Update app.py

app.py

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+import altair as alt
+import pandas as pd
+import streamlit as st
+from PIL import Image
+import cv2
+import numpy as np
+import io
+import zipfile
+
+st.title("Image Augmentation App")
+uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_image:
+    img = np.array(Image.open(uploaded_image))
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+    
+    st.write("**Applying augmentations...**")
+    
+    transformations = {
+        "Translated": cv2.warpAffine(img, np.float32([[1, 0, 50], [0, 1, 50]]), (img.shape[1], img.shape[0])),
+        "Scaled": cv2.resize(img, None, fx=1.2, fy=1.2),
+        "Rotated": cv2.warpAffine(img, cv2.getRotationMatrix2D((img.shape[1] // 2, img.shape[0] // 2), 45, 1), (img.shape[1], img.shape[0])),
+        "Cropped": img[50:img.shape[0] - 50, 50:img.shape[1] - 50]
+    }
+    
+    augmented_images = []
+    zip_buffer = io.BytesIO()
+    
+    with zipfile.ZipFile(zip_buffer, "w") as zipf:
+        for i, (name, transformed_img) in enumerate(transformations.items()):
+            img_pil = Image.fromarray(cv2.cvtColor(transformed_img, cv2.COLOR_BGR2RGB))
+            img_bytes = io.BytesIO()
+            img_pil.save(img_bytes, format="JPEG")
+            zipf.writestr(f"aug_{i + 1}_{name}.jpg", img_bytes.getvalue())
+            augmented_images.append(img_pil)
+    
+    st.write("**Augmentation completed!**")
+    st.image(augmented_images, width=150)
+    
+    zip_buffer.seek(0)
+    st.download_button("Download Augmented Images", data=zip_buffer, file_name="augmented_images.zip", mime="application/zip")
+