add code

Dockerfile

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+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy files
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app ./app
+
+# Run the FastAPI server using Uvicorn
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

LICENSE

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+MIT License
+
+Copyright (c) 2025 Shahzad Ali
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+provided to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app/main.py

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+from tensorflow import keras
+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+import numpy as np
+from io import BytesIO
+from PIL import Image
+import logging
+from huggingface_hub import hf_hub_download
+logging.basicConfig(level=logging.DEBUG)
+
+app = FastAPI()
+
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model_path = hf_hub_download(repo_id="ShahzadAli44/rice_cnn", filename="rice_cnn_model.keras")
+
+
+try:
+    MODEL = keras.saving.load_model(model_path)
+    logging.info("Model loaded successfully.")
+except Exception as e:
+    logging.error(f"Error loading model: {e}")
+    MODEL = None
+
+CLASS_NAMES = [
+    "bacterial_leaf_blight", "brown_spot", "healthy", "leaf_blast",
+    "leaf_scald", "narrow_brown_spot", "rice_hispa", "sheath_blight", "tungro"
+]
+
+DISEASE_DETAILS = {
+    "bacterial_leaf_blight": {
+        "symptoms": "Leaves turn yellow with wet-looking spots that spread and dry out.",
+        "treatment": "Use disease-resistant rice seeds. Spray copper-based fungicides. Apply balanced fertilizers with nitrogen and phosphorus."
+    },
+    "brown_spot": {
+        "symptoms": "Small brown spots appear on leaves, which later turn yellow.",
+        "treatment": "Use potassium-rich fertilizers. Spray Mancozeb fungicide. Maintain good drainage to avoid water stress."
+    },
+    "healthy": {
+        "symptoms": "Leaves are green and strong, with no signs of disease.",
+        "treatment": "No treatment needed. Keep the soil healthy by using compost and balanced fertilizers."
+    },
+    "leaf_blast": {
+        "symptoms": "Leaves get white or gray spots that spread and kill the leaf.",
+        "treatment": "Spray Tricyclazole fungicide. Keep the right water level in the field. Use silica-based fertilizers to strengthen plants."
+    },
+    "leaf_scald": {
+        "symptoms": "Leaf edges turn yellow or brown, and the leaf dries up.",
+        "treatment": "Use resistant rice varieties. Avoid too much nitrogen fertilizer. Spray Propiconazole fungicide if needed."
+    },
+    "narrow_brown_spot": {
+        "symptoms": "Thin, dark brown streaks appear on leaves.",
+        "treatment": "Reduce plant overcrowding. Spray Propiconazole fungicide. Use potassium and phosphorus fertilizers to improve plant health."
+    },
+    "rice_hispa": {
+        "symptoms": "Leaves get white scars and small holes due to insect feeding.",
+        "treatment": "Remove infected leaves. Spray Chlorpyrifos insecticide. Keep fields clean to reduce insect attacks."
+    },
+    "sheath_blight": {
+        "symptoms": "White or gray patches appear on the lower part of the plant, leading to weak stems.",
+        "treatment": "Keep enough space between plants. Apply Azoxystrobin fungicide. Use compost and phosphorus-rich fertilizers."
+    },
+    "tungro": {
+        "symptoms": "Plants grow slowly, and leaves turn yellow or orange.",
+        "treatment": "Use virus-free seedlings. Spray insecticides like Imidacloprid to control pests. Apply nitrogen fertilizers to strengthen plants."
+    }
+}
+
+
+def read_file_as_image(data) -> np.ndarray:
+    try:
+        image = Image.open(BytesIO(data)).convert("RGB")  
+        logging.debug(f"Image size: {image.size}")
+        return np.array(image)
+    except Exception as e:
+        logging.error("Error reading image file: %s", str(e))
+        raise ValueError("Invalid image data")
+
+
+@app.get("/")
+def home():
+    return {"message": "Agrico API is live!"}
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    if MODEL is None:
+        return {"error": "Model is not loaded properly."}
+
+    try:
+        image_data = await file.read()
+        image = read_file_as_image(image_data)
+        img_batch = np.expand_dims(image, 0)
+        logging.debug(f"Image batch shape: {img_batch.shape}")
+        
+        predictions = MODEL.predict(img_batch)
+        logging.debug(f"Predictions: {predictions}")
+
+        predicted_class = CLASS_NAMES[np.argmax(predictions[0])]
+        confidence = np.max(predictions[0])
+        if confidence < 0.5:
+            return {"error": "The uploaded image does not appear to be a rice crop leaf."}
+        disease_details = DISEASE_DETAILS.get(predicted_class, {})
+        
+        return {
+            'class': predicted_class,
+            'confidence': float(confidence),
+            'symptoms': disease_details.get("symptoms", "No symptoms available."),
+            'treatment': disease_details.get("treatment", "No treatment information available.")
+        }
+    except Exception as e:
+        logging.error("Error during prediction: %s", str(e))
+        return {"error": "An error occurred while processing the image."}

requirements.txt

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+fastapi
+uvicorn
+numpy
+pillow
+tensorflow-cpu
+huggingface_hub
+python-multipart

vercel.json

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+{
+    "builds": [
+      {
+        "src": "main.py",
+        "use": "@vercel/python"
+      }
+    ],
+    "routes": [
+      {
+        "src": "/(.*)",
+        "dest": "main.py"
+      }
+    ]
+  }