intial docker deployment

Dockerfile

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+# Use official Python image
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the app
+COPY . .
+
+# Expose the port HF Spaces uses
+EXPOSE 7860
+
+# Run the Flask app
+CMD ["python", "app.py"]

app.py

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+import os
+import json
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import io
+from huggingface_hub import hf_hub_download
+
+# Initialize Flask app
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes
+
+# Load model and class indices
+working_dir = os.path.dirname(os.path.abspath(__file__))
+#model_path = os.path.join(working_dir, "trained_model", "plant_disease_model.tflite")
+model_path = hf_hub_download(
+    repo_id="sidd-harth011/checkingPDRMod",  # ✅ your repo
+    filename="plant_disease_model.tflite"
+)
+
+# Load the TFLite model
+interpreter = tf.lite.Interpreter(model_path=model_path)
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Load class indices
+class_indices_path = os.path.join(working_dir, "class_indices.json")
+with open(class_indices_path, 'r') as f:
+    class_indices = json.load(f)
+
+# -----------------------------
+# Preprocessing function
+# -----------------------------
+def load_and_preprocess_image(image, target_size=(224, 224)):
+    img = image.resize(target_size)
+    img_array = np.array(img, dtype=np.float32)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
+    return img_array
+
+# -----------------------------
+# Function to clean label
+# -----------------------------
+def clean_label(label: str) -> str:
+    if "___" in label:
+        label = label.split("___")[-1]
+    return label.replace("_", " ").title()
+
+# -----------------------------
+# Prediction function
+# -----------------------------
+def predict_image_class(image):
+    preprocessed_img = load_and_preprocess_image(image)
+    interpreter.set_tensor(input_details[0]['index'], preprocessed_img)
+    interpreter.invoke()
+    predictions = interpreter.get_tensor(output_details[0]['index'])
+    predicted_class_index = np.argmax(predictions, axis=1)[0]
+    predicted_class_name = class_indices[str(predicted_class_index)]
+    predicted_class_name = clean_label(predicted_class_name)
+    
+    # Get confidence score
+    confidence = float(predictions[0][predicted_class_index])
+    
+    return predicted_class_name, confidence
+
+# -----------------------------
+# API endpoint for image classification
+# -----------------------------
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Check if image is in the request
+        if 'image' not in request.files:
+            return jsonify({'error': 'No image provided'}), 400
+        
+        # Get the image file
+        image_file = request.files['image']
+        
+        # Check if filename is empty
+        if image_file.filename == '':
+            return jsonify({'error': 'No image selected'}), 400
+        
+        # Read and process the image
+        image = Image.open(io.BytesIO(image_file.read()))
+        
+        # Make prediction
+        predicted_class, confidence = predict_image_class(image)
+        
+        # Return prediction as JSON
+        return jsonify({
+            'prediction': predicted_class,
+            'confidence': confidence,
+            'status': 'success'
+        })
+        
+    except Exception as e:
+        return jsonify({'error': str(e), 'status': 'error'}), 500
+
+# -----------------------------
+# Health check endpoint
+# -----------------------------
+@app.route('/health', methods=['GET'])
+def health_check():
+    return jsonify({'status': 'healthy', 'message': 'Plant Disease Classification API is running'})
+
+# -----------------------------
+# Run the Flask app
+# -----------------------------
+if __name__ == '__main__':
+    # You can change the host and port as needed
+    app.run(host='0.0.0.0', port=7860, debug=False)

class_indices.json

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+{"0": "Apple___Apple_scab", "1": "Apple___Black_rot", "2": "Apple___Cedar_apple_rust", "3": "Apple___healthy", "4": "Blueberry___healthy", "5": "Cherry_(including_sour)___Powdery_mildew", "6": "Cherry_(including_sour)___healthy", "7": "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", "8": "Corn_(maize)___Common_rust_", "9": "Corn_(maize)___Northern_Leaf_Blight", "10": "Corn_(maize)___healthy", "11": "Grape___Black_rot", "12": "Grape___Esca_(Black_Measles)", "13": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", "14": "Grape___healthy", "15": "Orange___Haunglongbing_(Citrus_greening)", "16": "Peach___Bacterial_spot", "17": "Peach___healthy", "18": "Pepper,_bell___Bacterial_spot", "19": "Pepper,_bell___healthy", "20": "Potato___Early_blight", "21": "Potato___Late_blight", "22": "Potato___healthy", "23": "Raspberry___healthy", "24": "Soybean___healthy", "25": "Squash___Powdery_mildew", "26": "Strawberry___Leaf_scorch", "27": "Strawberry___healthy", "28": "Tomato___Bacterial_spot", "29": "Tomato___Early_blight", "30": "Tomato___Late_blight", "31": "Tomato___Leaf_Mold", "32": "Tomato___Septoria_leaf_spot", "33": "Tomato___Spider_mites Two-spotted_spider_mite", "34": "Tomato___Target_Spot", "35": "Tomato___Tomato_Yellow_Leaf_Curl_Virus", "36": "Tomato___Tomato_mosaic_virus", "37": "Tomato___healthy"}

requirements.txt

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+tensorflow
+numpy
+flask
+flask-cors
+pillow
+huggingface-hub