Update app.py

app.py

@@ -1,117 +1,119 @@
-import streamlit as st
-from transformers import pipeline
-from PIL import Image
+# import streamlit as st
+# from transformers import pipeline
+# from PIL import Image
 
-MODEL_1 = "google/vit-base-patch16-224"
-MIN_ACEPTABLE_SCORE = 0.1
-MAX_N_LABELS = 5
-MODEL_2 = "nateraw/vit-age-classifier"
-MODELS = [
-            "google/efficientnet-b0",
-            "google/vit-base-patch16-224", #Classifição geral
-            "nateraw/vit-age-classifier", #Classifição de idade
-            "microsoft/resnet-50", #Classifição geral
-            "Falconsai/nsfw_image_detection", #Classifição NSFW
-            "cafeai/cafe_aesthetic", #Classifição de estética
-            "microsoft/resnet-18", #Classifição geral
-            "microsoft/resnet-34", #Classifição geral escolhida pelo copilot 
-            "microsoft/resnet-101", #Classifição geral escolhida pelo copilot 
-            "microsoft/resnet-152", #Classifição geral escolhida pelo copilot
-            "microsoft/swin-tiny-patch4-window7-224",#Classifição geral
-            "-- Reinstated on testing--",
-            "microsoft/beit-base-patch16-224-pt22k-ft22k", #Classifição geral
-            "-- New --",
-            "-- Still in the testing process --",
-            "facebook/convnext-large-224", #Classifição geral
-            "timm/resnet50.a1_in1k", #Classifição geral
-            "timm/mobilenetv3_large_100.ra_in1k", #Classifição geral
-            "trpakov/vit-face-expression", #Classifição de expressão facial
-            "rizvandwiki/gender-classification", #Classifição de gênero
-            "#q-future/one-align",  #Classifição geral
-            "LukeJacob2023/nsfw-image-detector", #Classifição NSFW   
-            "vit-base-patch16-224-in21k", #Classifição geral
-            "not-lain/deepfake", #Classifição deepfake
-            "carbon225/vit-base-patch16-224-hentai", #Classifição hentai    
-            "facebook/convnext-base-224-22k-1k", #Classifição geral
-            "facebook/convnext-large-224", #Classifição geral
-            "facebook/convnext-tiny-224",#Classifição geral
-            "nvidia/mit-b0", #Classifição geral
-            "microsoft/resnet-18", #Classifição geral
-            "microsoft/swinv2-base-patch4-window16-256", #Classifição geral
-            "andupets/real-estate-image-classification", #Classifição de imóveis
-            "timm/tf_efficientnetv2_s.in21k", #Classifição geral
-            "timm/convnext_tiny.fb_in22k",
-            "DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition", #Classifição de atividade humana
-            "FatihC/swin-tiny-patch4-window7-224-finetuned-eurosat-watermark", #Classifição geral
-            "aalonso-developer/vit-base-patch16-224-in21k-clothing-classifier", #Classifição de roupas
-            "RickyIG/emotion_face_image_classification", #Classifição de emoções
-            "shadowlilac/aesthetic-shadow" #Classifição de estética
-        ]
+print("hello")
 
-def classify(image, model):
-    classifier = pipeline("image-classification", model=model)
-    result= classifier(image)
-    return result
+# MODEL_1 = "google/vit-base-patch16-224"
+# MIN_ACEPTABLE_SCORE = 0.1
+# MAX_N_LABELS = 5
+# MODEL_2 = "nateraw/vit-age-classifier"
+# MODELS = [
+#             "google/efficientnet-b0",
+#             "google/vit-base-patch16-224", #Classifição geral
+#             "nateraw/vit-age-classifier", #Classifição de idade
+#             "microsoft/resnet-50", #Classifição geral
+#             "Falconsai/nsfw_image_detection", #Classifição NSFW
+#             "cafeai/cafe_aesthetic", #Classifição de estética
+#             "microsoft/resnet-18", #Classifição geral
+#             "microsoft/resnet-34", #Classifição geral escolhida pelo copilot 
+#             "microsoft/resnet-101", #Classifição geral escolhida pelo copilot 
+#             "microsoft/resnet-152", #Classifição geral escolhida pelo copilot
+#             "microsoft/swin-tiny-patch4-window7-224",#Classifição geral
+#             "-- Reinstated on testing--",
+#             "microsoft/beit-base-patch16-224-pt22k-ft22k", #Classifição geral
+#             "-- New --",
+#             "-- Still in the testing process --",
+#             "facebook/convnext-large-224", #Classifição geral
+#             "timm/resnet50.a1_in1k", #Classifição geral
+#             "timm/mobilenetv3_large_100.ra_in1k", #Classifição geral
+#             "trpakov/vit-face-expression", #Classifição de expressão facial
+#             "rizvandwiki/gender-classification", #Classifição de gênero
+#             "#q-future/one-align",  #Classifição geral
+#             "LukeJacob2023/nsfw-image-detector", #Classifição NSFW   
+#             "vit-base-patch16-224-in21k", #Classifição geral
+#             "not-lain/deepfake", #Classifição deepfake
+#             "carbon225/vit-base-patch16-224-hentai", #Classifição hentai    
+#             "facebook/convnext-base-224-22k-1k", #Classifição geral
+#             "facebook/convnext-large-224", #Classifição geral
+#             "facebook/convnext-tiny-224",#Classifição geral
+#             "nvidia/mit-b0", #Classifição geral
+#             "microsoft/resnet-18", #Classifição geral
+#             "microsoft/swinv2-base-patch4-window16-256", #Classifição geral
+#             "andupets/real-estate-image-classification", #Classifição de imóveis
+#             "timm/tf_efficientnetv2_s.in21k", #Classifição geral
+#             "timm/convnext_tiny.fb_in22k",
+#             "DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition", #Classifição de atividade humana
+#             "FatihC/swin-tiny-patch4-window7-224-finetuned-eurosat-watermark", #Classifição geral
+#             "aalonso-developer/vit-base-patch16-224-in21k-clothing-classifier", #Classifição de roupas
+#             "RickyIG/emotion_face_image_classification", #Classifição de emoções
+#             "shadowlilac/aesthetic-shadow" #Classifição de estética
+#         ]
 
-def save_result(result):
-    st.write("In the future, this function will save the result in a database.")
+# def classify(image, model):
+#     classifier = pipeline("image-classification", model=model)
+#     result= classifier(image)
+#     return result
 
-def print_result(result):
+# def save_result(result):
+#     st.write("In the future, this function will save the result in a database.")
 
-    comulative_discarded_score = 0
-    for i in range(len(result)):
-        if result[i]['score'] < MIN_ACEPTABLE_SCORE:
-            comulative_discarded_score += result[i]['score']
-        else:
-            st.write(result[i]['label'])
-            st.progress(result[i]['score'])
-            st.write(result[i]['score'])
+# def print_result(result):
 
-    st.write(f"comulative_discarded_score:")
-    st.progress(comulative_discarded_score)
-    st.write(comulative_discarded_score)
+#     comulative_discarded_score = 0
+#     for i in range(len(result)):
+#         if result[i]['score'] < MIN_ACEPTABLE_SCORE:
+#             comulative_discarded_score += result[i]['score']
+#         else:
+#             st.write(result[i]['label'])
+#             st.progress(result[i]['score'])
+#             st.write(result[i]['score'])
+
+#     st.write(f"comulative_discarded_score:")
+#     st.progress(comulative_discarded_score)
+#     st.write(comulative_discarded_score)
     
 
 
-def main():
-    st.title("Image Classification")
-    st.write("This is a simple web app to test and compare different image classifier models using Hugging Face's image-classification pipeline.")
-    st.write("From time to time more models will be added to the list. If you want to add a model, please open an issue on the GitHub repository.")
-    st.write("If you like this project, please consider liking it or buying me a coffee. It will help me to keep working on this and other projects. Thank you!")
+# def main():
+#     st.title("Image Classification")
+#     st.write("This is a simple web app to test and compare different image classifier models using Hugging Face's image-classification pipeline.")
+#     st.write("From time to time more models will be added to the list. If you want to add a model, please open an issue on the GitHub repository.")
+#     st.write("If you like this project, please consider liking it or buying me a coffee. It will help me to keep working on this and other projects. Thank you!")
 
-    # Buy me a Coffee Setup
-    bmc_link = "https://www.buymeacoffee.com/nuno.tome" 
-    # image_url = "https://helloimjessa.files.wordpress.com/2021/06/bmc-button.png?w=150" # Image URL
-    image_url = "https://i.giphy.com/RETzc1mj7HpZPuNf3e.webp" # Image URL
+#     # Buy me a Coffee Setup
+#     bmc_link = "https://www.buymeacoffee.com/nuno.tome" 
+#     # image_url = "https://helloimjessa.files.wordpress.com/2021/06/bmc-button.png?w=150" # Image URL
+#     image_url = "https://i.giphy.com/RETzc1mj7HpZPuNf3e.webp" # Image URL
         
-    image_size = "150px" # Image size
-    #image_link_markdown = f"<img src='{image_url}' width='25%'>"
-    image_link_markdown = f"[![Buy Me a Coffee]({image_url})]({bmc_link})"
+#     image_size = "150px" # Image size
+#     #image_link_markdown = f"<img src='{image_url}' width='25%'>"
+#     image_link_markdown = f"[![Buy Me a Coffee]({image_url})]({bmc_link})"
 
-    #image_link_markdown = f"[![Buy Me a Coffee]({image_url})]({bmc_link})" # Create a clickable image link
+#     #image_link_markdown = f"[![Buy Me a Coffee]({image_url})]({bmc_link})" # Create a clickable image link
 
-    st.markdown(image_link_markdown, unsafe_allow_html=True) # Display the image link
-    # Buy me a Coffee Setup
+#     st.markdown(image_link_markdown, unsafe_allow_html=True) # Display the image link
+#     # Buy me a Coffee Setup
     
-    #st.markdown("<img src='https://helloimjessa.files.wordpress.com/2021/06/bmc-button.png?w=1024' width='15%'>", unsafe_allow_html=True)
+#     #st.markdown("<img src='https://helloimjessa.files.wordpress.com/2021/06/bmc-button.png?w=1024' width='15%'>", unsafe_allow_html=True)
   
-    input_image = st.file_uploader("Upload Image")
-    shosen_model = st.selectbox("Select the model to use",  MODELS)
+#     input_image = st.file_uploader("Upload Image")
+#     shosen_model = st.selectbox("Select the model to use",  MODELS)
     
     
-    if input_image is not None:
-        image_to_classify = Image.open(input_image)
-        st.image(image_to_classify, caption="Uploaded Image")
-        if st.button("Classify"):
-            image_to_classify = Image.open(input_image)
-            classification_obj1 =[]
-            #avable_models = st.selectbox
+#     if input_image is not None:
+#         image_to_classify = Image.open(input_image)
+#         st.image(image_to_classify, caption="Uploaded Image")
+#         if st.button("Classify"):
+#             image_to_classify = Image.open(input_image)
+#             classification_obj1 =[]
+#             #avable_models = st.selectbox
             
-            classification_result = classify(image_to_classify, shosen_model)
-            classification_obj1.append(classification_result)
-            print_result(classification_result)
-            save_result(classification_result)
+#             classification_result = classify(image_to_classify, shosen_model)
+#             classification_obj1.append(classification_result)
+#             print_result(classification_result)
+#             save_result(classification_result)
 
 
-if __name__ == "__main__":
-    main()
+# if __name__ == "__main__":
+#     main()