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| import gradio as gr | |
| import random | |
| from datetime import datetime | |
| import tempfile | |
| import os | |
| import edge_tts | |
| import asyncio | |
| import warnings | |
| from gradio_client import Client | |
| import pytz | |
| import re | |
| import json | |
| warnings.filterwarnings('ignore') | |
| # Initialize client outside of interface definition | |
| arxiv_client = None | |
| def init_client(): | |
| global arxiv_client | |
| if arxiv_client is None: | |
| arxiv_client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern") | |
| return arxiv_client | |
| def generate_story(prompt, model_choice): | |
| """Generate story using specified model""" | |
| try: | |
| client = init_client() | |
| if client is None: | |
| return "Error: Story generation service is not available." | |
| result = client.predict( | |
| prompt=prompt, | |
| llm_model_picked=model_choice, | |
| stream_outputs=True, | |
| api_name="/ask_llm" | |
| ) | |
| return result | |
| except Exception as e: | |
| return f"Error generating story: {str(e)}" | |
| async def generate_speech(text, voice="en-US-AriaNeural"): | |
| """Generate speech from text""" | |
| try: | |
| communicate = edge_tts.Communicate(text, voice) | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file: | |
| tmp_path = tmp_file.name | |
| await communicate.save(tmp_path) | |
| return tmp_path | |
| except Exception as e: | |
| print(f"Error in text2speech: {str(e)}") | |
| return None | |
| def process_story_and_audio(prompt, model_choice): | |
| """Process story and generate audio""" | |
| try: | |
| # Generate story | |
| story = generate_story(prompt, model_choice) | |
| if isinstance(story, str) and story.startswith("Error"): | |
| return story, None | |
| # Generate audio | |
| audio_path = asyncio.run(generate_speech(story)) | |
| return story, audio_path | |
| except Exception as e: | |
| return f"Error: {str(e)}", None | |
| # Create the Gradio interface | |
| with gr.Blocks(title="AI Story Generator") as demo: | |
| gr.Markdown(""" | |
| # 🎭 AI Story Generator & Narrator | |
| Generate creative stories and listen to them! | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt_input = gr.Textbox( | |
| label="Story Concept", | |
| placeholder="Enter your story idea...", | |
| lines=3 | |
| ) | |
| model_choice = gr.Dropdown( | |
| label="Model", | |
| choices=[ | |
| "mistralai/Mixtral-8x7B-Instruct-v0.1", | |
| "mistralai/Mistral-7B-Instruct-v0.2" | |
| ], | |
| value="mistralai/Mixtral-8x7B-Instruct-v0.1" | |
| ) | |
| generate_btn = gr.Button("Generate Story") | |
| with gr.Row(): | |
| story_output = gr.Textbox( | |
| label="Generated Story", | |
| lines=10, | |
| interactive=False | |
| ) | |
| with gr.Row(): | |
| audio_output = gr.Audio( | |
| label="Story Narration", | |
| type="filepath" | |
| ) | |
| generate_btn.click( | |
| fn=process_story_and_audio, | |
| inputs=[prompt_input, model_choice], | |
| outputs=[story_output, audio_output] | |
| ) | |
| # Launch the app using the current pattern | |
| if __name__ == "__main__": | |
| demo.launch() |