Create app.py

app.py

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+import spaces
+import gradio as gr
+
+import subprocess  # 🥲
+subprocess.run(
+    "pip install flash-attn --no-build-isolation",
+    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    shell=True,
+)
+# subprocess.run(
+#     "pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git",
+#     shell=True,
+# )
+
+import torch
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava.conversation import conv_templates, SeparatorStyle
+import copy
+import warnings
+from decord import VideoReader, cpu
+import numpy as np
+import tempfile
+import os
+import shutil
+#warnings.filterwarnings("ignore")
+title = "# Demo of VLM on Crime scenes"
+description1 ="""The **🌋📹LLaVA-Video-7B-Qwen2** is a 7B parameter model  trained on the 🌋📹LLaVA-Video-178K dataset and the LLaVA-OneVision dataset. It is [based on the **Qwen2 language model**](https://huggingface.co/collections/Qwen/qwen2-6659360b33528ced941e557f), supporting a context window of up to 32K tokens. The model can process and interact with images, multi-images, and videos, with specific optimizations for video analysis.
+This model leverages the **SO400M vision backbone** for visual input and Qwen2 for language processing, making it highly efficient in multi-modal reasoning, including visual and video-based tasks.
+🌋📹LLaVA-Video has larger variants of [32B](https://huggingface.co/lmms-lab/LLaVA-NeXT-Video-32B-Qwen) and [72B](https://huggingface.co/lmms-lab/LLaVA-Video-72B-Qwen2) and with a [variant](https://huggingface.co/lmms-lab/LLaVA-Video-7B-Qwen2-Video-Only) only trained on the new synthetic data
+For further details, please visit the [Project Page](https://github.com/LLaVA-VL/LLaVA-NeXT) or check out the corresponding [research paper](https://arxiv.org/abs/2410.02713).
+- **Architecture**: `LlavaQwenForCausalLM`
+- **Attention Heads**: 28
+- **Hidden Layers**: 28
+- **Hidden Size**: 3584
+"""
+description2 ="""
+We have leveraged this VLM for Crime scene video description. The expected performance is achieved and we thank everyone who made this possible.
+"""
+
+
+def load_video(video_path, max_frames_num, fps=1, force_sample=False):
+    if max_frames_num == 0:
+        return np.zeros((1, 336, 336, 3))
+    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+    total_frame_num = len(vr)
+    video_time = total_frame_num / vr.get_avg_fps()
+    fps = round(vr.get_avg_fps()/fps)
+    frame_idx = [i for i in range(0, len(vr), fps)]
+    frame_time = [i/fps for i in frame_idx]
+    if len(frame_idx) > max_frames_num or force_sample:
+        sample_fps = max_frames_num
+        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int)
+        frame_idx = uniform_sampled_frames.tolist()
+        frame_time = [i/vr.get_avg_fps() for i in frame_idx]
+    frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
+    spare_frames = vr.get_batch(frame_idx).asnumpy()
+    return spare_frames, frame_time, video_time
+
+# Load the model
+pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2"
+model_name = "llava_qwen"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+device_map = "auto"
+
+print("Loading model...")
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map)
+model.eval()
+print("Model loaded successfully!")
+
+@spaces.GPU
+def process_video(video_path, question):
+    max_frames_num = 64
+    video, frame_time, video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
+    video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].to(device).bfloat16()
+    video = [video]
+
+    conv_template = "qwen_1_5"
+    time_instruction = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}. Please answer the following questions related to this video."
+    
+    full_question = DEFAULT_IMAGE_TOKEN + f"{time_instruction}\n{question}"
+    
+    conv = copy.deepcopy(conv_templates[conv_template])
+    conv.append_message(conv.roles[0], full_question)
+    conv.append_message(conv.roles[1], None)
+    prompt_question = conv.get_prompt()
+    
+    input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
+    
+    with torch.no_grad():
+        output = model.generate(
+            input_ids,
+            images=video,
+            modalities=["video"],
+            do_sample=False,
+            temperature=0,
+            max_new_tokens=4096,
+        )
+    
+    response = tokenizer.batch_decode(output, skip_special_tokens=True)[0].strip()
+    return response
+
+def gradio_interface(video_file, question):
+    if video_file is None:
+        return "Please upload a video file."
+    response = process_video(video_file, question)
+    return response
+
+with gr.Blocks() as demo:
+    gr.Markdown(title)
+    with gr.Row():
+        with gr.Group():
+            gr.Markdown(description1)
+        with gr.Group():
+            gr.Markdown(description2)
+    with gr.Row():
+        with gr.Column():
+            video_input = gr.Video()
+            question_input = gr.Textbox(label="🙋🏻‍♂️User Question", placeholder="Ask a question about the video... or Ask to describe the video")
+            submit_button = gr.Button("Ask")
+        output = gr.Textbox(label="VLM Bot")
+    
+    submit_button.click(
+        fn=gradio_interface,
+        inputs=[video_input, question_input],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch(show_error=True, ssr_mode = False)