 +46,10 @@ def predict():
         p['referral_Yes'] = 1 if data.get('referral') == 'Yes' else 0
 
         df_final = pd.DataFrame([p])[EXPECTED_FEATURES]
-        prediction_proba = model.predict_proba(df_final)[:, 1].tolist()[0] # Use predict_proba
-        return jsonify({'Conversion_Probability': float(prediction_proba)}) # Changed key to Conversion_Probability
+        prediction_proba = model.predict_proba(df_final)[:, 1].tolist()[0]
+        return jsonify({'Conversion_Probability': float(prediction_proba)})
     except Exception as e:
         return jsonify({'error': str(e)}), 400
 
 if __name__ == '__main__':
-    extraalearn_api.run(host='0.0.0.0', port=7860)
+    app.run(host='0.0.0.0', port=7860)

requirements.txt

@@ -1,14 +1,9 @@
-writefile backend_files/requirements.txt
 pandas==2.2.2
 numpy==2.0.2
 scikit-learn==1.6.1
 seaborn==0.13.2
 joblib==1.4.2
 xgboost==2.1.4
-joblib==1.4.2
-Werkzeug==2.2.2
 flask==2.2.2
 gunicorn==20.1.0
 requests==2.32.3
-uvicorn[standard]
-streamlit==1.43.2