Update app.py

app.py

@@ -1,117 +1,33 @@
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import load_img, img_to_array
-import numpy as np
-from flask import Flask, request, render_template, jsonify
-from werkzeug.utils import secure_filename
-import os
+import gradio as gr
+import tensorflow as tf
 from huggingface_hub import hf_hub_download
-from tensorflow import keras
-
-# Initialize Flask app
-app = Flask(__name__)
+from PIL import Image
+import numpy as np
 
 # Load model from Hugging Face Hub
 model_path = hf_hub_download(repo_id="arpitsharrrma/soilnet-model", filename="SoilNet.keras")
+model = tf.keras.models.load_model(model_path)
+
+# Define class labels (adjust if needed)
+class_names = ['Alluvial Soil', 'Black Soil', 'Clay Soil', 'Red Soil', 'Sandy Soil']
+
+def predict_soil(image):
+    image = image.resize((150, 150))  # Model input size
+    img_array = np.array(image) / 255.0
+    img_array = img_array.reshape(1, 150, 150, 3)
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions)]
+    confidence = float(np.max(predictions)) * 100
+    return f"{predicted_class} ({confidence:.2f}% confidence)"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_soil,
+    inputs=gr.Image(type="pil", label="Upload Soil Image"),
+    outputs=gr.Textbox(label="Predicted Soil Type"),
+    title="SoilNet - Soil Type Classifier",
+    description="Upload a soil image and the model will predict the soil type using deep learning."
+)
+
+interface.launch()
 
-SoilNet = keras.models.load_model(model_path)
-
-# Classes dictionary
-classes = {
-    0: "Alluvial Soil:-{ Rice, Wheat, Sugarcane, Maize, Cotton, Soyabean, Jute }",
-    1: "Black Soil:-{ Virginia, Wheat, Jowar, Millets, Linseed, Castor, Sunflower }",
-    2: "Clay Soil:-{ Rice, Lettuce, Chard, Broccoli, Cabbage, Snap Beans }",
-    3: "Red Soil:-{ Cotton, Wheat, Pulses, Millets, Oil Seeds, Potatoes }"
-}
-
-# API Key (set this securely in prod)
-API_KEY = "your-secret-api-key-1234"
-
-# Prediction function
-def model_predict(image_path, model):
-    image = load_img(image_path, target_size=(224, 224))
-    image = img_to_array(image) / 255.0
-    image = np.expand_dims(image, axis=0)
-
-    result = np.argmax(model.predict(image), axis=-1)[0]
-    prediction = classes[result]
-
-    if result == 0:
-        return "Alluvial", "Alluvial.html"
-    elif result == 1:
-        return "Black", "Black.html"
-    elif result == 2:
-        return "Clay", "Clay.html"
-    elif result == 3:
-        return "Red", "Red.html"
-
-# Route: Home (form)
-@app.route('/', methods=['GET'])
-def index():
-    return render_template('index.html')
-
-# Route: Form-based upload + result display
-@app.route('/predict', methods=['POST'])
-def predict():
-    file = request.files.get('image')
-    if not file or file.filename == '':
-        return "No image uploaded", 400
-
-    # Validate extension
-    filename = secure_filename(file.filename)
-    if not filename.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.gif')):
-        return "Unsupported file type", 400
-
-    # Save image
-    upload_folder = os.path.join(os.path.dirname(__file__), 'static', 'user_uploaded')
-    os.makedirs(upload_folder, exist_ok=True)
-    file_path = os.path.join(upload_folder, filename)
-    file.save(file_path)
-
-    # Check image is valid
-    try:
-        _ = load_img(file_path)
-    except Exception as e:
-        os.remove(file_path)
-        return f"Invalid image file: {e}", 400
-
-    pred, output_page = model_predict(file_path, SoilNet)
-    user_image_path = os.path.join('static', 'user_uploaded', filename)
-
-    return render_template(output_page, pred_output=pred, user_image=user_image_path)
-
-# Route: API endpoint with API key
-@app.route('/api/predict', methods=['POST'])
-def api_predict():
-    key = request.headers.get('x-api-key')
-    if key != API_KEY:
-        return jsonify({"error": "Unauthorized"}), 401
-
-    file = request.files.get('image')
-    if not file or file.filename == '':
-        return jsonify({"error": "No image uploaded"}), 400
-
-    filename = secure_filename(file.filename)
-    if not filename.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.gif')):
-        return jsonify({"error": "Unsupported file type"}), 400
-
-    api_temp_folder = os.path.join(os.path.dirname(__file__), 'static', 'api_temp')
-    os.makedirs(api_temp_folder, exist_ok=True)
-    file_path = os.path.join(api_temp_folder, filename)
-    file.save(file_path)
-
-    try:
-        _ = load_img(file_path)
-    except Exception as e:
-        os.remove(file_path)
-        return jsonify({"error": f"Invalid image: {str(e)}"}), 400
-
-    pred, _ = model_predict(file_path, SoilNet)
-    os.remove(file_path)
-
-    return jsonify({"soil_type": pred})
-
-# Start the app
-if __name__ == '__main__':
-    app.run(debug=True)
-
-    app.run(debug=True, threaded=False)