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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}")
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