import os from collections.abc import Iterator from threading import Thread import gradio as gr import spaces import torch import edge_tts import asyncio from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer from transformers.image_utils import load_image import time DESCRIPTION = """ # QwQ Edge 💬 """ css = ''' h1 { text-align: center; display: block; } #duplicate-button { margin: auto; color: #fff; background: #1565c0; border-radius: 100vh; } ''' MAX_MAX_NEW_TOKENS = 2048 DEFAULT_MAX_NEW_TOKENS = 1024 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model_id = "prithivMLmods/FastThink-0.5B-Tiny" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16, ) model.eval() TTS_VOICES = [ "en-US-JennyNeural", # @tts1 "en-US-GuyNeural", # @tts2 "en-US-AriaNeural", # @tts3 "en-US-DavisNeural", # @tts4 "en-US-JaneNeural", # @tts5 "en-US-JasonNeural", # @tts6 "en-US-NancyNeural", # @tts7 "en-US-TonyNeural", # @tts8 ] MODEL_ID = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct" processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True) model_m = Qwen2VLForConditionalGeneration.from_pretrained( MODEL_ID, trust_remote_code=True, torch_dtype=torch.float16 ).to("auto").eval() async def text_to_speech(text: str, voice: str, output_file="output.mp3"): """Convert text to speech using Edge TTS and save as MP3""" communicate = edge_tts.Communicate(text, voice) await communicate.save(output_file) return output_file @spaces.GPU def generate( input_dict: dict, chat_history: list[dict], max_new_tokens: int = 1024, temperature: float = 0.6, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2, ): """Generates chatbot response and handles TTS requests with multimodal input support""" text = input_dict["text"] files = input_dict.get("files", []) # Check if input includes image(s) if len(files) > 1: images = [load_image(image) for image in files] elif len(files) == 1: images = [load_image(files[0])] else: images = [] # Check if message is for TTS tts_prefix = "@tts" is_tts = any(text.strip().lower().startswith(f"{tts_prefix}{i}") for i in range(1, 9)) voice_index = next((i for i in range(1, 9) if text.strip().lower().startswith(f"{tts_prefix}{i}")), None) if is_tts and voice_index: voice = TTS_VOICES[voice_index - 1] text = text.replace(f"{tts_prefix}{voice_index}", "").strip() else: voice = None text = text.replace(tts_prefix, "").strip() conversation = [*chat_history, {"role": "user", "content": text}] if images: # Process multimodal input messages = [ {"role": "user", "content": [ *[{"type": "image", "image": image} for image in images], {"type": "text", "text": text}, ]} ] prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = processor(text=[prompt], images=images, return_tensors="pt", padding=True).to("cuda") # Handle generation for multimodal input streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True) generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens) thread = Thread(target=model_m.generate, kwargs=generation_kwargs) thread.start() buffer = "" yield "Thinking..." for new_text in streamer: buffer += new_text buffer = buffer.replace("<|im_end|>", "") time.sleep(0.01) yield buffer else: # Process text-only input input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt") if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") input_ids = input_ids.to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( {"input_ids": input_ids}, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, top_k=top_k, temperature=temperature, num_beams=1, repetition_penalty=repetition_penalty, ) t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() outputs = [] for text in streamer: outputs.append(text) yield "".join(outputs) final_response = "".join(outputs) if is_tts and voice: output_file = asyncio.run(text_to_speech(final_response, voice)) yield gr.Audio(output_file, autoplay=True) # Return playable audio else: yield final_response # Return text response demo = gr.ChatInterface( fn=generate, additional_inputs=[ gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS), gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6), gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9), gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50), gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2), ], examples=[ ["@tts1 Who is Nikola Tesla, and why did he die?"], ["A train travels 60 kilometers per hour. If it travels for 5 hours, how far will it travel in total?"], ["Write a Python function to check if a number is prime."], ["@tts2 What causes rainbows to form?"], ["Rewrite the following sentence in passive voice: 'The dog chased the cat.'"], ["@tts5 What is the capital of France?"], ], cache_examples=False, type="messages", description=DESCRIPTION, css=css, fill_height=True, textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True, ) if __name__ == "__main__": demo.queue(max_size=20).launch()