Create app.py

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import requests
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load the pre-trained model
+model_path = 'dog_breed.h5'  # Update with your model path
+model = load_model(model_path)
+
+# Dictionary to map index to breed name
+breed_names = {
+    0: 'Beagle', 1: 'Boxer', 2: 'Bulldog', 3: 'Dachshund', 4: 'German Shepherd',
+    5: 'Golden Retriever', 6: 'Labrador Retriever', 7: 'Poodle', 8: 'Rottweiler', 9: 'Yorkshire Terrier'
+}
+
+# Function to preprocess the image
+def preprocess_image(image):
+    image = image.resize((150, 150))
+    img_array = np.array(image)
+    img_array = img_array / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+# Function to classify the breed
+def classify_breed(image, model):
+    img_array = preprocess_image(image)
+    predictions = model.predict(img_array)
+    predicted_class_index = np.argmax(predictions)
+    return breed_names[predicted_class_index]
+
+# Function to fetch breed information from an API
+def fetch_breed_info(breed_name):
+    url = f'https://api.thedogapi.com/v1/breeds/search?q={breed_name}'
+    response = requests.get(url)
+    if response.status_code == 200:
+        breed_info = response.json()
+        return breed_info
+    else:
+        return None
+
+# API route for prediction
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    try:
+        # Check file type
+        if file.content_type not in ["image/jpeg", "image/png", "image/jpg"]:
+            raise HTTPException(status_code=400, detail="Invalid file type. Only JPG and PNG are allowed.")
+
+        # Read and process the image
+        image = Image.open(file.file)
+
+        # Classify the breed
+        breed_name = classify_breed(image, model)
+
+        # Fetch breed information
+        breed_info = fetch_breed_info(breed_name)
+
+        # Prepare response
+        response = {
+            "predicted_breed": breed_name,
+            "breed_info": breed_info[0] if breed_info else "No additional information available."
+        }
+        return JSONResponse(content=response)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Root route
+@app.get("/")
+def read_root():
+    return {"message": "Welcome to the Dog Breed Classification API!"}