Update app.py

app.py

@@ -1,21 +1,116 @@
+import streamlit as st
 from transformers import pipeline
-import gradio as gr
+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/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
+        ]
 
-generator = pipeline("image-to-image", model="timbrooks/instruct-pix2pix")
-def generate_face_variations(image):
-    variations = generator(image, num_images=4, guidance_scale=7.5)
-    return variations
+def classify(image, model):
+    classifier = pipeline("image-classification", model=model)
+    result= classifier(image)
+    return result
 
-with gr.Blocks() as demo:
-    gr.Markdown("# Face Position Variation Generator")
+def save_result(result):
+    st.write("In the future, this function will save the result in a database.")
+
+def print_result(result):
+
+    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!")
+
+    # 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_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
     
-    with gr.Row():
-        input_image = gr.Image(type="pil")
-        output_gallery = gr.Gallery().style(grid=2, height="auto")
+    #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)
     
-    generate_btn = gr.Button("Generate")
     
-    generate_btn.click(fn=generate_face_variations, inputs=input_image, outputs=output_gallery)
+    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)
+
 
-demo.launch()
+if __name__ == "__main__":
+    main()