Update main.py

main.py

@@ -1,49 +1,69 @@
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import StreamingResponse
-import os
-import io
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import pandas as pd
+import numpy as np
+import joblib
 
-from transformers import pipeline
 
+# Load your trained model and encoders
+xgb_model = joblib.load("xgb_model.joblib")
+encoders = joblib.load("encoders.joblib")
 
-pipe_flan = pipeline("text2text-generation", model="google/flan-t5-small")
-
-
-
-
-temp = open("model/t.txt","w")
-temp.write("aaaaaaaaaaaaa")
-temp.close()
-
-temp = open("model/t.txt","r")
+# Function to handle unseen labels during encoding
+def safe_transform(encoder, column):
+    classes = encoder.classes_
+    return [encoder.transform([x])[0] if x in classes else -1 for x in column]
 
+# Define FastAPI app
 app = FastAPI()
-
-
-
-
-@app.get("/")
-def read_root():
-    output = pipe_flan("hi")
-    return {"message": str(output[0]["generated_text"])}
-
-@app.post("/uploadfile/")
-async def create_upload_file(file: UploadFile = File(...)):
-    try:
-        # Save the file with a specific name
-        file_path = "inputvoice.mp3"
-
-        with open(file_path, "wb") as f:
-            f.write(file.file.read())
-
-        # Read the content of the saved file
-        with open(file_path, "rb") as f:
-            file_content = f.read()
-
-        # Return the content as a streaming response
-        return StreamingResponse(io.BytesIO(file_content), media_type="audio/mpeg", headers={"Content-Disposition": "inline; filename=inputvoice.mp3"})
-
-    except PermissionError as e:
-        return {"error": f"PermissionError: {str(e)}"}
-
-
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Endpoint for making predictions
+@app.post("/predict")
+def predict(customer_name: str,
+    customer_address: str,
+    customer_phone: str,
+    customer_email: str,
+    cod:str,
+    weight: str,
+    pickup_address: str,
+    origin_city_name: str,
+    destination_city_name: str):
+    # Convert input data to DataFrame
+    input_data = {
+        'customer_name': customer_name,
+        'customer_address': customer_address,
+        'customer_phone': customer_phone,
+        'customer_email': customer_email,
+        'cod': float(cod),
+        'weight': float(weight),
+        'pickup_address':pickup_address,
+        'origin_city.name':origin_city_name,
+        'destination_city.name':destination_city_name
+    }
+    input_df = pd.DataFrame([input_data])
+
+    # Encode categorical variables using the same encoders used during training
+    for col in input_df.columns:
+        if col in encoders:
+            input_df[col] = safe_transform(encoders[col], input_df[col])
+
+    # Predict and obtain probabilities
+    pred = xgb_model.predict(input_df)
+    pred_proba = xgb_model.predict_proba(input_df)
+
+    # Output
+    predicted_status = "Unknown" if pred[0] == -1 else encoders['status.name'].inverse_transform([pred])[0]
+    probability = pred_proba[0][pred[0]] * 100 if pred[0] != -1 else "Unknown"
+
+    if predicted_status == "RETURN TO CLIENT":
+       probability = 100 - probability
+
+    return {"Probability": round(probability,2)}