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app.py

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+# import gradio as gr
+# import requests
+# import io
+# import os
+# from PIL import Image, ImageEnhance
+
+# API_URL = "https://api-inference.huggingface.co/models/sd-community/sdxl-flash"
+# API_KEY = os.getenv("HF_API_KEY")
+# headers = {"Authorization": f"Bearer {API_KEY}"}  # Corrected to use f-string
+
+# def query(payload):
+#     response = requests.post(API_URL, headers=headers, json=payload)
+#     if response.status_code != 200:
+#         return None
+#     return response.content
+
+# def gen_img(prompt, num_inference_steps, guidance_scale, contrast):
+#     payload = {
+#         "inputs": prompt,
+#         "parameters": {
+#             "num_inference_steps": num_inference_steps,
+#             "guidance_scale": guidance_scale
+#         }
+#     }
+#     image_bytes = query(payload)
+#     if image_bytes is None:
+#         return None
+#     image = Image.open(io.BytesIO(image_bytes))
+    
+#     enhancer = ImageEnhance.Contrast(image)
+#     image = enhancer.enhance(contrast)
+
+#     return image
+
+# with gr.Blocks(theme=gr.themes.Soft(primary_hue=gr.themes.colors.lime, spacing_size="md", radius_size="lg", 
+#                                     font=[gr.themes.GoogleFont("Copperplate"), "Fantasy", "sans-serif"])) as demo:
+    
+#     gr.Markdown("""
+#     <div style='text-align: center; 
+#                 font-size: 36px; 
+#                 font-family: Copperplate, Fantasy; 
+#                 color: orange;
+#                 /* background-color: green; 
+#                 padding: 5px; 
+#                 border-radius: 30px; */ '>
+#         Pencil Vision (a holofote product)
+#     </div>
+#     """)
+#     with gr.Row():
+#         prompt = gr.Textbox(label="Enter your prompt to generate the image")
+    
+#     with gr.Row():
+#         num_inference_steps = gr.Slider(minimum=1, maximum=15, value=6, step=3, label="Number of Inference Steps")
+#         guidance_scale = gr.Slider(minimum=1.0, maximum=5.0, value=2.5, step=1, label="Guidance Scale")
+#         contrast_scale = gr.Slider(minimum=1.0, maximum=3.0, value=1.0, step=0.1, label="Contrast Scale")  
+    
+#     with gr.Row():
+#         generate_button = gr.Button("Generate Image")
+    
+#     output_image = gr.Image(label="Generated Image")
+#     error_message = gr.Markdown(visible=False)
+    
+#     def handle_click(prompt, num_inference_steps, guidance_scale, contrast_scale):
+#         image = gen_img(prompt, num_inference_steps, guidance_scale, contrast_scale)
+#         if image is None:
+#             return None, gr.update(visible=True, value="Error generating image. Please try again.")
+#         return image, gr.update(visible=False)
+    
+#     generate_button.click(handle_click, inputs=[prompt, num_inference_steps, guidance_scale, contrast_scale], outputs=[output_image, error_message])
+
+# demo.launch(share=True)
+
+#------------------------------------------------------------------------------------------------------------------------------------------------------------
+import gradio as gr
+import requests
+import io
+import os
+import logging
+from PIL import Image, ImageEnhance
+from gradio_client import Client
+
+# API URLs and headers
+API_URL_1 = "https://api-inference.huggingface.co/models/sd-community/sdxl-flash"
+
+API_KEY = os.getenv("HF_API_KEY")
+headers = {"Authorization": f"Bearer {API_KEY}"}
+
+client = Client("AP123/SDXL-Lightning")
+
+def query(api_url, payload):
+    try:
+        response = requests.post(api_url, headers=headers, json=payload)
+        response.raise_for_status()
+        return response.content
+    except requests.exceptions.HTTPError as http_err:
+        logging.error(f"HTTP error occurred: {http_err}")
+        print(f"HTTP error occurred: {http_err}")
+    except Exception as err:
+        logging.error(f"An error occurred: {err}")
+        print(f"An error occurred: {err}")
+    return None
+
+def map_inference_steps(steps):
+    if steps <= 1:
+        return "1-Step"
+    elif steps <= 2:
+        return "2-Step"
+    elif steps <= 4:
+        return "4-Step"
+    else:
+        return "8-Step"
+
+def gen_img(prompt, num_inference_steps, guidance_scale, contrast):
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "num_inference_steps": num_inference_steps,
+            "guidance_scale": guidance_scale
+        }
+    }
+
+    # Try generating image with the first model
+    image_bytes = query(API_URL_1, payload)
+    if image_bytes is None:
+        # print("First model failed, trying second model...")
+        try:
+            mapped_steps = map_inference_steps(num_inference_steps)
+            result = client.predict(prompt, mapped_steps, api_name="/generate_image")
+            with open(result, "rb") as img_file:
+                image = Image.open(img_file)
+                enhancer = ImageEnhance.Contrast(image)
+                image = enhancer.enhance(contrast)
+                return image, "Image generated by the second model"
+        except Exception as e:
+            # logging.error(f"Error with the second model: {e}")
+            # print(f"Error with the second model: {e}")
+            return None, "Error with the second model"
+    
+    try:
+        image = Image.open(io.BytesIO(image_bytes))
+        enhancer = ImageEnhance.Contrast(image)
+        image = enhancer.enhance(contrast)
+        return image, "Image generated by the first model"
+    except Exception as e:
+        # logging.error(f"Error processing image: {e}")
+        return None, "Error processing image"
+
+with gr.Blocks(theme=gr.themes.Soft(primary_hue=gr.themes.colors.lime, spacing_size="md", radius_size="lg", 
+                                    font=[gr.themes.GoogleFont("Copperplate"), "Fantasy", "sans-serif"])) as demo:
+    
+    gr.Markdown("""
+    <div style='text-align: center; 
+                font-size: 36px; 
+                font-family: Copperplate, Fantasy; 
+                color: orange;'>
+        Pencil Vision (early beta 1.3)
+    </div>
+    """)
+    with gr.Row():
+        prompt = gr.Textbox(label="Enter your prompt to generate the image", placeholder="A fantasy landscape with mountains and a river")
+
+    with gr.Row():
+        num_inference_steps = gr.Slider(minimum=1, maximum=15, value=6, step=1, label="Number of Inference Steps")
+        guidance_scale = gr.Slider(minimum=1.0, maximum=5.0, value=2.5, step=0.1, label="Guidance Scale")
+        contrast_scale = gr.Slider(minimum=1.0, maximum=3.0, value=1.0, step=0.1, label="Contrast Scale")  
+
+    with gr.Row():
+        generate_button = gr.Button("Generate Image")
+
+    output_image = gr.Image(label="Generated Image")
+    # model_info = gr.Textbox(label="Model Info", interactive=False)
+    error_message = gr.Markdown(visible=False)
+    
+    def handle_click(prompt, num_inference_steps, guidance_scale, contrast_scale):
+        image, info = gen_img(prompt, num_inference_steps, guidance_scale, contrast_scale)
+        if image is None:
+            return None, info, gr.update(visible=True, value="Error generating image. Please try again.")
+        return image, info, gr.update(visible=True)
+    
+    generate_button.click(handle_click, inputs=[prompt, num_inference_steps, guidance_scale, contrast_scale], outputs=[output_image, error_message])
+
+demo.launch(share=True)

requirements.txt

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+gradio
+requests
+Pillow
+transformers
+huggingface_hub