Create app.py

app.py

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+from fastapi import FastAPI, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from io import BytesIO
+
+app = FastAPI()
+
+# Enable CORS to allow requests from frontend (React)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Change ["http://localhost:5173"] for better security
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load your model
+model = load_model("densenet201_food_classification.h5")
+
+# Define class indices
+class_indices = {
+    0: "burger",
+    1: "butter_naan",
+    2: "chai",
+    3: "chapati",
+    4: "chole_bhature",
+    5: "dal_makhani",
+    6: "dhokla",
+    7: "fried_rice",
+    8: "idli",
+    9: "jalebi",
+    10: "kaathi_rolls",
+    11: "kadai_paneer",
+    12: "kulfi",
+    13: "masala_dosa",
+    14: "momos",
+    15: "paani_puri",
+    16: "pakode",
+    17: "pav_bhaji",
+    18: "pizza",
+    19: "samosa"
+}
+
+def predict_image(image, model):
+    try:
+        img = load_img(image, target_size=(224, 224))
+        image_array = img_to_array(img) / 255.0
+        image_array = np.expand_dims(image_array, axis=0)
+
+        predictions = model.predict(image_array)
+        class_idx = np.argmax(predictions)
+        class_label = class_indices.get(class_idx, "Unknown")
+        confidence = float(predictions[0][class_idx])
+
+        return class_label, confidence
+    except Exception as e:
+        return None, None
+
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    try:
+        image_data = await file.read()
+        image = BytesIO(image_data)
+
+        class_label, confidence = predict_image(image, model)
+
+        if class_label is None:
+            return {"error": "Prediction failed"}
+
+        return {"predicted_class": class_label, "confidence": f"{confidence:.2f}"}
+    except Exception as e:
+        return {"error": f"Internal Server Error: {str(e)}"}