Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, Llama4ForConditionalGeneration, TextIteratorStreamer
+from transformers.image_utils import load_image
+from threading import Thread
+import time
+import torch
+import spaces
+import cv2
+import numpy as np
+from PIL import Image
+
+def progress_bar_html(label: str) -> str:
+    """
+    Returns an HTML snippet for a thin progress bar with a label.
+    The progress bar is styled as a dark animated bar.
+    """
+    return f'''
+<div style="display: flex; align-items: center;">
+    <span style="margin-right: 10px; font-size: 14px;">{label}</span>
+    <div style="width: 110px; height: 5px; background-color: #9370DB; border-radius: 2px; overflow: hidden;">
+        <div style="width: 100%; height: 100%; background-color: #4B0082; animation: loading 1.5s linear infinite;"></div>
+    </div>
+</div>
+<style>
+@keyframes loading {{
+    0% {{ transform: translateX(-100%); }}
+    100% {{ transform: translateX(100%); }}
+}}
+</style>
+    '''
+
+def downsample_video(video_path):
+    """
+    Downsamples the video to 10 evenly spaced frames.
+    Each frame is converted to a PIL Image along with its timestamp.
+    """
+    vidcap = cv2.VideoCapture(video_path)
+    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = vidcap.get(cv2.CAP_PROP_FPS)
+    frames = []
+    if total_frames <= 0 or fps <= 0:
+        vidcap.release()
+        return frames
+    # Sample 10 evenly spaced frames.
+    frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)
+    for i in frame_indices:
+        vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
+        success, image = vidcap.read()
+        if success:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(image)
+            timestamp = round(i / fps, 2)
+            frames.append((pil_image, timestamp))
+    vidcap.release()
+    return frames
+
+MODEL_ID = "meta-llama/Llama-4-Scout-17B-16E-Instruct"  # Alternatively: "Qwen/Qwen2.5-VL-3B-Instruct" 
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+"""model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16
+).to("cuda").eval()"""
+
+model = Llama4ForConditionalGeneration.from_pretrained(
+    model_id,
+    attn_implementation="flex_attention",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+).to("cuda").eval()
+
+@spaces.GPU
+def model_inference(input_dict, history):
+    text = input_dict["text"]
+    files = input_dict["files"]
+
+    if text.strip().lower().startswith("@video-infer"):
+        # Remove the tag from the query.
+        text = text[len("@video-infer"):].strip()
+        if not files:
+            gr.Error("Please upload a video file along with your @video-infer query.")
+            return
+        # Assume the first file is a video.
+        video_path = files[0]
+        frames = downsample_video(video_path)
+        if not frames:
+            gr.Error("Could not process video.")
+            return
+        # Build messages: start with the text prompt.
+        messages = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": text}]
+            }
+        ]
+        # Append each frame with a timestamp label.
+        for image, timestamp in frames:
+            messages[0]["content"].append({"type": "text", "text": f"Frame {timestamp}:"})
+            messages[0]["content"].append({"type": "image", "image": image})
+        # Collect only the images from the frames.
+        video_images = [image for image, _ in frames]
+        # Prepare the prompt.
+        prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = processor(
+            text=[prompt],
+            images=video_images,
+            return_tensors="pt",
+            padding=True,
+        ).to("cuda")
+        # Set up streaming generation.
+        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+        generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
+        thread = Thread(target=model.generate, kwargs=generation_kwargs)
+        thread.start()
+        buffer = ""
+        yield progress_bar_html("Processing video with Qwen2.5VL Model")
+        for new_text in streamer:
+            buffer += new_text
+            time.sleep(0.01)
+            yield buffer
+        return
+
+    if len(files) > 1:
+        images = [load_image(image) for image in files]
+    elif len(files) == 1:
+        images = [load_image(files[0])]
+    else:
+        images = []
+
+    if text == "" and not images:
+        gr.Error("Please input a query and optionally image(s).")
+        return
+    if text == "" and images:
+        gr.Error("Please input a text query along with the image(s).")
+        return
+
+    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 if images else None,
+        return_tensors="pt",
+        padding=True,
+    ).to("cuda")
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    buffer = ""
+    yield progress_bar_html("Processing with Qwen2.5VL Model")
+    for new_text in streamer:
+        buffer += new_text
+        time.sleep(0.01)
+        yield buffer
+
+examples = [
+    [{"text": "Describe the Image?", "files": ["example_images/document.jpg"]}],
+    [{"text": "@video-infer Explain the content of the Advertisement", "files": ["example_images/videoplayback.mp4"]}],
+    [{"text": "@video-infer Explain the content of the video in detail", "files": ["example_images/breakfast.mp4"]}],
+    [{"text": "@video-infer Explain the content of the video.", "files": ["example_images/sky.mp4"]}],
+]
+
+demo = gr.ChatInterface(
+    fn=model_inference,
+    description="# **meta-llama/Llama-4-Scout-17B-16E-Instruct `@video-infer for video understanding`** (based on demo from here: https://huggingface.co/spaces/prithivMLmods/Qwen2.5-VL-7B-Instruct)",
+    examples=examples,
+    fill_height=True,
+    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image", "video"], file_count="multiple"),
+    stop_btn="Stop Generation",
+    multimodal=True,
+    cache_examples=False,
+)
+
+demo.launch(debug=True)