Delete app.py
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app.py
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import gradio as gr
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import requests
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from PIL import Image
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from io import BytesIO
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# Hugging Face API URL and Token
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API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
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headers = {"Authorization": f"Bearer YOUR_HF_API_KEY"} # Replace with your Hugging Face API key
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# Function to query the Hugging Face API for image generation
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def query_hf_api(prompt):
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response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
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if response.status_code != 200:
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return None # Return None if there's an error in the API call
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return response.content
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# Function to generate comic panels based on story description
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def generate_comic_panels(story_description):
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# Creating prompts for three key scenes from the story
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scenes = [f"{story_description} - Scene {i+1}" for i in range(3)]
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images = []
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for scene in scenes:
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# Querying the API for each scene
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image_bytes = query_hf_api(scene)
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if image_bytes is None:
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return ["Error: Could not generate images, check the API or story."]
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image = Image.open(BytesIO(image_bytes)) # Convert bytes to image
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images.append(image)
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return images
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# Function to set up the Gradio interface
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def create_interface():
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description = """
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Welcome to GenArt Narrative! Enter a brief story description and watch it transform into a comic-strip format,
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with images generated for key scenes of your story.
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"""
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# Textbox for input and gallery for output
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inputs = gr.Textbox(lines=5, placeholder="Enter a short story description here...", label="Story Description")
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outputs = gr.Gallery(label="Generated Comic Panels", columns=3) # Remove the .style()
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# Launch the interface
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gr.Interface(
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fn=generate_comic_panels,
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inputs=inputs,
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outputs=outputs,
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title="GenArt Narrative",
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description=description,
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).launch()
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# Run the app
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if __name__ == "__main__":
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create_interface()
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