Upload folder using huggingface_hub

Dockerfile

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

app.py

@@ -3,7 +3,10 @@ import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
 
-app = Flask(__name__)
+# Initialize Flask app for ExtraaLearn
+extraalearn_api = Flask(__name__)
+
+# Load the model
 model = joblib.load('Extraalearn.joblib')
 
 EXPECTED_FEATURES = [
@@ -16,11 +19,11 @@ EXPECTED_FEATURES = [
     'educational_channels_Yes', 'referral_Yes'
 ]
 
-@app.get('/')
+@extraalearn_api.get('/')
 def home():
-    return 'ExtraaLearn API is Running'
+    return 'ExtraaLearn Lead Conversion API is Running'
 
-@app.post('/v1/predict')
+@extraalearn_api.post('/v1/predict')
 def predict():
     try:
         data = request.get_json()
@@ -41,6 +44,7 @@ def predict():
         p['digital_media_Yes'] = 1 if data.get('digital_media') == 'Yes' else 0
         p['educational_channels_Yes'] = 1 if data.get('educational_channels') == 'Yes' else 0
         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
@@ -48,4 +52,4 @@ def predict():
         return jsonify({'error': str(e)}), 400
 
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860)
+    extraalearn_api.run(host='0.0.0.0', port=7860)