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Update app.py
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app.py
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@@ -2,32 +2,31 @@ import gradio as gr
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import requests
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from PIL import Image
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import io
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import
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# Define the API URL for the Craiyon model (lightweight text-to-image generator)
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API_URL = "https://api-inference.huggingface.co/models/dalle-mini"
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# Function to call the model and generate images
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def generate_comic(prompt):
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# Sending request to the model with user prompt
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response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
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# Print the response for debugging
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print(f"API Response: {response.status_code}, {response.text}")
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# Check if the response is successful
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if response.status_code != 200:
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return f"Error: {response.status_code}, {response.text}"
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# Extracting
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images = response.json().get("generated_images", [])
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if not images:
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return "No images were generated, please try again with a different prompt."
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# Process the images (assuming they are base64 encoded)
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pil_images = []
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for img in images:
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image_data = base64.b64decode(img)
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@@ -38,21 +37,12 @@ def generate_comic(prompt):
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# Gradio interface setup
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def gradio_interface():
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# Set up input and output elements
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with gr.Blocks() as demo:
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gr.Markdown("## GenArt Narrative - Turn Your Story into Comic Panels!")
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with gr.Row():
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prompt = gr.Textbox(label="Enter your short story description", placeholder="Once upon a time...")
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# Directly specifying the number of columns without .style()
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output_gallery = gr.Gallery(label="Generated Comic Panels", columns=3, height=300)
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submit_button = gr.Button("Generate Comic")
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# Set up button functionality
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submit_button.click(fn=generate_comic, inputs=prompt, outputs=output_gallery)
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return demo
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# Run the Gradio app
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import requests
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from PIL import Image
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import io
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import os
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# Define the API URL for the Craiyon model (lightweight text-to-image generator)
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API_URL = "https://api-inference.huggingface.co/models/dalle-mini"
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# Function to call the model and generate images
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def generate_comic(prompt):
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api_token = os.environ.get("API_TOKEN") # Securely access the token
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headers = {"Authorization": f"Bearer {api_token}"}
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# Sending request to the model with user prompt
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response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
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# Check if the response is successful
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if response.status_code != 200:
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return f"Error: {response.status_code}, {response.text}"
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# Extracting the response data
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images = response.json().get("generated_images", [])
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if not images:
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return "No images were generated, please try again with a different prompt."
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# Process the images (assuming they are base64 encoded or URL)
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pil_images = []
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for img in images:
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image_data = base64.b64decode(img)
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# Gradio interface setup
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def gradio_interface():
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with gr.Blocks() as demo:
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gr.Markdown("## GenArt Narrative - Turn Your Story into Comic Panels!")
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prompt = gr.Textbox(label="Enter your short story description", placeholder="Once upon a time...")
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output_gallery = gr.Gallery(label="Generated Comic Panels", columns=3, height=300)
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submit_button = gr.Button("Generate Comic")
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submit_button.click(fn=generate_comic, inputs=prompt, outputs=output_gallery)
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return demo
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# Run the Gradio app
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