app.py

import streamlit as st
import numpy as np
import cv2
import warnings
import os

# Suppress warnings
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)

# Try importing TensorFlow
try:
    from tensorflow.keras.models import load_model
    from tensorflow.keras.preprocessing import image
except ImportError:
    st.error("Failed to import TensorFlow. Please make sure it's installed correctly.")

# Try importing PyTorch and Detectron2
try:
    import torch
    import detectron2
except ImportError:
    with st.spinner("Installing PyTorch and Detectron2..."):
        os.system("pip install torch torchvision")
        os.system("pip install 'git+https://github.com/facebookresearch/detectron2.git'")

    import torch
    import detectron2


import streamlit as st
import numpy as np
import cv2
import torch
import os
from PIL import Image
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog

# Suppress warnings
import warnings
import tensorflow as tf
warnings.filterwarnings("ignore")
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)

@st.cache_resource
def load_models():
    model_name = load_model('name_model_inception.h5')
    model_quality = load_model('type_model_inception.h5')
    return model_name, model_quality

model_name, model_quality = load_models()

# Detectron2 setup
@st.cache_resource
def load_detectron_model(fruit_name):
    cfg = get_cfg()
    config_path = os.path.join(f"{fruit_name.lower()}_config.yaml")
    cfg.merge_from_file(config_path)
    model_path = os.path.join(f"{fruit_name}_model.pth")
    cfg.MODEL.WEIGHTS = model_path
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
    cfg.MODEL.DEVICE = 'cpu'
    predictor = DefaultPredictor(cfg)
    return predictor, cfg

# Labels
label_map_name = {
    0: "Banana", 1: "Cucumber", 2: "Grape", 3: "Kaki", 4: "Papaya",
    5: "Peach", 6: "Pear", 7: "Peeper", 8: "Strawberry", 9: "Watermelon",
    10: "tomato"
}
label_map_quality = {0: "Good", 1: "Mild", 2: "Rotten"}

def predict_fruit(img):
    # Preprocess image
    img = Image.fromarray(img.astype('uint8'), 'RGB')
    img = img.resize((224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = x / 255.0

    # Predict
    pred_name = model_name.predict(x)
    pred_quality = model_quality.predict(x)

    predicted_name = label_map_name[np.argmax(pred_name, axis=1)[0]]
    predicted_quality = label_map_quality[np.argmax(pred_quality, axis=1)[0]]

    return predicted_name, predicted_quality, img

def main():
    st.title("Automated Fruits Monitoring  System")
    st.write("Upload an image of a fruit to detect its type, quality, and potential damage.")

    uploaded_file = st.file_uploader("Choose a fruit image...", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Image", use_column_width=True)

        if st.button("Analyze"):
            predicted_name, predicted_quality, img = predict_fruit(np.array(image))

            st.write(f"Fruits Type Detection:  {predicted_name}")
            st.write(f"Fruits Quality Classification:  {predicted_quality}")

            if predicted_name.lower() in ["kaki", "tomato", "strawberry", "peeper", "pear", "peach", "papaya", "watermelon", "grape", "banana", "cucumber"] and predicted_quality in ["Mild", "Rotten"]:
                st.write("Segmentation of Defective Region:")
                try:
                    predictor, cfg = load_detectron_model(predicted_name)
                    outputs = predictor(cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR))
                    v = Visualizer(np.array(img), MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=0.8)
                    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
                    st.image(out.get_image(), caption="Damage Detection Result", use_column_width=True)
                except Exception as e:
                    st.error(f"Error in damage detection: {str(e)}")
            else:
                st.write("No damage detection performed for this fruit or quality level.")

if __name__ == "__main__":
    main()