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import gradio as gr
import torch
from transformers import Qwen2_5OmniModel, Qwen2_5OmniProcessor
from qwen_omni_utils import process_mm_info
import soundfile as sf
import tempfile
import spaces
# Initialize the model and processor
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float16
model = Qwen2_5OmniModel.from_pretrained(
"Qwen/Qwen2.5-Omni-7B",
torch_dtype=torch_dtype,
device_map="auto",
enable_audio_output=True,
# attn_implementation="flash_attention_2" if torch.cuda.is_available() else None
)
processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
# System prompt
SYSTEM_PROMPT = {
"role": "system",
"content": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."
}
# Voice options
VOICE_OPTIONS = {
"Chelsie (Female)": "Chelsie",
"Ethan (Male)": "Ethan"
}
@spaces.GPU
def process_input(image, audio, video, text, chat_history, voice_type, enable_audio_output):
# Combine multimodal inputs
user_input = {
"text": text,
"image": image if image is not None else None,
"audio": audio if audio is not None else None,
"video": video if video is not None else None
}
# Prepare conversation history for model processing
conversation = [SYSTEM_PROMPT]
# Add previous chat history
if isinstance(chat_history, list):
for item in chat_history:
if isinstance(item, tuple) and len(item) == 2:
user_msg, bot_msg = item
conversation.append({"role": "user", "content": user_input_to_content(user_msg)})
conversation.append({"role": "assistant", "content": bot_msg})
else:
# Initialize chat history if it's not a list
chat_history = []
# Add current user input
conversation.append({"role": "user", "content": user_input_to_content(user_input)})
# Prepare for inference
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios, images, videos = process_mm_info(conversation, use_audio_in_video=True)
inputs = processor(
text=text,
audios=audios,
images=images,
videos=videos,
return_tensors="pt",
padding=True
)
inputs = inputs.to(model.device).to(model.dtype)
# Generate response
if enable_audio_output:
voice_type_value = VOICE_OPTIONS.get(voice_type, "Chelsie")
text_ids, audio = model.generate(
**inputs,
use_audio_in_video=True,
return_audio=True,
spk=voice_type_value
)
# Save audio to temporary file
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_file:
sf.write(
tmp_file.name,
audio.reshape(-1).detach().cpu().numpy(),
samplerate=24000,
)
audio_path = tmp_file.name
else:
text_ids = model.generate(
**inputs,
use_audio_in_video=True,
return_audio=False
)
audio_path = None
# Decode text response
text_response = processor.batch_decode(
text_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)[0]
# Clean up text response
text_response = text_response.strip()
# Format user message for chat history display
user_message_for_display = str(text) if text is not None else ""
if image is not None:
user_message_for_display = (user_message_for_display or "Image uploaded") + " [Image]"
if audio is not None:
user_message_for_display = (user_message_for_display or "Audio uploaded") + " [Audio]"
if video is not None:
user_message_for_display = (user_message_for_display or "Video uploaded") + " [Video]"
# If empty, provide a default message
if not user_message_for_display.strip():
user_message_for_display = "Multimodal input"
# Update chat history with properly formatted entries
if not isinstance(chat_history, list):
chat_history = []
chat_history.append((user_message_for_display, text_response))
# Prepare output
if enable_audio_output and audio_path:
return chat_history, text_response, audio_path
else:
return chat_history, text_response, None
def user_input_to_content(user_input):
if isinstance(user_input, str):
return user_input
elif isinstance(user_input, dict):
# Handle file uploads
content = []
if "text" in user_input and user_input["text"]:
content.append({"type": "text", "text": user_input["text"]})
if "image" in user_input and user_input["image"]:
content.append({"type": "image", "image": user_input["image"]})
if "audio" in user_input and user_input["audio"]:
content.append({"type": "audio", "audio": user_input["audio"]})
if "video" in user_input and user_input["video"]:
content.append({"type": "video", "video": user_input["video"]})
return content
return user_input
def create_demo():
with gr.Blocks(title="Qwen2.5-Omni ChatBot", theme=gr.themes.Soft()) as demo:
gr.Markdown("# Qwen2.5-Omni Multimodal ChatBot")
gr.Markdown("Experience the omni-modal capabilities of Qwen2.5-Omni through text, images, audio, and video interactions.")
# Hidden placeholder components for text-only input
placeholder_image = gr.Image(type="filepath", visible=False)
placeholder_audio = gr.Audio(type="filepath", visible=False)
placeholder_video = gr.Video(visible=False)
# Chat interface
with gr.Row():
with gr.Column(scale=3):
chatbot = gr.Chatbot(height=600)
with gr.Accordion("Advanced Options", open=False):
voice_type = gr.Dropdown(
choices=list(VOICE_OPTIONS.keys()),
value="Chelsie (Female)",
label="Voice Type"
)
enable_audio_output = gr.Checkbox(
value=True,
label="Enable Audio Output"
)
# Multimodal input components
with gr.Tabs():
with gr.TabItem("Text Input"):
text_input = gr.Textbox(
placeholder="Type your message here...",
label="Text Input"
)
text_submit = gr.Button("Send Text")
with gr.TabItem("Multimodal Input"):
with gr.Row():
image_input = gr.Image(
type="filepath",
label="Upload Image"
)
audio_input = gr.Audio(
type="filepath",
label="Upload Audio"
)
with gr.Row():
video_input = gr.Video(
label="Upload Video"
)
additional_text = gr.Textbox(
placeholder="Additional text message...",
label="Additional Text"
)
multimodal_submit = gr.Button("Send Multimodal Input")
clear_button = gr.Button("Clear Chat")
with gr.Column(scale=1):
gr.Markdown("## Model Capabilities")
gr.Markdown("""
**Qwen2.5-Omni can:**
- Process and understand text
- Analyze images and answer questions about them
- Transcribe and understand audio
- Analyze video content (with or without audio)
- Generate natural speech responses
""")
gr.Markdown("### Example Prompts")
gr.Examples(
examples=[
["Describe what you see in this image", "image"],
["What is being said in this audio clip?", "audio"],
["What's happening in this video?", "video"],
["Explain Artificial Intelligence in simple terms", "text"],
["Generate a short story about a robot learning to play AlphaGo", "text"]
],
inputs=[text_input, gr.Textbox(visible=False)],
label="Text Examples"
)
audio_output = gr.Audio(
label="Model Speech Output",
visible=True,
autoplay=True
)
text_output = gr.Textbox(
label="Model Text Response",
interactive=False
)
# Text input handling
text_submit.click(
fn=lambda text: str(text) if text is not None else "",
inputs=text_input,
outputs=[chatbot],
queue=False
).then(
fn=process_input,
inputs=[placeholder_image, placeholder_audio, placeholder_video, text_input, chatbot, voice_type, enable_audio_output],
outputs=[chatbot, text_output, audio_output]
)
# Multimodal input handling
def prepare_multimodal_input(image, audio, video, text):
# Create a display message that indicates what was uploaded
display_message = str(text) if text is not None else ""
if image is not None:
display_message = (display_message + " " if display_message.strip() else "") + "[Image]"
if audio is not None:
display_message = (display_message + " " if display_message.strip() else "") + "[Audio]"
if video is not None:
display_message = (display_message + " " if display_message.strip() else "") + "[Video]"
if not display_message.strip():
display_message = "Multimodal content"
return display_message
multimodal_submit.click(
fn=prepare_multimodal_input,
inputs=[image_input, audio_input, video_input, additional_text],
outputs=[chatbot],
queue=False
).then(
fn=process_input,
inputs=[image_input, audio_input, video_input, additional_text,
chatbot, voice_type, enable_audio_output],
outputs=[chatbot, text_output, audio_output]
)
# Clear chat
def clear_chat():
return [], None, None
clear_button.click(
fn=clear_chat,
outputs=[chatbot, text_output, audio_output]
)
# Update audio output visibility
def toggle_audio_output(enable_audio):
return gr.Audio(visible=enable_audio)
enable_audio_output.change(
fn=toggle_audio_output,
inputs=enable_audio_output,
outputs=audio_output
)
return demo
if __name__ == "__main__":
demo = create_demo()
demo.launch(server_name="0.0.0.0", server_port=7860) |