Spaces:
Runtime error
Runtime error
File size: 5,225 Bytes
d5f0495 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
import gradio as gr
import assemblyai as aai
from together import Together
import base64
from io import BytesIO
from PIL import Image
import os
import yaml
# Function to load API credentials
def load_credentials():
assemblyai_key = os.getenv("ASSEMBLYAI_API_KEY")
together_key = os.getenv("TOGETHER_API_KEY")
if not assemblyai_key or not together_key:
try:
with open('API.yml', 'r') as file:
api_creds = yaml.safe_load(file)
assemblyai_key = assemblyai_key or api_creds['assemblyai']
together_key = together_key or api_creds['Together_api']
except Exception as e:
print(f"Failed to load API credentials: {str(e)}")
return None, None
return assemblyai_key, together_key
# Initialize API clients
ASSEMBLYAI_API_KEY, TOGETHER_API_KEY = load_credentials()
if ASSEMBLYAI_API_KEY and TOGETHER_API_KEY:
aai.settings.api_key = ASSEMBLYAI_API_KEY
together_client = Together(api_key=TOGETHER_API_KEY)
else:
raise ValueError("API credentials not found. Please check your configuration.")
def transcribe_audio(audio_path):
"""Transcribe audio using AssemblyAI."""
try:
transcriber = aai.Transcriber()
transcript = transcriber.transcribe(audio_path)
return transcript.text
except Exception as e:
return f"Error in transcription: {str(e)}"
def generate_image(prompt):
"""Generate image using Together AI."""
try:
response = together_client.images.generate(
prompt=prompt,
model="black-forest-labs/FLUX.1-schnell-Free",
width=1024,
height=768,
steps=4,
n=1,
response_format="b64_json"
)
# Convert base64 to PIL Image
img_data = base64.b64decode(response.data[0].b64_json)
img = Image.open(BytesIO(img_data))
return img
except Exception as e:
return f"Error in image generation: {str(e)}"
def process_audio(audio, progress=gr.Progress()):
"""Process audio file and generate image"""
if audio is None:
return None, "Please provide an audio input."
progress(0.3, desc="Transcribing audio...")
transcribed_text = transcribe_audio(audio)
if isinstance(transcribed_text, str) and not transcribed_text.startswith("Error"):
progress(0.6, desc="Generating image...")
generated_image = generate_image(transcribed_text)
if isinstance(generated_image, Image.Image):
progress(1.0, desc="Complete!")
return generated_image, transcribed_text
else:
return None, f"Image generation failed: {generated_image}"
else:
return None, f"Transcription failed: {transcribed_text}"
# Custom CSS for better styling
custom_css = """
#app-title {
text-align: center;
margin-bottom: 10px;
}
#app-subtitle {
text-align: center;
margin-bottom: 30px;
}
#main-container {
max-width: 1200px;
margin: auto;
}
"""
# Create Gradio interface
def create_interface():
with gr.Blocks(css=custom_css, theme=gr.themes.Soft(
primary_hue="indigo",
secondary_hue="blue",
neutral_hue="slate"
)) as app:
gr.HTML(
"""
<div id="app-title">
<h1> Voice Generated Visions </h1>
</div>
<div id="app-subtitle">
<h3>✨ Transform Your Words into Stunning Visual Art ✨</h3>
</div>
"""
)
with gr.Row():
with gr.Column():
audio_input = gr.Audio(
label="Record or Upload Audio",
sources=["microphone", "upload"],
type="filepath"
)
submit_btn = gr.Button("🚀 Generate Vision", variant="primary")
with gr.Column():
output_image = gr.Image(label="Generated Image 🖼️")
output_text = gr.Textbox(
label="Transcribed Text 📝",
placeholder="Your speech will appear here...",
lines=3
)
# Add usage instructions
with gr.Accordion("ℹ️ How to Use"):
gr.Markdown("""
1. **Record or Upload** 🎤
- Click the microphone icon to record your voice
- Or upload an audio file from your device
2. **Generate** 🎨
- Click 'Generate Vision' to process your audio
- Wait for the magic to happen!
3. **Results** ✨
- View your transcribed text
- See your words transformed into art
""")
submit_btn.click(
fn=process_audio,
inputs=[audio_input],
outputs=[output_image, output_text]
)
return app
# Launch the app
if __name__ == "__main__":
demo = create_interface()
demo.launch(share=True)
|