Spaces:
Running
Running
File size: 3,246 Bytes
c1ca117 fef6f0f 4b3ee30 c1ca117 ba9176f c1ca117 de391e9 dfd6986 c1ca117 dfd6986 f02143a de391e9 dfd6986 f02143a de391e9 f02143a de391e9 dfd6986 de391e9 dfd6986 c1ca117 de391e9 efd3d3c c1ca117 dfd6986 c1ca117 dfd6986 de391e9 dfd6986 c1ca117 de391e9 f02143a c1ca117 de391e9 c1ca117 dfd6986 de391e9 c1ca117 f02143a c1ca117 f02143a de391e9 f02143a c1ca117 f02143a de391e9 f02143a de391e9 f02143a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
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() |