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import gradio as gr |
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import torch |
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from threading import Thread |
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from transformers import ( |
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SmolVLMProcessor, |
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AutoModelForImageTextToText, |
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TextIteratorStreamer, |
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) |
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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MODEL_ID = "HuggingFaceTB/SmolVLM2-2.2B-Instruct" |
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processor = SmolVLMProcessor.from_pretrained(MODEL_ID) |
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model = AutoModelForImageTextToText.from_pretrained( |
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MODEL_ID, |
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torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32, |
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).to(DEVICE).eval() |
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def analyze_stream(text, image, max_tokens): |
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if image is None and not text.strip(): |
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return "❌ Veuillez fournir un texte ou une image." |
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content = [] |
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if image: |
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content.append({"type": "image", "path": image}) |
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if text.strip(): |
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content.append({"type": "text", "text": text}) |
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messages = [{"role": "user", "content": content}] |
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inputs = processor.apply_chat_template( |
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messages, |
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add_generation_prompt=True, |
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tokenize=True, |
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return_tensors="pt", |
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).to(DEVICE) |
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streamer = TextIteratorStreamer( |
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processor, |
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skip_prompt=True, |
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skip_special_tokens=True, |
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) |
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Thread( |
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target=model.generate, |
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kwargs=dict( |
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**inputs, |
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streamer=streamer, |
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max_new_tokens=max_tokens, |
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do_sample=False, |
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temperature=0.0, |
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), |
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).start() |
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output = "" |
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for token in streamer: |
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output += token |
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yield output |
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with gr.Blocks(theme=gr.themes.Soft()) as demo: |
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gr.Markdown("## ⚡ SmolVLM2 – Analyse Temps Réel") |
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with gr.Row(): |
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with gr.Column(): |
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txt = gr.Textbox( |
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label="Question / Description", |
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lines=3, |
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) |
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img = gr.Image(type="filepath", label="Image") |
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max_tokens = gr.Slider( |
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50, 400, value=200, step=50, label="Max Tokens" |
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) |
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btn = gr.Button("🚀 Analyser", variant="primary") |
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with gr.Column(): |
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out = gr.Textbox( |
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label="Réponse en Temps Réel", |
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lines=14, |
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) |
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btn.click( |
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fn=analyze_stream, |
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inputs=[txt, img, max_tokens], |
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outputs=out, |
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) |
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demo.launch() |
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