Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+import requests
+from io import BytesIO
+
+# This is a placeholder for your image classification function
+def classify_image(image):
+    pipe = pipeline("image-classification", "SolubleFish/swin-tiny-patch4-window7-224-finetuned-eurosat")
+    return pipe(image)
+
+# Title
+st.title("Image Classification Web App")
+
+# Intro
+st.write("Please provide a Satellite image for classification")
+
+# Image input via URL
+url = st.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)
+    except Exception as e:
+        st.write("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"])
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+# Classification button
+if st.button("Classify Image"):
+    if url or uploaded_file:
+        results = classify_image(image)
+        if results:
+            # Use markdown to present the results
+            for result in results:
+                st.markdown(f"**Class name:** {result['label']} \n\n **Confidence:** {str(format(result['score']*100, '.2f'))}"+"%")
+        else:
+            st.write("No results found.")
+    else:
+        st.write("Please provide an image for classification.")
+