Create app-backup.py

app-backup.py

@@ -0,0 +1,349 @@
+#!/usr/bin/env python
+
+import os
+import re
+import tempfile
+from collections.abc import Iterator
+from threading import Thread
+
+import cv2
+import gradio as gr
+import spaces
+import torch
+from loguru import logger
+from PIL import Image
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer
+
+model_id = os.getenv("MODEL_ID", "google/gemma-3-27b-it")
+processor = AutoProcessor.from_pretrained(model_id, padding_side="left")
+model = Gemma3ForConditionalGeneration.from_pretrained(
+    model_id, device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="eager"
+)
+
+MAX_NUM_IMAGES = int(os.getenv("MAX_NUM_IMAGES", "5"))
+
+
+def count_files_in_new_message(paths: list[str]) -> tuple[int, int]:
+    image_count = 0
+    video_count = 0
+    for path in paths:
+        if path.endswith(".mp4"):
+            video_count += 1
+        else:
+            image_count += 1
+    return image_count, video_count
+
+
+def count_files_in_history(history: list[dict]) -> tuple[int, int]:
+    image_count = 0
+    video_count = 0
+    for item in history:
+        if item["role"] != "user" or isinstance(item["content"], str):
+            continue
+        if item["content"][0].endswith(".mp4"):
+            video_count += 1
+        else:
+            image_count += 1
+    return image_count, video_count
+
+
+def validate_media_constraints(message: dict, history: list[dict]) -> bool:
+    new_image_count, new_video_count = count_files_in_new_message(message["files"])
+    history_image_count, history_video_count = count_files_in_history(history)
+    image_count = history_image_count + new_image_count
+    video_count = history_video_count + new_video_count
+    if video_count > 1:
+        gr.Warning("Only one video is supported.")
+        return False
+    if video_count == 1:
+        if image_count > 0:
+            gr.Warning("Mixing images and videos is not allowed.")
+            return False
+        if "<image>" in message["text"]:
+            gr.Warning("Using <image> tags with video files is not supported.")
+            return False
+        # TODO: Add frame count validation for videos similar to image count limits  # noqa: FIX002, TD002, TD003
+    if video_count == 0 and image_count > MAX_NUM_IMAGES:
+        gr.Warning(f"You can upload up to {MAX_NUM_IMAGES} images.")
+        return False
+    if "<image>" in message["text"] and message["text"].count("<image>") != new_image_count:
+        gr.Warning("The number of <image> tags in the text does not match the number of images.")
+        return False
+    return True
+
+
+def downsample_video(video_path: str) -> list[tuple[Image.Image, float]]:
+    vidcap = cv2.VideoCapture(video_path)
+    fps = vidcap.get(cv2.CAP_PROP_FPS)
+    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    frame_interval = int(fps / 3)
+    frames = []
+
+    for i in range(0, total_frames, frame_interval):
+        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
+
+
+def process_video(video_path: str) -> list[dict]:
+    content = []
+    frames = downsample_video(video_path)
+    for frame in frames:
+        pil_image, timestamp = frame
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
+            pil_image.save(temp_file.name)
+            content.append({"type": "text", "text": f"Frame {timestamp}:"})
+            content.append({"type": "image", "url": temp_file.name})
+    logger.debug(f"{content=}")
+    return content
+
+
+def process_interleaved_images(message: dict) -> list[dict]:
+    logger.debug(f"{message['files']=}")
+    parts = re.split(r"(<image>)", message["text"])
+    logger.debug(f"{parts=}")
+
+    content = []
+    image_index = 0
+    for part in parts:
+        logger.debug(f"{part=}")
+        if part == "<image>":
+            content.append({"type": "image", "url": message["files"][image_index]})
+            logger.debug(f"file: {message['files'][image_index]}")
+            image_index += 1
+        elif part.strip():
+            content.append({"type": "text", "text": part.strip()})
+        elif isinstance(part, str) and part != "<image>":
+            content.append({"type": "text", "text": part})
+    logger.debug(f"{content=}")
+    return content
+
+
+def process_new_user_message(message: dict) -> list[dict]:
+    if not message["files"]:
+        return [{"type": "text", "text": message["text"]}]
+
+    if message["files"][0].endswith(".mp4"):
+        return [{"type": "text", "text": message["text"]}, *process_video(message["files"][0])]
+
+    if "<image>" in message["text"]:
+        return process_interleaved_images(message)
+
+    return [
+        {"type": "text", "text": message["text"]},
+        *[{"type": "image", "url": path} for path in message["files"]],
+    ]
+
+
+def process_history(history: list[dict]) -> list[dict]:
+    messages = []
+    current_user_content: list[dict] = []
+    for item in history:
+        if item["role"] == "assistant":
+            if current_user_content:
+                messages.append({"role": "user", "content": current_user_content})
+                current_user_content = []
+            messages.append({"role": "assistant", "content": [{"type": "text", "text": item["content"]}]})
+        else:
+            content = item["content"]
+            if isinstance(content, str):
+                current_user_content.append({"type": "text", "text": content})
+            else:
+                current_user_content.append({"type": "image", "url": content[0]})
+    return messages
+
+
+@spaces.GPU(duration=120)
+def run(message: dict, history: list[dict], system_prompt: str = "", max_new_tokens: int = 512) -> Iterator[str]:
+    if not validate_media_constraints(message, history):
+        yield ""
+        return
+
+    messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": [{"type": "text", "text": system_prompt}]})
+    messages.extend(process_history(history))
+    messages.append({"role": "user", "content": process_new_user_message(message)})
+
+    inputs = processor.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt",
+    ).to(device=model.device, dtype=torch.bfloat16)
+
+    streamer = TextIteratorStreamer(processor, timeout=30.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    output = ""
+    for delta in streamer:
+        output += delta
+        yield output
+
+
+examples = [
+    [
+        {
+            "text": "I need to be in Japan for 10 days, going to Tokyo, Kyoto and Osaka. Think about number of attractions in each of them and allocate number of days to each city. Make public transport recommendations.",
+            "files": [],
+        }
+    ],
+    [
+        {
+            "text": "Write the matplotlib code to generate the same bar chart.",
+            "files": ["assets/additional-examples/barchart.png"],
+        }
+    ],
+    [
+        {
+            "text": "What is odd about this video?",
+            "files": ["assets/additional-examples/tmp.mp4"],
+        }
+    ],
+    [
+        {
+            "text": "I already have this supplement <image> and I want to buy this one <image>. Any warnings I should know about?",
+            "files": ["assets/additional-examples/pill1.png", "assets/additional-examples/pill2.png"],
+        }
+    ],
+    [
+        {
+            "text": "Write a poem inspired by the visual elements of the images.",
+            "files": ["assets/sample-images/06-1.png", "assets/sample-images/06-2.png"],
+        }
+    ],
+    [
+        {
+            "text": "Compose a short musical piece inspired by the visual elements of the images.",
+            "files": [
+                "assets/sample-images/07-1.png",
+                "assets/sample-images/07-2.png",
+                "assets/sample-images/07-3.png",
+                "assets/sample-images/07-4.png",
+            ],
+        }
+    ],
+    [
+        {
+            "text": "Write a short story about what might have happened in this house.",
+            "files": ["assets/sample-images/08.png"],
+        }
+    ],
+    [
+        {
+            "text": "Create a short story based on the sequence of images.",
+            "files": [
+                "assets/sample-images/09-1.png",
+                "assets/sample-images/09-2.png",
+                "assets/sample-images/09-3.png",
+                "assets/sample-images/09-4.png",
+                "assets/sample-images/09-5.png",
+            ],
+        }
+    ],
+    [
+        {
+            "text": "Describe the creatures that would live in this world.",
+            "files": ["assets/sample-images/10.png"],
+        }
+    ],
+    [
+        {
+            "text": "Read text in the image.",
+            "files": ["assets/additional-examples/1.png"],
+        }
+    ],
+    [
+        {
+            "text": "When is this ticket dated and how much did it cost?",
+            "files": ["assets/additional-examples/2.png"],
+        }
+    ],
+    [
+        {
+            "text": "Read the text in the image into markdown.",
+            "files": ["assets/additional-examples/3.png"],
+        }
+    ],
+    [
+        {
+            "text": "Evaluate this integral.",
+            "files": ["assets/additional-examples/4.png"],
+        }
+    ],
+    [
+        {
+            "text": "caption this image",
+            "files": ["assets/sample-images/01.png"],
+        }
+    ],
+    [
+        {
+            "text": "What's the sign says?",
+            "files": ["assets/sample-images/02.png"],
+        }
+    ],
+    [
+        {
+            "text": "Compare and contrast the two images.",
+            "files": ["assets/sample-images/03.png"],
+        }
+    ],
+    [
+        {
+            "text": "List all the objects in the image and their colors.",
+            "files": ["assets/sample-images/04.png"],
+        }
+    ],
+    [
+        {
+            "text": "Describe the atmosphere of the scene.",
+            "files": ["assets/sample-images/05.png"],
+        }
+    ],
+]
+
+DESCRIPTION = """\
+<img src='https://huggingface.co/spaces/huggingface-projects/gemma-3-12b-it/resolve/main/assets/logo.png' id='logo' />
+
+This is a demo of Gemma 3 27B it, a vision language model with outstanding performance on a wide range of tasks.
+You can upload images, interleaved images and videos. Note that video input only supports single-turn conversation and mp4 input.
+"""
+
+demo = gr.ChatInterface(
+    fn=run,
+    type="messages",
+    chatbot=gr.Chatbot(type="messages", scale=1, allow_tags=["image"]),
+    textbox=gr.MultimodalTextbox(file_types=["image", ".mp4"], file_count="multiple", autofocus=True),
+    multimodal=True,
+    additional_inputs=[
+        gr.Textbox(label="System Prompt", value="You are a helpful assistant."),
+        gr.Slider(label="Max New Tokens", minimum=100, maximum=2000, step=10, value=700),
+    ],
+    stop_btn=False,
+    title="Gemma 3 27B IT",
+    description=DESCRIPTION,
+    examples=examples,
+    run_examples_on_click=False,
+    cache_examples=False,
+    css_paths="style.css",
+    delete_cache=(1800, 1800),
+)
+
+if __name__ == "__main__":
+    demo.launch()