Update app.py

app.py

@@ -1,63 +1,95 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+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
+from llava.conversation import conv_templates
+import copy
+from decord import VideoReader, cpu
+import numpy as np
 
+title = "# 🎥 Instagram Short Video Analyzer with LLaVA-Video"
+description = """
+This application uses the LLaVA-Video-7B-Qwen2 model to analyze Instagram short videos.
+Upload your Instagram short video and ask questions about its content!
 """
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def load_video(video_path, max_frames_num=64, fps=1):
+    vr = VideoReader(video_path, ctx=cpu(0))
+    total_frame_num = len(vr)
+    video_time = total_frame_num / vr.get_avg_fps()
+    fps = round(vr.get_avg_fps()/fps)
+    frame_idx = list(range(0, len(vr), fps))
+    if len(frame_idx) > max_frames_num:
+        frame_idx = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int).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
 
-    messages.append({"role": "user", "content": message})
+# 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"
 
-    response = ""
+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!")
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+def process_instagram_short(video_path, question):
+    max_frames_num = 64
+    video, frame_time, video_time = load_video(video_path, max_frames_num)
+    video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].to(device).bfloat16()
+    video = [video]
 
-        response += token
-        yield response
+    time_instruction = f"This is an Instagram short video lasting {video_time:.2f} seconds. {len(video[0])} frames were sampled at {frame_time}. Analyze this short video and answer the following question:"
+    
+    full_question = DEFAULT_IMAGE_TOKEN + f"{time_instruction}\n{question}"
+    
+    conv = copy.deepcopy(conv_templates["qwen_1_5"])
+    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
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def gradio_interface(video_file, question):
+    if video_file is None:
+        return "Please upload an Instagram short video."
+    response = process_instagram_short(video_file, question)
+    return response
 
+with gr.Blocks() as demo:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    
+    with gr.Row():
+        with gr.Column():
+            video_input = gr.Video(label="Upload Instagram Short Video")
+            question_input = gr.Textbox(label="Ask a question about the video", placeholder="What's happening in this Instagram short?")
+            submit_button = gr.Button("Analyze Short Video")
+        output = gr.Textbox(label="Analysis Result")
+    
+    submit_button.click(
+        fn=gradio_interface,
+        inputs=[video_input, question_input],
+        outputs=output
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(show_error=True)