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Update app.py
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app.py
CHANGED
@@ -6,13 +6,27 @@ import os
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import edge_tts
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import asyncio
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import warnings
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from gradio_client import Client
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import pytz
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import re
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import json
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warnings.filterwarnings('ignore')
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# Initialize client outside of interface definition
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arxiv_client = None
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@@ -22,6 +36,58 @@ def init_client():
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arxiv_client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
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return arxiv_client
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def generate_story(prompt, model_choice):
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"""Generate story using specified model"""
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try:
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@@ -52,63 +118,90 @@ async def generate_speech(text, voice="en-US-AriaNeural"):
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return None
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def process_story_and_audio(prompt, model_choice):
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"""Process story and
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try:
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# Generate story
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story = generate_story(prompt, model_choice)
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if isinstance(story, str) and story.startswith("Error"):
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return story, None
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# Generate audio
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audio_path = asyncio.run(generate_speech(story))
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except Exception as e:
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return f"Error: {str(e)}", None
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# Create the Gradio interface
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with gr.Blocks(title="AI Story Generator") as demo:
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gr.Markdown("""
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# ๐ญ AI Story Generator & Narrator
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Generate creative stories
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""")
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with gr.Row():
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with gr.Column():
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)
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value="mistralai/Mixtral-8x7B-Instruct-v0.1"
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)
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generate_btn = gr.Button("Generate Story")
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lines=10,
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interactive=False
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)
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audio_output = gr.Audio(
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label="Story Narration",
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type="filepath"
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)
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generate_btn.click(
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fn=process_story_and_audio,
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inputs=[prompt_input, model_choice],
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outputs=[story_output, audio_output]
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)
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# Launch the app using the current pattern
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if __name__ == "__main__":
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demo.launch()
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import edge_tts
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import asyncio
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import warnings
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import pytz
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import re
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import json
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import pandas as pd
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from pathlib import Path
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from gradio_client import Client
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warnings.filterwarnings('ignore')
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# Initialize story starters
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STORY_STARTERS = pd.DataFrame({
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'category': ['Adventure', 'Mystery', 'Romance', 'Sci-Fi', 'Fantasy'],
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'starter': [
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'In a hidden temple deep in the Amazon...',
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'The detective found an unusual note...',
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'Two strangers meet on a rainy evening...',
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'The space station received an unexpected signal...',
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'A magical portal appeared in the garden...'
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]
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})
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# Initialize client outside of interface definition
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arxiv_client = None
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arxiv_client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
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return arxiv_client
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def save_story(story, audio_path):
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"""Save story and audio to gallery"""
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try:
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# Create gallery directory if it doesn't exist
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gallery_dir = Path("gallery")
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gallery_dir.mkdir(exist_ok=True)
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# Generate timestamp for unique filename
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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# Save story text
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story_path = gallery_dir / f"story_{timestamp}.txt"
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with open(story_path, "w") as f:
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f.write(story)
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# Copy audio file to gallery
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if audio_path:
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new_audio_path = gallery_dir / f"audio_{timestamp}.mp3"
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os.system(f"cp {audio_path} {str(new_audio_path)}")
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return str(story_path), str(new_audio_path)
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except Exception as e:
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print(f"Error saving to gallery: {str(e)}")
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return None, None
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def load_gallery():
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"""Load all stories and audio from gallery"""
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try:
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gallery_dir = Path("gallery")
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if not gallery_dir.exists():
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return []
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files = []
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for story_file in gallery_dir.glob("story_*.txt"):
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timestamp = story_file.stem.split('_')[1]
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audio_file = gallery_dir / f"audio_{timestamp}.mp3"
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with open(story_file) as f:
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story_text = f.read()
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files.append({
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"timestamp": timestamp,
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"story_path": str(story_file),
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"audio_path": str(audio_file) if audio_file.exists() else None,
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"story_preview": story_text[:100] + "..."
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})
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return sorted(files, key=lambda x: x["timestamp"], reverse=True)
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except Exception as e:
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print(f"Error loading gallery: {str(e)}")
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return []
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def generate_story(prompt, model_choice):
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"""Generate story using specified model"""
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try:
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return None
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def process_story_and_audio(prompt, model_choice):
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"""Process story, generate audio, and save to gallery"""
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try:
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# Generate story
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story = generate_story(prompt, model_choice)
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if isinstance(story, str) and story.startswith("Error"):
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return story, None, None
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# Generate audio
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audio_path = asyncio.run(generate_speech(story))
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# Save to gallery
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story_path, saved_audio_path = save_story(story, audio_path)
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return story, audio_path, load_gallery()
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except Exception as e:
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return f"Error: {str(e)}", None, None
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# Create the Gradio interface
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with gr.Blocks(title="AI Story Generator") as demo:
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gr.Markdown("""
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# ๐ญ AI Story Generator & Narrator
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Generate creative stories, listen to them, and build your gallery!
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""")
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with gr.Row():
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with gr.Column(scale=3):
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with gr.Row():
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prompt_input = gr.Textbox(
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label="Story Concept",
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placeholder="Enter your story idea...",
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lines=3
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)
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with gr.Row():
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model_choice = gr.Dropdown(
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label="Model",
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choices=[
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"mistralai/Mixtral-8x7B-Instruct-v0.1",
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"mistralai/Mistral-7B-Instruct-v0.2"
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],
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value="mistralai/Mixtral-8x7B-Instruct-v0.1"
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)
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generate_btn = gr.Button("Generate Story")
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with gr.Row():
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story_output = gr.Textbox(
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label="Generated Story",
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lines=10,
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interactive=False
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)
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with gr.Row():
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audio_output = gr.Audio(
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label="Story Narration",
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type="filepath"
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)
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# Sidebar with Story Starters and Gallery
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with gr.Column(scale=1):
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gr.Markdown("### ๐ Story Starters")
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story_starters = gr.Dataframe(
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value=STORY_STARTERS,
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headers=["category", "starter"],
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interactive=False
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)
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gr.Markdown("### ๐ฌ Gallery")
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gallery = gr.Dataframe(
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value=load_gallery(),
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headers=["timestamp", "story_preview"],
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interactive=False
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)
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# Event handlers
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def update_prompt(evt: gr.SelectData):
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return STORY_STARTERS.iloc[evt.index[0]]["starter"]
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story_starters.select(update_prompt, None, prompt_input)
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generate_btn.click(
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fn=process_story_and_audio,
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inputs=[prompt_input, model_choice],
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outputs=[story_output, audio_output, gallery]
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)
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if __name__ == "__main__":
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demo.launch()
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