Add Backend

Backend/Leaf-Disease-Detection/main.py

+from fastapi import FastAPI, File, UploadFile
+import firebase_admin
+from firebase_admin import credentials, storage
+import uvicorn
+import numpy as np
+from PIL import Image
+import requests
+from io import BytesIO
+import tensorflow as tf
+from urllib.parse import unquote
+from fastapi.middleware.cors import CORSMiddleware
+
+
+app = FastAPI()
+
+cred = credentials.Certificate("serviceAccountKey.json")
+firebase_admin.initialize_app(cred, {"storageBucket": "rp-project-d2045.appspot.com"})
+bucket = storage.bucket()
+
+
+def upload_to_firebase(file: UploadFile) -> str:
+    # Read the uploaded image
+    image_bytes = file.file.read()
+
+    # Resize the image to 256x256
+    image = Image.open(BytesIO(image_bytes))
+    image = image.resize((256, 256))
+
+    # Convert the image back to bytes
+    image_bytes_resized = BytesIO()
+    image.save(image_bytes_resized, format="JPEG")
+    image_bytes_resized = image_bytes_resized.getvalue()
+
+    # Set the path and upload the resized image to Firebase
+    image_path = f"images/{file.filename}"
+    blob = bucket.blob(image_path)
+    blob.upload_from_string(image_bytes_resized, content_type=file.content_type)
+    image_url = blob.public_url
+    return image_url
+
+
+
+def read_file_as_image(url) -> np.ndarray:
+    response = requests.get(url)
+    try:
+        image = Image.open(BytesIO(response.content))
+    except Exception as e:
+        print(f"Error while opening image: {e}")
+        return None
+    return np.array(image)
+
+
+MODEL_PATH_1 = "Models/1/VGG16.h5"  # Update with the correct model path
+MODEL_PATH_2 = "Models/2/VGG19.h5"  # Update with the correct model path for model 2
+CLASS_NAMES_1 = ['Diseases','Helthy','undefined'  ]
+CLASS_NAMES_2 = ['Anthracnose', 'Bacterial Blight', 'Cercospora Leaf Spot']
+
+model_1 = tf.keras.models.load_model(MODEL_PATH_1)
+model_2 = tf.keras.models.load_model(MODEL_PATH_2)
+
+@app.get("/")
+async def ping():
+    return {"message": "Ping successful"}
+
+@app.post("/predict")
+async def predict_disease(
+        file: UploadFile = File(...)
+):
+    image_url = upload_to_firebase(file)
+    image = read_file_as_image(unquote(image_url))
+
+    if image is None:
+        return {"error": "Failed to load image"}
+
+    img_batch = np.expand_dims(image, 0)
+
+    predictions_1 = model_1.predict(img_batch)
+    predicted_class_1 = CLASS_NAMES_1[np.argmax(predictions_1[0])]
+    confidence_1 = np.max(predictions_1[0])
+
+    if predicted_class_1 == "Diseases":
+        predictions_2 = model_2.predict(img_batch)
+        predicted_class_2 = CLASS_NAMES_2[np.argmax(predictions_2[0])]
+        confidence_2 = np.max(predictions_2[0])
+
+        return {
+            'class_model_1': predicted_class_1,
+            'confidence_model_1': float(confidence_1),
+            'class_model_2': predicted_class_2,
+            'confidence_model_2': float(confidence_2)
+        }
+
+    else:
+        return {
+            'class_model_1': predicted_class_1,
+            'confidence_model_1': float(confidence_1)
+        }
+
+
+
+# CORS middleware configuration
+origins = [
+    "http://localhost:8081",
+    "http://localhost:3000",
+    "http://localhost:8088", # Replace this with your frontend's domain
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host='192.168.140.107', port=8000)
\ No newline at end of file

Backend/Leaf-Disease-Detection/requirements.txt

 absl-py==1.4.0

Backend/Leaf-Disease-Detection/serviceAccountkey.json

+{
+  "type": "service_account",
+  "project_id": "rp-project-d2045",
+  "private_key_id": "1afcdfd65ccfbfad05701b64473f28d58ffbb67b",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\n3Yr3MT3i2GgRx3g7zfB1FQTL\n-----END PRIVATE KEY-----\n",
+  "client_email": "firebase-adminsdk-nz7m9@rp-project-d2045.iam.gserviceaccount.com",
+  "client_id": "113165482898063166799",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-nz7m9%40rp-project-d2045.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}