Upload folder using huggingface_hub

.gitignore

@@ -0,0 +1,13 @@
+.vercel
+*.log
+*.pyc
+__pycache__
+
+# Environments 
+.env 
+.venv 
+env/ 
+venv/ 
+ENV/ 
+env.bak/ 
+venv.bak/ 

README.md

@@ -1,12 +1,6 @@
 ---
-title: Teenage Engineering
-emoji: 🐢
-colorFrom: indigo
-colorTo: purple
+title: teenage-engineering
+app_file: index.py
 sdk: gradio
 sdk_version: 4.36.1
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

index.py

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+import os
+import base64
+import numpy as np
+from PIL import Image
+import io
+import requests
+import gradio as gr
+
+import replicate
+
+from dotenv import load_dotenv, find_dotenv
+
+# Locate the .env file
+dotenv_path = find_dotenv()
+
+load_dotenv(dotenv_path)
+
+REPLICATE_API_TOKEN = os.getenv('REPLICATE_API_TOKEN')
+
+
+def image_classifier(prompt, starter_image, image_strength):
+        
+    if starter_image is not None:
+        starter_image_pil = Image.fromarray(starter_image.astype('uint8'))
+
+        # Resize the starter image if either dimension is larger than 768 pixels
+        if starter_image_pil.size[0] > 512 or starter_image_pil.size[1] > 512:
+            # Calculate the new size while maintaining the aspect ratio
+            if starter_image_pil.size[0] > starter_image_pil.size[1]:
+                # Width is larger than height
+                new_width = 512
+                new_height = int((512 / starter_image_pil.size[0]) * starter_image_pil.size[1])
+            else:
+                # Height is larger than width
+                new_height = 512
+                new_width = int((512 / starter_image_pil.size[1]) * starter_image_pil.size[0])
+            
+            # Resize the image
+            starter_image_pil = starter_image_pil.resize((new_width, new_height), Image.LANCZOS)
+
+                # Save the starter image to a bytes buffer
+            buffered = io.BytesIO()
+            starter_image_pil.save(buffered, format="JPEG")
+            
+            # Encode the starter image to base64
+            starter_image_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+    if starter_image is not None:
+        input = {
+            "prompt": prompt + " in the style of TOK",
+            "negative_prompt": "worst quality, low quality, illustration, 2d, painting, cartoons, sketch",
+            "apply_watermark": False,
+            "num_inference_steps": 25,
+            #"refine": "expert_ensemble_refiner",
+            "num_outputs": 3,
+            "lora_scale": .9,
+            "image": "data:image/jpeg;base64," + starter_image_base64, 
+            "prompt_strength": 1-image_strength,
+        }
+    else:
+        input = {
+            "width": 512,
+            "height": 512,
+            "prompt": prompt + " in the style of TOK",
+            "negative_prompt": "worst quality, low quality, illustration, 2d, painting, cartoons, sketch",
+            "apply_watermark": False,
+            "num_inference_steps": 50,
+            "num_outputs": 3,
+            "scheduler": "DDIM",
+            "refine": "expert_ensemble_refiner",
+            "lora_scale": .85,
+        }
+    
+    output = replicate.run(
+        # update to new trained model
+        "ltejedor/teenage-engineering:8a7c93ece9c7a4ae76e08c013f1b4bf25bb3258fa549d112b9f90766d9defb92",
+        input=input
+    )
+
+    print(output)
+    
+    # Download the image from the URL
+    image_url = output[0]
+    print(image_url)
+    response = requests.get(image_url)
+    print(response)
+    img1 = Image.open(io.BytesIO(response.content))
+
+    # Download the image from the URL
+    image_url = output[1]
+    print(image_url)
+    response = requests.get(image_url)
+    print(response)
+    img2 = Image.open(io.BytesIO(response.content))
+
+    # Download the image from the URL
+    image_url = output[2]
+    print(image_url)
+    response = requests.get(image_url)
+    print(response)
+    img3 = Image.open(io.BytesIO(response.content))
+
+    return [img1, img2, img3]
+
+
+demo = gr.Interface(fn=image_classifier, inputs=["text", "image", gr.Slider(0, 1, step=0.025, value=0.2, label="Image Strength")], outputs=["image", "image", "image"])
+demo.launch(share=False)

requirements.txt

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+replicate
+gradio