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app.py

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+
+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()

requirements.txt

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+streamlit
+tensorflow
+numpy
+opencv-python
+opencv-python
+torch
+torchvision
+matplotlib