init!

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import io
+import time
+
+# Hugging Face Transformers specific imports
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load pre-trained model and tokenizer
+model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+def rotate_image(image, angle):
+    """
+    Rotate the given PIL image by the given angle
+    """
+    return image.rotate(angle, expand=True)
+
+def main():
+    st.title("Hugging Face Streamlit Example: Rotating Image")
+
+    # Upload an image
+    st.sidebar.title("Upload Image")
+    uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    # Rotation speed adjustment
+    rotation_speed = st.sidebar.slider("Rotation Speed", min_value=0, max_value=360, value=10, step=1)
+
+    if uploaded_file is not None:
+        # Display the image
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Rotate the image at a constant speed
+        st.write("Rotating image...")
+        rotation_angle = 0
+        while True:
+            rotated_image = rotate_image(image, rotation_angle)
+            st.image(rotated_image, caption="Rotated Image", use_column_width=True)
+            time.sleep(0.1)  # Adjust rotation speed
+            rotation_angle += rotation_speed
+            rotation_angle %= 360  # Keep angle within 0-359 range
+
+if __name__ == "__main__":
+    main()