Update app.py

app.py

@@ -16,171 +16,171 @@
 
 
 
-# import streamlit as st
-# from tensorflow.keras.models import load_model
-# from tensorflow.keras.preprocessing import image
-# import numpy as np
-# from PIL import Image
+import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
 
-# # Load the pre-trained models
-# @st.cache_resource
-# def load_models():
-#     model1 = load_model('name_model_inception.h5')  # Update with your Hugging Face model path
-#     model2 = load_model('type_model_inception.h5')  # Update with your Hugging Face model path
-#     return model1, model2
+# Load the pre-trained models
+@st.cache_resource
+def load_models():
+    model1 = load_model('name_model_inception.h5')  # Update with your Hugging Face model path
+    model2 = load_model('type_model_inception.h5')  # Update with your Hugging Face model path
+    return model1, model2
 
-# model1, model2 = load_models()
+model1, model2 = load_models()
 
-# # Label mappings
-# label_map1 = {
-#     0: "Banana", 1: "Cucumber", 2: "Grape", 3: "Kaki", 4: "Papaya",
-#     5: "Peach", 6: "Pear", 7: "Pepper", 8: "Strawberry", 9: "Watermelon", 10: "Tomato"
-# }
+# Label mappings
+label_map1 = {
+    0: "Banana", 1: "Cucumber", 2: "Grape", 3: "Kaki", 4: "Papaya",
+    5: "Peach", 6: "Pear", 7: "Pepper", 8: "Strawberry", 9: "Watermelon", 10: "Tomato"
+}
 
-# label_map2 = {
-#     0: "Good", 1: "Mild", 2: "Rotten"
-# }
+label_map2 = {
+    0: "Good", 1: "Mild", 2: "Rotten"
+}
 
-# # Streamlit app layout
-# st.title("Fruit Classifier")
+# Streamlit app layout
+st.title("Fruit Classifier")
 
-# # Upload image
-# uploaded_file = st.file_uploader("Choose an image of a fruit", type=["jpg", "jpeg", "png"])
+# Upload image
+uploaded_file = st.file_uploader("Choose an image of a fruit", type=["jpg", "jpeg", "png"])
 
-# if uploaded_file is not None:
-#     # Display the uploaded image
-#     img = Image.open(uploaded_file)
-#     st.image(img, caption="Uploaded Image", use_column_width=True)
+if uploaded_file is not None:
+    # Display the uploaded image
+    img = Image.open(uploaded_file)
+    st.image(img, caption="Uploaded Image", use_column_width=True)
     
-#     # Preprocess the image
-#     img = img.resize((224, 224))  # Resize image to match the model input
-#     img_array = image.img_to_array(img)
-#     img_array = np.expand_dims(img_array, axis=0)
-#     img_array = img_array / 255.0  # Normalize the image
+    # Preprocess the image
+    img = img.resize((224, 224))  # Resize image to match the model input
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0  # Normalize the image
 
-#     # Make predictions
-#     pred1 = model1.predict(img_array)
-#     pred2 = model2.predict(img_array)
+    # Make predictions
+    pred1 = model1.predict(img_array)
+    pred2 = model2.predict(img_array)
 
-#     predicted_class1 = np.argmax(pred1, axis=1)
-#     predicted_class2 = np.argmax(pred2, axis=1)
+    predicted_class1 = np.argmax(pred1, axis=1)
+    predicted_class2 = np.argmax(pred2, axis=1)
 
-#     # Display results
-#     st.write(f"**Type Detection**: {label_map1[predicted_class1[0]]}")
-#     st.write(f"**Condition Detection**: {label_map2[predicted_class2[0]]}")
+    # Display results
+    st.write(f"**Type Detection**: {label_map1[predicted_class1[0]]}")
+    st.write(f"**Condition Detection**: {label_map2[predicted_class2[0]]}")
 
 
 
 
-!git clone 'https://github.com/facebookresearch/detectron2'
-dist = distutils.core.run_setup("./detectron2/setup.py")
-!python -m pip install {' '.join([f"'{x}'" for x in dist.install_requires])}
-sys.path.insert(0, os.path.abspath('./detectron2'))
+# !git clone 'https://github.com/facebookresearch/detectron2'
+# dist = distutils.core.run_setup("./detectron2/setup.py")
+# !python -m pip install {' '.join([f"'{x}'" for x in dist.install_requires])}
+# sys.path.insert(0, os.path.abspath('./detectron2'))
 
 
 
-import streamlit as st
-import numpy as np
-import cv2
-import warnings
-
-# 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
-    from detectron2.engine import DefaultPredictor
-    from detectron2.config import get_cfg
-    from detectron2.utils.visualizer import Visualizer
-    from detectron2.data import MetadataCatalog
-except ImportError:
-    st.error("Failed to import PyTorch or Detectron2. Please make sure they're installed correctly.")
-
-# Load the trained models
-try:
-    model_path_name = 'name_model_inception.h5'
-    model_path_quality = 'type_model_inception.h5'
-    detectron_config_path = 'watermelon.yaml'
-    detectron_weights_path = 'Watermelon_model.pth'
-
-    model_name = load_model(model_path_name)
-    model_quality = load_model(model_path_quality)
-except Exception as e:
-    st.error(f"Failed to load models: {str(e)}")
-
-# Streamlit app title
-st.title("Watermelon Quality and Damage Detection")
+# import streamlit as st
+# import numpy as np
+# import cv2
+# import warnings
+
+# # 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
+#     from detectron2.engine import DefaultPredictor
+#     from detectron2.config import get_cfg
+#     from detectron2.utils.visualizer import Visualizer
+#     from detectron2.data import MetadataCatalog
+# except ImportError:
+#     st.error("Failed to import PyTorch or Detectron2. Please make sure they're installed correctly.")
+
+# # Load the trained models
+# try:
+#     model_path_name = 'name_model_inception.h5'
+#     model_path_quality = 'type_model_inception.h5'
+#     detectron_config_path = 'watermelon.yaml'
+#     detectron_weights_path = 'Watermelon_model.pth'
+
+#     model_name = load_model(model_path_name)
+#     model_quality = load_model(model_path_quality)
+# except Exception as e:
+#     st.error(f"Failed to load models: {str(e)}")
+
+# # Streamlit app title
+# st.title("Watermelon Quality and Damage Detection")
 
-# Upload image
-uploaded_file = st.file_uploader("Choose a watermelon image...", type=["jpg", "jpeg", "png"])
+# # Upload image
+# uploaded_file = st.file_uploader("Choose a watermelon image...", type=["jpg", "jpeg", "png"])
 
-if uploaded_file is not None:
-    try:
-        # Load the image
-        img = image.load_img(uploaded_file, target_size=(224, 224))
-        img_array = image.img_to_array(img)
-        img_array = np.expand_dims(img_array, axis=0)
-        img_array /= 255.0
-
-        # Display uploaded image
-        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
-
-        # Predict watermelon name
-        pred_name = model_name.predict(img_array)
-        predicted_name = 'Watermelon'
-
-        # Predict watermelon quality
-        pred_quality = model_quality.predict(img_array)
-        predicted_class_quality = np.argmax(pred_quality, axis=1)
-
-        # Define labels for watermelon quality
-        label_map_quality = {
-            0: "Good",
-            1: "Mild",
-            2: "Rotten"
-        }
-
-        predicted_quality = label_map_quality[predicted_class_quality[0]]
-
-        # Display predictions
-        st.write(f"Fruit Type Detection: {predicted_name}")
-        st.write(f"Fruit Quality Classification: {predicted_quality}")
-
-        # If the quality is 'Mild' or 'Rotten', pass the image to the mask detection model
-        if predicted_quality in ["Mild", "Rotten"]:
-            st.write("Passing the image to the mask detection model for damage detection...")
-
-            # Load the image again for the mask detection (Detectron2 requires the original image)
-            im = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
-
-            # Setup Detectron2 configuration for watermelon
-            cfg = get_cfg()
-            cfg.merge_from_file(detectron_config_path)
-            cfg.MODEL.WEIGHTS = detectron_weights_path
-            cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
-            cfg.MODEL.DEVICE = 'cpu'  # Use CPU for inference
-
-            predictor = DefaultPredictor(cfg)
-            predictor.model.load_state_dict(torch.load(detectron_weights_path, map_location=torch.device('cpu')))
-
-            # Run prediction on the image
-            outputs = predictor(im)
-
-            # Visualize the predictions
-            v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=0.8)
-            out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-
-            # Display the output
-            st.image(out.get_image()[:, :, ::-1], caption="Detected Damage", use_column_width=True)
-
-    except Exception as e:
-        st.error(f"An error occurred during processing: {str(e)}")
+# if uploaded_file is not None:
+#     try:
+#         # Load the image
+#         img = image.load_img(uploaded_file, target_size=(224, 224))
+#         img_array = image.img_to_array(img)
+#         img_array = np.expand_dims(img_array, axis=0)
+#         img_array /= 255.0
+
+#         # Display uploaded image
+#         st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+#         # Predict watermelon name
+#         pred_name = model_name.predict(img_array)
+#         predicted_name = 'Watermelon'
+
+#         # Predict watermelon quality
+#         pred_quality = model_quality.predict(img_array)
+#         predicted_class_quality = np.argmax(pred_quality, axis=1)
+
+#         # Define labels for watermelon quality
+#         label_map_quality = {
+#             0: "Good",
+#             1: "Mild",
+#             2: "Rotten"
+#         }
+
+#         predicted_quality = label_map_quality[predicted_class_quality[0]]
+
+#         # Display predictions
+#         st.write(f"Fruit Type Detection: {predicted_name}")
+#         st.write(f"Fruit Quality Classification: {predicted_quality}")
+
+#         # If the quality is 'Mild' or 'Rotten', pass the image to the mask detection model
+#         if predicted_quality in ["Mild", "Rotten"]:
+#             st.write("Passing the image to the mask detection model for damage detection...")
+
+#             # Load the image again for the mask detection (Detectron2 requires the original image)
+#             im = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
+
+#             # Setup Detectron2 configuration for watermelon
+#             cfg = get_cfg()
+#             cfg.merge_from_file(detectron_config_path)
+#             cfg.MODEL.WEIGHTS = detectron_weights_path
+#             cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
+#             cfg.MODEL.DEVICE = 'cpu'  # Use CPU for inference
+
+#             predictor = DefaultPredictor(cfg)
+#             predictor.model.load_state_dict(torch.load(detectron_weights_path, map_location=torch.device('cpu')))
+
+#             # Run prediction on the image
+#             outputs = predictor(im)
+
+#             # Visualize the predictions
+#             v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=0.8)
+#             out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+
+#             # Display the output
+#             st.image(out.get_image()[:, :, ::-1], caption="Detected Damage", use_column_width=True)
+
+#     except Exception as e:
+#         st.error(f"An error occurred during processing: {str(e)}")