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.gitattributes

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 *.h5 filter=lfs diff=lfs merge=lfs -text
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.gitignore

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-myenv

README.md

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 ---
-title: Potato Leaf Disease Detection
-emoji: 🥔
-colorFrom: green
-colorTo: blue
+title: Potato
+emoji: 🚀
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.13.0
+sdk_version: 5.29.0
 app_file: app.py
 pinned: false
 ---
 
-# Potato Leaf Disease Detection
-
-This model detects diseases in potato leaves, classifying them into three categories:
-- Early Blight
-- Late Blight
-- Healthy
-
-## Usage
-
-### Web Interface
-The model is available through a Gradio web interface. Simply run:
-```bash
-python app.py
-```
-Then open your browser to `http://localhost:7860`
-
-### API Usage
-You can also use the model through an API endpoint:
-
-```python
-import requests
-
-# Replace with your Hugging Face Space URL
-API_URL = "https://your-space-url.hf.space/predict"
-
-# Load your image
-with open("path_to_image.jpg", "rb") as f:
-    image_data = f.read()
-
-# Make prediction
-response = requests.post(API_URL, files={"file": image_data})
-prediction = response.json()["prediction"]
-print(f"Prediction: {prediction}")
-```
-
-## Model Details
-- Input: 256x256 RGB image of a potato leaf
-- Output: Classification into one of three categories
-- Model: TensorFlow/Keras model
-
-## Requirements
-See `requirements.txt` for all dependencies. 
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,61 +1,66 @@
 import gradio as gr
-from fastapi import FastAPI, File, UploadFile
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
 import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
 from PIL import Image
-import io
-import uvicorn
-import tensorflow as tf
-
-# Configure TensorFlow to use CPU only
-tf.config.set_visible_devices([], 'GPU')
-
-# Load model with custom_objects to handle version compatibility
-MODEL_PATH = "best_model.keras"
-try:
-    model = load_model(MODEL_PATH, compile=False)
-except Exception as e:
-    print(f"Error loading model: {str(e)}")
-    # Try loading with custom_objects
-    model = load_model(MODEL_PATH, compile=False, custom_objects={'tf': tf})
-
-class_names = ["Early Blight", "Late Blight", "Healthy"]
-
-# Preprocess image
-def preprocess(img):
-    if isinstance(img, str):  # Handle file path
-        img = Image.open(img)
-    elif isinstance(img, bytes):  # Handle bytes
-        img = Image.open(io.BytesIO(img))
-    
-    img = img.resize((256, 256))
-    img_array = image.img_to_array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-    prediction = model.predict(img_array, verbose=0)  # Added verbose=0 to reduce output
-    class_index = np.argmax(prediction[0])
-    return class_names[class_index]
-
-# Create FastAPI app
-app = FastAPI(title="Potato Leaf Disease Detection API")
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    contents = await file.read()
-    prediction = preprocess(contents)
-    return {"prediction": prediction}
+import requests
+import os
+
+# Constants
+MODEL_URL = "https://storage.googleapis.com/potato-model-detection/potato_disease_model.h5"
+MODEL_PATH = "potato_disease_model.h5"
+CLASS_NAMES = ["Early Blight", "Late Blight", "Healthy"]
+
+def download_model():
+    """Download the model if not already available locally."""
+    if not os.path.exists(MODEL_PATH):
+        print("Downloading model...")
+        response = requests.get(MODEL_URL)
+        if response.status_code == 200:
+            with open(MODEL_PATH, 'wb') as f:
+                f.write(response.content)
+            print("Model downloaded successfully.")
+        else:
+            raise Exception(f"Failed to download model: HTTP {response.status_code}")
+
+def load_disease_model(path):
+    """Load the trained Keras model."""
+    try:
+        return load_model(path)
+    except Exception as e:
+        raise RuntimeError(f"Error loading model: {e}")
+
+def preprocess_image(image):
+    """Convert and resize image for model input."""
+    image = image.convert("RGB").resize((256, 256))
+    img_array = img_to_array(image) / 255.0
+    return np.expand_dims(img_array, axis=0)
+
+def predict(image):
+    """Make a prediction and return class probabilities."""
+    try:
+        img_array = preprocess_image(image)
+        prediction = model.predict(img_array)[0]
+
+        # Normalize if not already
+        if not np.isclose(np.sum(prediction), 1.0, atol=1e-2):
+            prediction = prediction / np.sum(prediction)
+
+        return {CLASS_NAMES[i]: round(float(pred), 4) for i, pred in enumerate(prediction)}
+    except Exception as e:
+        return {"error": str(e)}
+
+# Ensure model is ready
+download_model()
+model = load_disease_model(MODEL_PATH)
 
 # Gradio Interface
 interface = gr.Interface(
-    fn=preprocess,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(),
-    title="Potato Leaf Disease Detection",
-    description="Upload a potato leaf image to classify as Early Blight, Late Blight, or Healthy."
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Leaf Image"),
+    outputs=gr.Label(num_top_classes=3, label="Prediction"),
+    title="otato Leaf Disease Detection",
+    description="Upload an image of a potato leaf to detect Early Blight, Late Blight, or Healthy condition.",
 )
 
-# Mount Gradio app
-app = gr.mount_gradio_app(app, interface, path="/")
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+interface.launch()

potato_disease_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a0ea2599967005ff4ddec606ab9693d2823ccb4cd7cfe33bc293887d1c060ba6
+size 712308576