import os import torch from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor, TextIteratorStreamer, BitsAndBytesConfig import gradio as gr from threading import Thread import numpy as np from PIL import Image import subprocess import spaces from parler_tts import ParlerTTSForConditionalGeneration import soundfile as sf import tempfile import asyncio from concurrent.futures import ThreadPoolExecutor # Add this global variable after the imports executor = ThreadPoolExecutor(max_workers=2) # Install flash-attention subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) # Constants TITLE = "

Phi 3.5 Multimodal (Text + Vision)

" DESCRIPTION = "# Phi-3.5 Multimodal Demo (Text + Vision)" # Model configurations TEXT_MODEL_ID = "microsoft/Phi-3.5-mini-instruct" VISION_MODEL_ID = "microsoft/Phi-3.5-vision-instruct" device = "cuda" if torch.cuda.is_available() else "cpu" # Quantization config for text model quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4" ) # Load models and tokenizers text_tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID) text_model = AutoModelForCausalLM.from_pretrained( TEXT_MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto", quantization_config=quantization_config ) try: vision_model = AutoModelForCausalLM.from_pretrained( VISION_MODEL_ID, trust_remote_code=True, torch_dtype="auto", attn_implementation="flash_attention_2" ).to(device).eval() except Exception as e: print(f"Error loading model with flash attention: {e}") print("Falling back to default attention implementation") vision_model = AutoModelForCausalLM.from_pretrained( VISION_MODEL_ID, trust_remote_code=True, torch_dtype="auto" ).to(device).eval() vision_processor = AutoProcessor.from_pretrained(VISION_MODEL_ID, trust_remote_code=True) # Initialize Parler-TTS tts_model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device) tts_tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1") # Add the generate_speech function here async def generate_speech(text, tts_model, tts_tokenizer): tts_input_ids = tts_tokenizer(text, return_tensors="pt").input_ids.to(device) tts_description = "A clear and natural voice reads the text with moderate speed and expression." tts_description_ids = tts_tokenizer(tts_description, return_tensors="pt").input_ids.to(device) with torch.no_grad(): audio_generation = tts_model.generate(input_ids=tts_description_ids, prompt_input_ids=tts_input_ids) return audio_generation.cpu().numpy().squeeze() from gradio import Error as GradioError @spaces.GPU(timeout=300) def stream_text_chat(message, history, system_prompt, temperature=0.8, max_new_tokens=1024, top_p=1.0, top_k=20, use_tts=True): try: conversation = [{"role": "system", "content": system_prompt}] for prompt, answer in history: conversation.extend([ {"role": "user", "content": prompt}, {"role": "assistant", "content": answer}, ]) conversation.append({"role": "user", "content": message}) input_ids = text_tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(text_model.device) attention_mask = torch.ones_like(input_ids) streamer = TextIteratorStreamer(text_tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, do_sample=temperature > 0, top_p=top_p, top_k=top_k, temperature=temperature, eos_token_id=text_tokenizer.eos_token_id, pad_token_id=text_tokenizer.pad_token_id, streamer=streamer, ) thread = Thread(target=text_model.generate, kwargs=generate_kwargs) thread.start() buffer = "" for new_text in streamer: buffer += new_text yield history + [[message, buffer]], None # Yield None for audio initially # Only attempt TTS if it's enabled and we have a response if use_tts and buffer: try: audio = generate_speech_sync(buffer, tts_model, tts_tokenizer) yield history + [[message, buffer]], (tts_model.config.sampling_rate, audio) except Exception as e: print(f"TTS failed: {str(e)}") yield history + [[message, buffer]], None else: yield history + [[message, buffer]], None except GradioError: yield history + [[message, "GPU task aborted. Please try again."]], None except Exception as e: print(f"An error occurred: {str(e)}") yield history + [[message, f"An error occurred: {str(e)}"]], None def generate_speech_sync(text, tts_model, tts_tokenizer): try: tts_input_ids = tts_tokenizer(text, return_tensors="pt").input_ids.to(device) tts_description = "A clear and natural voice reads the text with moderate speed and expression." tts_description_ids = tts_tokenizer(tts_description, return_tensors="pt").input_ids.to(device) with torch.no_grad(): audio_generation = tts_model.generate(input_ids=tts_description_ids, prompt_input_ids=tts_input_ids) audio_buffer = audio_generation.cpu().numpy().squeeze() return audio_buffer if audio_buffer.size > 0 else np.array([0.0]) except Exception as e: print(f"Speech generation failed: {str(e)}") return np.array([0.0]) @spaces.GPU(timeout=300) # Increase timeout to 5 minutes def process_vision_query(image, text_input): try: prompt = f"<|user|>\n<|image_1|>\n{text_input}<|end|>\n<|assistant|>\n" # Ensure the image is in the correct format if isinstance(image, np.ndarray): image = Image.fromarray(image).convert("RGB") elif not isinstance(image, Image.Image): raise ValueError("Invalid image type. Expected PIL.Image.Image or numpy.ndarray") inputs = vision_processor(prompt, images=image, return_tensors="pt").to(device) with torch.no_grad(): generate_ids = vision_model.generate( **inputs, max_new_tokens=1000, eos_token_id=vision_processor.tokenizer.eos_token_id ) generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] response = vision_processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] return response except Exception as e: print(f"An error occurred: {str(e)}") return f"An error occurred: {str(e)}" # Custom CSS custom_css = """ body { background-color: #0b0f19; color: #e2e8f0; font-family: 'Arial', sans-serif;} #custom-header { text-align: center; padding: 20px 0; background-color: #1a202c; margin-bottom: 20px; border-radius: 10px;} #custom-header h1 { font-size: 2.5rem; margin-bottom: 0.5rem;} #custom-header h1 .blue { color: #60a5fa;} #custom-header h1 .pink { color: #f472b6;} #custom-header h2 { font-size: 1.5rem; color: #94a3b8;} .suggestions { display: flex; justify-content: center; flex-wrap: wrap; gap: 1rem; margin: 20px 0;} .suggestion { background-color: #1e293b; border-radius: 0.5rem; padding: 1rem; display: flex; align-items: center; transition: transform 0.3s ease; width: 200px;} .suggestion:hover { transform: translateY(-5px);} .suggestion-icon { font-size: 1.5rem; margin-right: 1rem; background-color: #2d3748; padding: 0.5rem; border-radius: 50%;} .gradio-container { max-width: 100% !important;} #component-0, #component-1, #component-2 { max-width: 100% !important;} footer { text-align: center; margin-top: 2rem; color: #64748b;} """ # Custom HTML for the header custom_header = """

Phi 3.5 Multimodal Assistant

Text and Vision AI at Your Service

""" # Custom HTML for suggestions custom_suggestions = """
💬

Chat with the Text Model

🖼️

Analyze Images with Vision Model

🤖

Get AI-generated responses

🔍

Explore advanced options

""" # Gradio interface with gr.Blocks(css=custom_css, theme=gr.themes.Base().set( body_background_fill="#0b0f19", body_text_color="#e2e8f0", button_primary_background_fill="#3b82f6", button_primary_background_fill_hover="#2563eb", button_primary_text_color="white", block_title_text_color="#94a3b8", block_label_text_color="#94a3b8", )) as demo: gr.HTML(custom_header) with gr.Tab("Text Model (Phi-3.5-mini)"): chatbot = gr.Chatbot(height=400) msg = gr.Textbox(label="Message", placeholder="Type your message here...") audio_output = gr.Audio(label="Generated Speech", autoplay=True) with gr.Accordion("Advanced Options", open=False): system_prompt = gr.Textbox(value="You are a helpful assistant", label="System Prompt") temperature = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.8, label="Temperature") max_new_tokens = gr.Slider(minimum=128, maximum=8192, step=1, value=1024, label="Max new tokens") top_p = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="top_p") top_k = gr.Slider(minimum=1, maximum=20, step=1, value=20, label="top_k") use_tts = gr.Checkbox(label="Enable Text-to-Speech", value=True) submit_btn = gr.Button("Submit", variant="primary") clear_btn = gr.Button("Clear Chat", variant="secondary") def clear_chat(): return None submit_btn.click(stream_text_chat, inputs=[msg, chatbot, system_prompt, temperature, max_new_tokens, top_p, top_k, use_tts], outputs=[chatbot, audio_output]) clear_btn.click(clear_chat, outputs=chatbot) with gr.Tab("Vision Model (Phi-3.5-vision)"): with gr.Row(): with gr.Column(scale=1): vision_input_img = gr.Image(label="Upload an Image", type="pil") vision_text_input = gr.Textbox(label="Ask a question about the image", placeholder="What do you see in this image?") vision_submit_btn = gr.Button("Analyze Image", variant="primary") with gr.Column(scale=1): vision_output_text = gr.Textbox(label="AI Analysis", lines=10) vision_submit_btn.click(process_vision_query, inputs=[vision_input_img, vision_text_input], outputs=vision_output_text) gr.HTML("") if __name__ == "__main__": demo.launch(share=True)