Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import json
+
+# Load class indices
+with open("class_indices.json", "r") as f:
+    class_indices = json.load(f)
+
+# Reverse the mapping for predictions
+class_names = {v: k for k, v in class_indices.items()}
+
+# Load the TFLite model
+interpreter = tf.lite.Interpreter(model_path="model.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Define the image preprocessing function
+def preprocess_image(image, target_size=(224, 224)):
+    image = image.resize(target_size)
+    image = np.array(image) / 255.0  # Normalize the image
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image.astype(np.float32)
+
+# Define prediction function
+def predict(image):
+    input_data = preprocess_image(image)
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+    interpreter.invoke()
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    predicted_class = np.argmax(output_data)
+    confidence = np.max(output_data)
+    return class_names[predicted_class], confidence
+
+# Streamlit UI
+st.title("🌾 Crop Disease Prediction")
+st.write("Upload an image of a crop leaf, and the app will predict the disease (if any).")
+
+uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    st.write("Processing...")
+    
+    # Perform prediction
+    predicted_class, confidence = predict(image)
+    st.write(f"**Prediction:** {predicted_class}")
+    st.write(f"**Confidence:** {confidence:.2f}")