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app.py

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+import streamlit as st
+import replicate
+import os
+import requests
+from PIL import Image
+from io import BytesIO
+
+# Set up environment variable for Replicate API Token
+os.environ['REPLICATE_API_TOKEN'] = 'r8_3V5WKOBwbbuL0DQGMliP0972IAVIBo62Lmi8I'  # Replace with your actual API token
+
+def upscale_image(image_path):
+    # Open the image file
+    with open(image_path, "rb") as img_file:
+        # Run the GFPGAN model
+        output = replicate.run(
+            "tencentarc/gfpgan:9283608cc6b7be6b65a8e44983db012355fde4132009bf99d976b2f0896856a3",
+            input={"img": img_file, "version": "v1.4", "scale": 16}
+        )
+        
+        # The output is a URI of the processed image
+        # We will retrieve the image data and save it
+        response = requests.get(output)
+        img = Image.open(BytesIO(response.content))
+        img.save("upscaled.png")  # Save the upscaled image
+        return img
+
+def main():
+    st.title("Image Upscaling")
+    st.write("Upload an image and it will be upscaled.")
+
+    uploaded_file = st.file_uploader("Choose an image...", type="png")
+    if uploaded_file is not None:
+        with open("temp_img.png", "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        st.success("Uploaded image successfully!")
+        if st.button("Upscale Image"):
+            img = upscale_image("temp_img.png")
+            st.image(img, caption='Upscaled Image', use_column_width=True)
+
+if __name__ == "__main__":
+    main()

main.py

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+import streamlit as st
+import replicate
+import os
+import requests
+from PIL import Image
+from io import BytesIO
+
+# Set up environment variable for Replicate API Token
+os.environ['REPLICATE_API_TOKEN'] = 'r8_3V5WKOBwbbuL0DQGMliP0972IAVIBo62Lmi8I'  # Replace with your actual API token
+
+def upscale_image(image_path):
+    # Open the image file
+    with open(image_path, "rb") as img_file:
+        # Run the GFPGAN model
+        output = replicate.run(
+            "tencentarc/gfpgan:9283608cc6b7be6b65a8e44983db012355fde4132009bf99d976b2f0896856a3",
+            input={"img": img_file, "version": "v1.4", "scale": 16}
+        )
+        
+        # The output is a URI of the processed image
+        # We will retrieve the image data and save it
+        response = requests.get(output)
+        img = Image.open(BytesIO(response.content))
+        img.save("upscaled.png")  # Save the upscaled image
+        return img
+
+def main():
+    st.title("Image Upscaling")
+    st.write("Upload an image and it will be upscaled.")
+
+    uploaded_file = st.file_uploader("Choose an image...", type="png")
+    if uploaded_file is not None:
+        with open("temp_img.png", "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        st.success("Uploaded image successfully!")
+        if st.button("Upscale Image"):
+            img = upscale_image("temp_img.png")
+            st.image(img, caption='Upscaled Image', use_column_width=True)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+Pillow
+requests
+replicate