Create main.py

main.py

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+main.py
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+Update main.py
+48e0e29
+1 day ago
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+import os
+import sys
+
+import uvicorn
+from fastapi import FastAPI, Request, File, UploadFile
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+import pandas as pd
+import numpy as np
+from typing import List
+import joblib
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+# Create an instance of FastAPI
+app = FastAPI(debug=True)
+
+# Load the trained models and transformers
+num_imputer = joblib.load('numerical_imputer.joblib')
+cat_imputer = joblib.load('cat_imputer.joblib')
+encoder = joblib.load('encoder.joblib')
+scaler = joblib.load('scaler.joblib')
+model = joblib.load('lr_model_vif_smote.joblib')
+
+original_feature_names = ['MONTANT', 'FREQUENCE_RECH', 'REVENUE', 'ARPU_SEGMENT', 'FREQUENCE',
+                          'DATA_VOLUME', 'ON_NET', 'ORANGE', 'TIGO', 'ZONE1', 'ZONE2', 'REGULARITY', 'FREQ_TOP_PACK',
+                          'REGION_DAKAR', 'REGION_DIOURBEL', 'REGION_FATICK', 'REGION_KAFFRINE', 'REGION_KAOLACK',
+                          'REGION_KEDOUGOU', 'REGION_KOLDA', 'REGION_LOUGA', 'REGION_MATAM', 'REGION_SAINT-LOUIS',
+                          'REGION_SEDHIOU', 'REGION_TAMBACOUNDA', 'REGION_THIES', 'REGION_ZIGUINCHOR',
+                          'TENURE_Long-term', 'TENURE_Medium-term', 'TENURE_Mid-term', 'TENURE_Short-term',
+                          'TENURE_Very short-term', 'TOP_PACK_data', 'TOP_PACK_international', 'TOP_PACK_messaging',
+                          'TOP_PACK_other_services', 'TOP_PACK_social_media', 'TOP_PACK_value_added_services',
+                          'TOP_PACK_voice']
+
+
+class InputData(BaseModel):
+    MONTANT: float
+    FREQUENCE_RECH: float
+    REVENUE: float
+    ARPU_SEGMENT: float
+    FREQUENCE: float
+    DATA_VOLUME: float
+    ON_NET: float
+    ORANGE: float
+    TIGO: float
+    ZONE1: float
+    ZONE2: float
+    REGULARITY: float
+    FREQ_TOP_PACK: float
+    REGION: str
+    TENURE: str
+    TOP_PACK: str
+
+
+def preprocess_input(input_data):
+    input_df = pd.DataFrame(input_data, index=[0])
+
+    cat_columns = ['REGION', 'TENURE', 'TOP_PACK']
+    num_columns = [col for col in input_df.columns if col not in cat_columns]
+
+    input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns])
+    input_df_imputed_num = num_imputer.transform(input_df[num_columns])
+
+    input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat).toarray(),
+                                    columns=encoder.get_feature_names_out(cat_columns))
+
+    input_df_scaled = scaler.transform(input_df_imputed_num)
+    input_scaled_df = pd.DataFrame(input_df_scaled, columns=num_columns)
+    final_df = pd.concat([input_encoded_df, input_scaled_df], axis=1)
+    final_df = final_df.reindex(columns=original_feature_names, fill_value=0)
+
+    return final_df
+
+
+def make_prediction(data, model):
+    probabilities = model.predict_proba(data)
+    churn_labels = ["No Churn" if class_idx == 0 else "Churn" for class_idx in range(len(probabilities[0]))]
+    churn_probabilities = probabilities[0]
+
+    # Get the predicted churn label and its probability
+    predicted_class_index = np.argmax(churn_probabilities)
+    predicted_churn_label = churn_labels[predicted_class_index]
+    predicted_probability = churn_probabilities[predicted_class_index]
+
+    # Customize the output message based on the predicted churn label and its probability
+    if predicted_churn_label == "Churn":
+        output_message = f"⚠️ Customer is likely to churn with a probability of {predicted_probability:.2f}. This indicates a high risk of losing the customer. ⚠️"
+    else:
+        output_message = f"✅ Customer is not likely to churn with a probability of {predicted_probability:.2f}. This indicates a lower risk of losing the customer. ✅"
+
+    return output_message
+
+@app.get("/")
+def read_root():
+
+    info = """
+    Welcome to the Expressor Churn Prediction API!. This API provides advanced machine learning predictions for churn. ⚡📊 For more information and to explore the API's capabilities, please visit the documentation: https://abubakari-expresso-churn-prediction-fastapi.hf.space/docs
+    """
+    return info.strip()
+
+# Model information endpoint
+@app.post('/model-info')
+async def model_info():
+    model_name = model.__class__.__name__  # get model name
+    model_params = model.get_params()  # get model parameters
+    model_information = {
+        'model info': {
+            'model name': model_name,
+            'model parameters': model_params
+        }
+    }
+    return model_information  # return model information
+
+@app.post('/predict')
+async def predict(input_data: InputData):
+    input_features = input_data.dict()
+    preprocessed_data = preprocess_input(input_features)
+    prediction = make_prediction(preprocessed_data, model)
+
+    return {"prediction": prediction}
+
+
+@app.post('/batch_predict')
+async def predict(input_data: List[InputData]):
+    preprocessed_data = []
+
+    for data in input_data:
+        input_features = data.dict()
+        preprocessed = preprocess_input(input_features)
+        preprocessed_data.append(preprocessed)
+
+    predictions = [make_prediction(data, model) for data in preprocessed_data]
+
+    return {"predictions": predictions}
+