Update app.py

app.py

@@ -4,78 +4,90 @@ from PIL import Image
 import requests
 from io import BytesIO
 
-# This is a placeholder for your image classification function
-def classify_image1(image):
-    pipe1 = pipeline("image-classification", "SolubleFish/swin_transformer-finetuned-eurosat")
-    return pipe1(image)
-def classify_image2(image):
-    pipe2 = pipeline("image-classification", "SolubleFish/image_classification_convnext")
-    return pipe2(image)
-def classify_image3(image):
-    pipe3 = pipeline("image-classification", "SolubleFish/image_classification_vit")
-    return pipe3(image)
 
 # Title
 st.title("Image Classification Web App")
+st.markdown("This app uses Hugging Face's 'transformers' library to classify images using pre-trained models. The app uses three different models for image classification: swin, convnext and vit. Please select a model to classify the image you put on the left sidebar.")
 
 # Intro
-st.write("Please provide a Satellite image for classification")
+st.sidebar.markdown("**Please provide a Satellite image for classification**")
 
 # Image input via URL
-url = st.text_input("Image URL")
+url = st.sidebar.text_input("Image URL")
 if url:
     try:
         response = requests.get(url)
         image = Image.open(BytesIO(response.content))
-        st.image(image, caption='Uploaded Image', use_column_width=True)
+        st.sidebar.image(image, caption='Uploaded Image', use_column_width=True)
     except Exception as e:
-        st.write("Invalid URL. Please enter a valid URL for an image.")
+        st.sidebar.error("Invalid URL. Please enter a valid URL for an image.")
 
-# Image input via file uploader
-uploaded_file = st.file_uploader("Or upload an image", type=["jpg", "png"])
+# Image input via file uploader on the sidebar (but display image on the main page)
+uploaded_file = st.sidebar.file_uploader("Or upload an image", type=["jpg", "png"])
 if uploaded_file is not None:
     image = Image.open(uploaded_file)
     st.image(image, caption='Uploaded Image', use_column_width=True)
 
+# Documentation about the 3 models
+st.sidebar.markdown("## Find more information about the model architecture at the link below :  ")
+st.sidebar.markdown("*Vision Transformer (ViT)* https://huggingface.co/docs/transformers/main/en/model_doc/vit")
+st.sidebar.markdown("*ConvNext Transformer* https://huggingface.co/docs/transformers/main/en/model_doc/convnext")
+st.sidebar.markdown("*Swin Transformer* https://huggingface.co/docs/transformers/main/en/model_doc/swin")
+
+# Image classification function
+
+def classify_image1(image):
+    pipe1 = pipeline("image-classification", "SolubleFish/swin_transformer-finetuned-eurosat", token=access_token)
+    return pipe1(image)
+def classify_image2(image):
+    pipe2 = pipeline("image-classification", "SolubleFish/image_classification_convnext", token=access_token)
+    return pipe2(image)
+def classify_image3(image):
+    pipe3 = pipeline("image-classification", "SolubleFish/image_classification_vit", token=access_token)
+    return pipe3(image)
+
 
 # Create three columns
 col1, col2, col3 = st.columns(3)
 
 # Classification button for classify_image1
-if col1.button("Classify Image by swin"):
+if col1.button("Classify Image by Swin"):
     if url or uploaded_file:
         results = classify_image1(image)
         if results:
             # Use markdown to present the results
             for result in results:
-                col1.markdown(f"**Class name:** {result['label']} \n\n **Confidence:** {str(format(result['score']*100, '.2f'))}"+"%")
+                col1.markdown(f"Class name: **{result['label']}** \n\n Confidence: **{str(format(result['score']*100, '.2f'))}**"+"%")
+            col1.success("Classification completed.")
         else:
-            col1.write("No results found.")
+            col1.error("No results found.")
     else:
-        col1.write("Please provide an image for classification.")
+        col1.error("Please provide an image for classification.")
 
 # Classification button for classify_image2
-if col2.button("Classify Image by convnext"):
+if col2.button("Classify Image by ConvNext"):
     if url or uploaded_file:
         results = classify_image2(image)
         if results:
             # Use markdown to present the results
             for result in results:
-                col2.markdown(f"**Class name:** {result['label']} \n\n **Confidence:** {str(format(result['score']*100, '.2f'))}"+"%")
+                col2.markdown(f"Class name: **{result['label']}** \n\n Confidence: **{str(format(result['score']*100, '.2f'))}**"+"%")
+            col2.success("Classification completed.")
         else:
-            col2.write("No results found.")
+            col2.error("No results found.")
     else:
-        col2.write("Please provide an image for classification.")
+        col2.error("Please provide an image for classification.")
 
 # Classification button for classify_image3
-if col3.button("Classify Image by vit"):
+if col3.button("Classify Image by ViT"):
     if url or uploaded_file:
         results = classify_image3(image)
         if results:
             # Use markdown to present the results
             for result in results:
-                col3.markdown(f"**Class name:** {result['label']} \n\n **Confidence:** {str(format(result['score']*100, '.2f'))}"+"%")
+                col3.markdown(f"Class name: **{result['label']}** \n\n Confidence: **{str(format(result['score']*100, '.2f'))}**"+"%")
+            col3.success("Classification completed.")
         else:
-            col3.write("No results found.")
+            col3.error("No results found.")
     else:
-        col3.write("Please provide an image for classification.")
+        col3.error("Please provide an image for classification.")