Update app.py

app.py

@@ -1,345 +1,3 @@
-# import streamlit as st
-
-# # Set title of the app
-# st.title("Simple Streamlit App")
-
-# # Add text input
-# user_input = st.text_input("Enter your name:")
-
-# # Display the input value
-# if user_input:
-#     st.write(f"Hello, {user_input}!")
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-# 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
-
-# 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_map2 = {
-#     0: "Good", 1: "Mild", 2: "Rotten"
-# }
-
-# # Streamlit app layout
-# st.title("Fruit Classifier")
-
-# # 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)
-    
-#     # 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)
-
-#     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]]}")
-
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-
-
-# !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'))
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-# 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"])
-
-# 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)
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-#     except Exception as e:
-#         st.error(f"An error occurred during processing: {str(e)}")
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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
-
-# from detectron2.engine import DefaultPredictor
-# from detectron2.config import get_cfg
-# from detectron2.utils.visualizer import Visualizer
-# from detectron2.data import MetadataCatalog
-
-# # Load the trained models
-# @st.cache_resource
-# def load_models():
-#     try:
-#         model_path_name = 'name_model_inception.h5'
-#         model_path_quality = 'type_model_inception.h5'
-#         model_name = load_model(model_path_name)
-#         model_quality = load_model(model_path_quality)
-#         return model_name, model_quality
-#     except Exception as e:
-#         st.error(f"Failed to load models: {str(e)}")
-#         return None, None
-
-# model_name, model_quality = load_models()
-
-# # 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"])
-
-# 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
-#             @st.cache_resource
-#             def load_detectron_model():
-#                 cfg = get_cfg()
-#                 cfg.merge_from_file("watermelon.yaml")
-#                 cfg.MODEL.WEIGHTS = "Watermelon_model.pth"
-#                 cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
-#                 cfg.MODEL.DEVICE = 'cpu'  # Use CPU for inference
-#                 predictor = DefaultPredictor(cfg)
-#                 return predictor
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-#             predictor = load_detectron_model()
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-#             # 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)}")
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-# ///////////////////////////////////Working
-
 
 import streamlit as st
 import numpy as np
@@ -370,219 +28,6 @@ except ImportError:
     import torch
     import detectron2
 
-from detectron2.engine import DefaultPredictor
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog
-
-# # Load the trained models
-# @st.cache_resource
-# def load_models():
-#     try:
-#         model_path_name = 'name_model_inception.h5'
-#         model_path_quality = 'type_model_inception.h5'
-#         model_name = load_model(model_path_name)
-#         model_quality = load_model(model_path_quality)
-#         return model_name, model_quality
-#     except Exception as e:
-#         st.error(f"Failed to load models: {str(e)}")
-#         return None, None
-
-# model_name, model_quality = load_models()
-
-# # Setup Detectron2 configuration for watermelon
-# @st.cache_resource
-# def load_detectron_model():
-#     cfg = get_cfg()
-#     cfg.merge_from_file("watermelon.yaml")
-#     cfg.MODEL.WEIGHTS = "Watermelon_model.pth"
-#     cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
-#     cfg.MODEL.DEVICE = 'cpu'  # Use CPU for inference
-#     predictor = DefaultPredictor(cfg)
-#     return predictor, cfg
-
-# predictor, cfg = load_detectron_model()
-
-# # 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"])
-
-# 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
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-#         # 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)
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-#         # Define labels for watermelon quality
-#         label_map_quality = {
-#             0: "Good",
-#             1: "Mild",
-#             2: "Rotten"
-#         }
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-#         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...")
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-#             # Load the image again for the mask detection (Detectron2 requires the original image)
-#             im = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
-
-#             # 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)
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-#     except Exception as e:
-#         st.error(f"An error occurred during processing: {str(e)}")
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-# import streamlit as st
-# import numpy as np
-# import cv2
-# import torch
-# 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()
-#     cfg.merge_from_file(f"{fruit_name.lower()}.yaml")
-#     cfg.MODEL.WEIGHTS = f"{fruit_name.lower()}_model.pth"
-#     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: "Pepper", 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("Fruit Quality and Damage Detection")
-#     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"Fruit Type: {predicted_name}")
-#             st.write(f"Fruit Quality: {predicted_quality}")
-
-#             if predicted_name.lower() in ["kaki", "tomato", "strawberry", "pepper", "pear", "peach", "papaya", "watermelon", "grape", "banana", "cucumber"] and predicted_quality in ["Mild", "Rotten"]:
-#                 st.write("Detecting damage...")
-#                 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)
-#             else:
-#                 st.write("No damage detection performed for this fruit or quality level.")
-
-# if __name__ == "__main__":
-#     main()
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 import streamlit as st
 import numpy as np
@@ -628,7 +73,7 @@ def load_detectron_model(fruit_name):
 label_map_name = {
     0: "Banana", 1: "Cucumber", 2: "Grape", 3: "Kaki", 4: "Papaya",
     5: "Peach", 6: "Pear", 7: "Pepper", 8: "Strawberry", 9: "Watermelon",
-    10: "Tomato"
+    10: "tomato"
 }
 label_map_quality = {0: "Good", 1: "Mild", 2: "Rotten"}
 
@@ -650,7 +95,7 @@ def predict_fruit(img):
     return predicted_name, predicted_quality, img
 
 def main():
-    st.title("Fruit Quality and Damage Detection")
+    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"])
@@ -662,11 +107,11 @@ def main():
         if st.button("Analyze"):
             predicted_name, predicted_quality, img = predict_fruit(np.array(image))
 
-            st.write(f"Fruit Type: {predicted_name}")
-            st.write(f"Fruit Quality: {predicted_quality}")
+            st.write(f"Fruits Type Detection:  {predicted_name}")
+            st.write(f"Fruits Quality Classification:  {predicted_quality}")
 
             if predicted_name.lower() in ["kaki", "tomato", "strawberry", "pepper", "pear", "peach", "papaya", "watermelon", "grape", "banana", "cucumber"] and predicted_quality in ["Mild", "Rotten"]:
-                st.write("Detecting damage...")
+                st.write("Segmentation of Defective Region:")
                 try:
                     predictor, cfg = load_detectron_model(predicted_name)
                     outputs = predictor(cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR))