Create app.py

app.py

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+import streamlit as st
+import os
+import requests
+from PIL import Image
+from io import BytesIO
+
+# Set up environment variables for API keys
+os.environ['CLIPDROP_API_KEY'] = '1143a102dbe21628248d4bb992b391a49dc058c584181ea72e17c2ccd49be9ca69ccf4a2b97fc82c89ff1029578abbea'
+os.environ['STABILITY_API_KEY'] = 'sk-GBmsWR78MmCSAWGkkC1CFgWgE6GPgV00pNLJlxlyZWyT3QQO'
+os.environ['REPLICATE_API_TOKEN'] = '1143a102dbe21628248d4bb992b391a49dc058c584181ea72e17c2ccd49be9ca69ccf4a2b97fc82c89ff1029578abbea'
+
+# Importing Replicate and Stability SDK libraries
+import replicate
+import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
+
+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))
+
+        return img
+
+def generate_and_upscale_image(prompt):
+    # Make a POST request to the ClipDrop text-to-image API
+    url = 'https://clipdrop-api.co/text-to-image/v1'
+    headers = {'x-api-key': os.environ['CLIPDROP_API_KEY']}
+    data = {'prompt': prompt}
+    response = requests.post(url, headers=headers, data=data)
+
+    if response.status_code == 200:
+        # Get the generated image from the response
+        img = Image.open(BytesIO(response.content))
+
+        # Upscale the generated image using the Stability API
+        upscale_api = replicate.StabilityInference(
+            key=os.environ['STABILITY_API_KEY'],
+            upscale_engine="stable-diffusion-x4-latent-upscaler"
+        )
+        upscale_responses = upscale_api.upscale(init_image=img)
+
+        if upscale_responses:
+            # Get the upscaled image from the response
+            upscaled_img = None
+            for resp in upscale_responses:
+                for artifact in resp.artifacts:
+                    if artifact.type == generation.ARTIFACT_IMAGE:
+                        upscaled_img = Image.open(BytesIO(artifact.binary))
+                        break
+                if upscaled_img:
+                    break
+            return upscaled_img
+        else:
+            st.error('Failed to upscale the image.')
+    else:
+        st.error('Failed to generate image from text prompt.')
+
+def main():
+    st.title("Image Upscaling")
+    st.write("Upload an image or enter a text prompt to generate and upscale an image.")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
+    text_prompt = st.text_input("Enter a text prompt:", max_chars=1000)
+
+    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"):
+            # Upscale the uploaded image using GFPGAN
+            img = upscale_image("temp_img.png")
+            st.image(img, caption='Upscaled Image (GFPGAN)', use_column_width=True)
+
+    elif text_prompt != "":
+        if st.button("Generate and Upscale"):
+            # Generate and upscale an image from the text prompt
+            img = generate_and_upscale_image(text_prompt)
+            if img:
+                st.image(img, caption='Generated and Upscaled Image', use_column_width=True)
+    
+if __name__ == "__main__":
+    main()