Create app.py

app.py

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+import gradio as gr
+import spaces
+import torch
+import os
+import uuid
+import io
+import numpy as np
+from PIL import Image
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+from decord import VideoReader, cpu
+
+# =============================================================================
+# InternVL 전처리/로딩 코드 (원본 예시에서 발췌)
+# =============================================================================
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            # 이미지 면적 기준으로 좀 더 큰 쪽 선택
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) 
+        for i in range(1, n + 1) 
+        for j in range(1, n + 1) 
+        if i * j <= max_num and i * j >= min_num
+    )
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size
+    )
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(img) for img in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
+    if bound:
+        start, end = bound[0], bound[1]
+    else:
+        start, end = -100000, 100000
+    start_idx = max(first_idx, round(start * fps))
+    end_idx = min(round(end * fps), max_frame)
+    seg_size = float(end_idx - start_idx) / num_segments
+    frame_indices = np.array([
+        int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
+        for idx in range(num_segments)
+    ])
+    return frame_indices
+
+def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=8):
+    """
+    InternVL 예시 코드 참고: 여러 프레임을 추출하여 dynamic_preprocess 적용.
+    여기서는 기본적으로 num_segments=8로 설정.
+    """
+    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+    max_frame = len(vr) - 1
+    fps = float(vr.get_avg_fps())
+
+    pixel_values_list, num_patches_list = [], []
+    transform = build_transform(input_size=input_size)
+    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
+
+    for frame_index in frame_indices:
+        frame = vr[frame_index]
+        img = Image.fromarray(frame.asnumpy()).convert('RGB')
+        processed_imgs = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
+        tile_values = [transform(tile) for tile in processed_imgs]
+        tile_values = torch.stack(tile_values)
+        num_patches_list.append(tile_values.shape[0])
+        pixel_values_list.append(tile_values)
+
+    # 여러 프레임을 이어 붙여 최종 pixel_values 생성
+    pixel_values = torch.cat(pixel_values_list, dim=0)  # (sum(num_patches_list), 3, H, W)
+    return pixel_values, num_patches_list
+
+
+# =============================================================================
+# InternVL 모델 로딩
+# =============================================================================
+MODEL_ID = "OpenGVLab/InternVL2_5-8B"
+
+model = AutoModel.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=True,
+    trust_remote_code=True
+).eval().cuda()
+
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    use_fast=False
+)
+
+# Gradio 상단에 표시할 설명 문구
+DESCRIPTION = "[InternVL2_5-8B Demo](https://github.com/OpenGVLab/InternVL) - Using the InternVL2_5-8B"
+
+image_extensions = Image.registered_extensions()
+video_extensions = ("avi", "mp4", "mov", "mkv", "flv", "wmv", "mjpeg", "wav", "gif", "webm", "m4v", "3gp")
+
+def identify_and_save_blob(blob_path):
+    """
+    Qwen 예제 코드와 동일: blob을 열어보고 이미지인지 영상인지 확인 후,
+    임시 파일로 저장하여 경로 리턴
+    """
+    try:
+        with open(blob_path, 'rb') as file:
+            blob_content = file.read()
+            # Try to identify if it's an image
+            try:
+                Image.open(io.BytesIO(blob_content)).verify()  # Check if it's a valid image
+                extension = ".png"  # Default to PNG for saving
+                media_type = "image"
+            except (IOError, SyntaxError):
+                # If it's not a valid image, assume it's a video
+                extension = ".mp4"  # Default to MP4 for saving
+                media_type = "video"
+            
+            # Create a unique filename
+            filename = f"temp_{uuid.uuid4()}_media{extension}"
+            with open(filename, "wb") as f:
+                f.write(blob_content)
+            return filename, media_type
+    except FileNotFoundError:
+        raise ValueError(f"The file {blob_path} was not found.")
+    except Exception as e:
+        raise ValueError(f"An error occurred while processing the file: {e}")
+
+def process_file_upload(file_path):
+    """
+    파일 업로드 시 이미지/영상 미리보기 혹은 그대로 패스.
+    """
+    if isinstance(file_path, str):
+        if file_path.endswith(tuple([i for i, f in image_extensions.items()])):
+            # 이미지를 열어서 preview로 넘김
+            return file_path, Image.open(file_path)
+        elif file_path.endswith(video_extensions):
+            # 영상은 preview를 None으로
+            return file_path, None
+        else:
+            # blob 파일인 경우 처리
+            try:
+                media_path, media_type = identify_and_save_blob(file_path)
+                if media_type == "image":
+                    return media_path, Image.open(media_path)
+                return media_path, None
+            except Exception as e:
+                print(e)
+                raise ValueError("Unsupported media type. Please upload an image or video.")
+    return None, None
+
+@spaces.GPU
+def internvl_inference(media_input, text_input=None):
+    """
+    Qwen 예제의 qwen_inference 대신 InternVL을 이용한 추론 함수.
+    - 이미지/영상 파일을 InternVL에서 요구하는 pixel_values로 변환 후
+      model.chat() 호출하여 답변 생성.
+    """
+    if isinstance(media_input, str):  # If it's a filepath
+        media_path = media_input
+
+        # 미디어 종류 식별
+        if media_path.endswith(tuple([i for i, f in image_extensions.items()])):
+            media_type = "image"
+        elif media_path.endswith(video_extensions):
+            media_type = "video"
+        else:
+            # blob인지 체크
+            try:
+                media_path, media_type = identify_and_save_blob(media_input)
+            except Exception as e:
+                print(e)
+                raise ValueError("Unsupported media type. Please upload an image or video.")
+    else:
+        return "No media input found"
+
+    # 이미지 vs 영상 처리
+    if media_type == "image":
+        # 단일 이미지만 처리한다고 가정 (멀티-이미지도 확장 가능)
+        pixel_values = load_image(media_path, max_num=12)
+        pixel_values = pixel_values.to(torch.bfloat16).cuda()  # (N, 3, H, W)
+        # InternVL 대화
+        question = f"<image>\n{text_input}" if text_input else "<image>\n"
+        generation_config = dict(max_new_tokens=1024, do_sample=True)
+
+        response = model.chat(
+            tokenizer,
+            pixel_values,
+            question,
+            generation_config
+        )
+        return response
+
+    elif media_type == "video":
+        # 영상: 예시로 첫 8프레임에 대해 처리
+        pixel_values, num_patches_list = load_video(
+            media_path,
+            num_segments=8,
+            max_num=1
+        )
+        pixel_values = pixel_values.to(torch.bfloat16).cuda()
+        question_prefix = "".join([f"Frame{i+1}: <image>\n" for i in range(len(num_patches_list))])
+        question = question_prefix + (text_input if text_input else "")
+        generation_config = dict(max_new_tokens=1024, do_sample=True)
+
+        # 영상에서도 동일한 chat() 함수 사용
+        response = model.chat(
+            tokenizer,
+            pixel_values,
+            question,
+            generation_config,
+            num_patches_list=num_patches_list
+        )
+        return response
+
+    return "Unsupported media type"
+
+# 간단한 CSS
+css = """
+  #output {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+# Gradio 데모 구성
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Tab(label="Image/Video Input"):
+        with gr.Row():
+            with gr.Column():
+                input_media = gr.File(
+                    label="Upload Image or Video", type="filepath"
+                )
+                preview_image = gr.Image(label="Preview", visible=True)
+                text_input = gr.Textbox(label="Question")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+
+        input_media.change(
+            fn=process_file_upload,
+            inputs=[input_media],
+            outputs=[input_media, preview_image]
+        )
+        
+        submit_btn.click(
+            internvl_inference, 
+            [input_media, text_input], 
+            [output_text]
+        )
+
+demo.launch(debug=True)