Update app.py

app.py

@@ -1,329 +1,471 @@
-import os
-import time
-import cv2
-import imutils
-import shutil
-import img2pdf
-import glob
-from skimage.metrics import structural_similarity
-import gradio as gr
-import tempfile
-
-############# Define constants
-
-OUTPUT_SLIDES_DIR = f"./output"
-
-FRAME_RATE = 3                   # no.of frames per second that needs to be processed, fewer the count faster the speed
-WARMUP = FRAME_RATE              # initial number of frames to be skipped
-FGBG_HISTORY = FRAME_RATE * 15   # no.of frames in background object
-VAR_THRESHOLD = 16               # Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model.
-DETECT_SHADOWS = False            # If true, the algorithm will detect shadows and mark them.
-MIN_PERCENT = 0.1                # min % of diff between foreground and background to detect if motion has stopped
-MAX_PERCENT = 3                  # max % of diff between foreground and background to detect if frame is still in motion
-SSIM_THRESHOLD = 0.9             # SSIM threshold of two consecutive frame
-
-
-def get_frames(video_path):
-    '''A fucntion to return the frames from a video located at video_path
-    this function skips frames as defined in FRAME_RATE'''
-    
-    
-    # open a pointer to the video file initialize the width and height of the frame
-    vs = cv2.VideoCapture(video_path)
-    if not vs.isOpened():
-        raise Exception(f'unable to open file {video_path}')
-
-
-    total_frames = vs.get(cv2.CAP_PROP_FRAME_COUNT)
-    frame_time = 0
-    frame_count = 0
-
-    # loop over the frames of the video
-    while True:
-        vs.set(cv2.CAP_PROP_POS_MSEC, frame_time * 1000)    # move frame to a timestamp
-        frame_time += 1/FRAME_RATE
-
-        (_, frame) = vs.read()
-        # if the frame is None, then we have reached the end of the video file
-        if frame is None:
-            break
-
-        frame_count += 1
-        yield frame_count, frame_time, frame
-
-    vs.release()
- 
-
-
-def detect_unique_screenshots(video_path, output_folder_screenshot_path, progress=gr.Progress()):
-    '''Extract unique screenshots from video'''
-    fgbg = cv2.createBackgroundSubtractorMOG2(history=FGBG_HISTORY, varThreshold=VAR_THRESHOLD,detectShadows=DETECT_SHADOWS)
-
-    captured = False
-    start_time = time.time()
-    (W, H) = (None, None)
-
-    # Get total frames for progress calculation
-    cap = cv2.VideoCapture(video_path)
-    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    cap.release()
-
-    screenshoots_count = 0
-    last_screenshot = None
-    saved_files = []
-    
-    progress(0, desc="初始化视频处理...")
-    
-    for frame_count, frame_time, frame in get_frames(video_path):
-        # Update progress
-        progress((frame_count / total_frames) * 0.7, desc=f"处理视频帧 {frame_count}/{total_frames}")
-        
-        orig = frame.copy()
-        frame = imutils.resize(frame, width=600)
-        mask = fgbg.apply(frame)
-
-        if W is None or H is None:
-            (H, W) = mask.shape[:2]
-
-        p_diff = (cv2.countNonZero(mask) / float(W * H)) * 100
-
-        if p_diff < MIN_PERCENT and not captured and frame_count > WARMUP:
-            captured = True
-            filename = f"{screenshoots_count:03}_{round(frame_time/60, 2)}.png"
-            path = os.path.join(output_folder_screenshot_path, filename)
-
-            image_ssim = 0.0
-            if last_screenshot is not None:
-                image_ssim = structural_similarity(last_screenshot, orig, channel_axis=2, data_range=255)
-
-            if image_ssim < SSIM_THRESHOLD:
-                try:
-                    progress(0.7 + (screenshoots_count * 0.1), desc=f"保存截图 {screenshoots_count + 1}")
-                    print("saving {}".format(path))
-                    cv2.imwrite(str(path), orig)
-                    last_screenshot = orig
-                    saved_files.append(path)
-                    screenshoots_count += 1
-                except Exception as e:
-                    print(f"Error saving image: {str(e)}")
-                    continue
-
-        elif captured and p_diff >= MAX_PERCENT:
-            captured = False
-
-    progress(0.8, desc="截图提取完成")
-    print(f'{screenshoots_count} screenshots Captured!')
-    print(f'Time taken {time.time()-start_time}s')
-    return saved_files
-
-
-def initialize_output_folder(video_path):
-    '''Clean the output folder if already exists'''
-    # Create a safe folder name from video filename
-    video_filename = os.path.splitext(os.path.basename(video_path))[0]
-    # Replace potentially problematic characters
-    safe_filename = "".join(x for x in video_filename if x.isalnum() or x in (' ', '-', '_'))
-    output_folder_screenshot_path = os.path.join(OUTPUT_SLIDES_DIR, safe_filename)
-
-    if os.path.exists(output_folder_screenshot_path):
-        shutil.rmtree(output_folder_screenshot_path)
-
-    os.makedirs(output_folder_screenshot_path, exist_ok=True)
-    print('initialized output folder', output_folder_screenshot_path)
-    return output_folder_screenshot_path
-
-
-def convert_screenshots_to_pdf(video_path, output_folder_screenshot_path):
-    # Create a safe filename
-    video_filename = os.path.splitext(os.path.basename(video_path))[0]
-    safe_filename = "".join(x for x in video_filename if x.isalnum() or x in (' ', '-', '_'))
-    output_pdf_path = os.path.join(OUTPUT_SLIDES_DIR, f"{safe_filename}.pdf")
-    
-    try:
-        print('output_folder_screenshot_path', output_folder_screenshot_path)
-        print('output_pdf_path', output_pdf_path)
-        print('converting images to pdf..')
-        
-        # Get all PNG files and ensure they exist
-        png_files = sorted(glob.glob(os.path.join(output_folder_screenshot_path, "*.png")))
-        if not png_files:
-            raise Exception("No PNG files found to convert to PDF")
-            
-        with open(output_pdf_path, "wb") as f:
-            f.write(img2pdf.convert(png_files))
-            
-        print('Pdf Created!')
-        print('pdf saved at', output_pdf_path)
-        return output_pdf_path
-    except Exception as e:
-        print(f"Error creating PDF: {str(e)}")
-        raise
-
-
-def video_to_slides(video_path, progress=gr.Progress()):
-    progress(0.1, desc="准备处理视频...")
-    output_folder_screenshot_path = initialize_output_folder(video_path)
-    saved_files = detect_unique_screenshots(video_path, output_folder_screenshot_path, progress)
-    return output_folder_screenshot_path, saved_files
-
-
-def slides_to_pdf(video_path, output_folder_screenshot_path, saved_files, progress=gr.Progress()):
-    video_filename = os.path.splitext(os.path.basename(video_path))[0]
-    safe_filename = "".join(x for x in video_filename if x.isalnum() or x in (' ', '-', '_'))
-    output_pdf_path = os.path.join(OUTPUT_SLIDES_DIR, f"{safe_filename}.pdf")
-    
-    try:
-        progress(0.9, desc="正在生成PDF...")
-        print('output_folder_screenshot_path', output_folder_screenshot_path)
-        print('output_pdf_path', output_pdf_path)
-        
-        if not saved_files:
-            raise Exception("未从视频中捕获到截图")
-            
-        existing_files = [f for f in saved_files if os.path.exists(f)]
-        if not existing_files:
-            raise Exception("未找到保存的截图文件")
-            
-        with open(output_pdf_path, "wb") as f:
-            f.write(img2pdf.convert(existing_files))
-            
-        progress(1.0, desc="处理完成！")
-        print('PDF创建成功！')
-        print('PDF保存位置:', output_pdf_path)
-        return output_pdf_path
-    except Exception as e:
-        print(f"创建PDF时出错: {str(e)}")
-        raise
-
-
-def run_app(video_path, progress=gr.Progress()):
-    try:
-        if not video_path:
-            raise gr.Error("请选择要处理的视频文件")
-            
-        progress(0, desc="开始处理...")
-        output_folder_screenshot_path, saved_files = video_to_slides(video_path, progress)
-        return slides_to_pdf(video_path, output_folder_screenshot_path, saved_files, progress)
-    except Exception as e:
-        raise gr.Error(f"处理失败: {str(e)}")
-
-
-def process_video_file(video_file):
-    """Handle uploaded video file and return PDF"""
-    try:
-        # If video_file is a string (path), use it directly
-        if isinstance(video_file, str):
-            if video_file.strip() == "":
-                return None
-            return run_app(video_file)
-            
-        # If it's an uploaded file, create a temporary file
-        if video_file is not None:
-            # Generate a unique filename for the temporary video
-            temp_filename = f"temp_video_{int(time.time())}.mp4"
-            temp_path = os.path.join(tempfile.gettempdir(), temp_filename)
-            
-            try:
-                if hasattr(video_file, 'name'):  # If it's already a file path
-                    shutil.copyfile(video_file, temp_path)
-                else:  # If it's file content
-                    with open(temp_path, 'wb') as f:
-                        f.write(video_file)
-                
-                # Process the video
-                output_folder_screenshot_path, saved_files = video_to_slides(temp_path)
-                pdf_path = slides_to_pdf(temp_path, output_folder_screenshot_path, saved_files)
-                
-                # Cleanup
-                if os.path.exists(temp_path):
-                    os.unlink(temp_path)
-                return pdf_path
-                
-            except Exception as e:
-                if os.path.exists(temp_path):
-                    os.unlink(temp_path)
-                raise gr.Error(f"处理视频时出错: {str(e)}")
-        return None
-    except Exception as e:
-        raise gr.Error(f"处理视频时出错: {str(e)}")
-
-# Create a modern interface with custom CSS
-css = """
-.gradio-container {
-    font-family: 'SF Pro Display', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
-}
-.container {
-    max-width: 900px;
-    margin: auto;
-    padding: 20px;
-}
-.gr-button {
-    background: linear-gradient(90deg, #2563eb, #3b82f6);
-    border: none;
-    color: white;
-}
-.gr-button:hover {
-    background: linear-gradient(90deg, #1d4ed8, #2563eb);
-    transform: translateY(-1px);
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
-}
-.status-info {
-    margin-top: 10px;
-    padding: 10px;
-    border-radius: 4px;
-    background-color: #f3f4f6;
-}
-"""
-
-with gr.Blocks(css=css) as iface:
-    gr.Markdown(
-        """
-        # 🎥 视频转PDF智能助手
-        
-        ### 轻松将视频转换为高质量PDF文档
-        公众号：正经人王同学 | 全网同名
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            video_input = gr.Video(label="上传视频")
-            video_path = gr.Textbox(label="或输入视频路径", placeholder="例如: ./input/video.mp4")
-            convert_btn = gr.Button("开始转换", variant="primary")
-        
-    with gr.Row():
-        output_file = gr.File(label="下载PDF")
-        
-    with gr.Row():
-        status = gr.Markdown(value="", elem_classes=["status-info"])
-    
-    gr.Markdown(
-        """
-        ### 使用说明
-        1. 上传视频文件 或 输入视频文件路径
-        2. 点击"开始转换"按钮
-        3. 等待处理完成后下载生成的PDF文件
-        
-        ### 特点
-        - 智能检测视频关键帧
-        - 高质量PDF输出
-        - 支持多种视频格式
-        """
-    )
-    
-    def process_video(video, path):
-        if video:
-            return run_app(video)
-        elif path:
-            return run_app(path)
-        else:
-            raise gr.Error("请上传视频或输入视频路径")
-    
-    convert_btn.click(
-        fn=process_video,
-        inputs=[video_input, video_path],
-        outputs=[output_file],
-    )
-
-if __name__ == "__main__":
-    iface.launch()
+import os
+import time
+import cv2
+import imutils
+import shutil
+import img2pdf
+import glob
+from skimage.metrics import structural_similarity
+import gradio as gr
+import tempfile
+import whisper
+from moviepy.editor import VideoFileClip
+from PIL import Image, ImageDraw, ImageFont
+
+############# Define constants
+
+OUTPUT_SLIDES_DIR = f"./output"
+
+FRAME_RATE = 3                   # no.of frames per second that needs to be processed, fewer the count faster the speed
+WARMUP = FRAME_RATE              # initial number of frames to be skipped
+FGBG_HISTORY = FRAME_RATE * 15   # no.of frames in background object
+VAR_THRESHOLD = 16               # Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model.
+DETECT_SHADOWS = False            # If true, the algorithm will detect shadows and mark them.
+MIN_PERCENT = 0.1                # min % of diff between foreground and background to detect if motion has stopped
+MAX_PERCENT = 3                  # max % of diff between foreground and background to detect if frame is still in motion
+SSIM_THRESHOLD = 0.9             # SSIM threshold of two consecutive frame
+
+
+def get_frames(video_path):
+    '''A fucntion to return the frames from a video located at video_path
+    this function skips frames as defined in FRAME_RATE'''
+    
+    
+    # open a pointer to the video file initialize the width and height of the frame
+    vs = cv2.VideoCapture(video_path)
+    if not vs.isOpened():
+        raise Exception(f'unable to open file {video_path}')
+
+
+    total_frames = vs.get(cv2.CAP_PROP_FRAME_COUNT)
+    frame_time = 0
+    frame_count = 0
+
+    # loop over the frames of the video
+    while True:
+        vs.set(cv2.CAP_PROP_POS_MSEC, frame_time * 1000)    # move frame to a timestamp
+        frame_time += 1/FRAME_RATE
+
+        (_, frame) = vs.read()
+        # if the frame is None, then we have reached the end of the video file
+        if frame is None:
+            break
+
+        frame_count += 1
+        yield frame_count, frame_time, frame
+
+    vs.release()
+ 
+
+
+def detect_unique_screenshots(video_path, output_folder_screenshot_path, progress=gr.Progress()):
+    '''Extract unique screenshots from video'''
+    fgbg = cv2.createBackgroundSubtractorMOG2(history=FGBG_HISTORY, varThreshold=VAR_THRESHOLD,detectShadows=DETECT_SHADOWS)
+
+    captured = False
+    start_time = time.time()
+    (W, H) = (None, None)
+
+    # Get total frames for progress calculation
+    cap = cv2.VideoCapture(video_path)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    cap.release()
+
+    screenshoots_count = 0
+    last_screenshot = None
+    saved_files = []
+    
+    progress(0, desc="初始化视频处理...")
+    
+    for frame_count, frame_time, frame in get_frames(video_path):
+        # Update progress
+        progress((frame_count / total_frames) * 0.7, desc=f"处理视频帧 {frame_count}/{total_frames}")
+        
+        orig = frame.copy()
+        frame = imutils.resize(frame, width=600)
+        mask = fgbg.apply(frame)
+
+        if W is None or H is None:
+            (H, W) = mask.shape[:2]
+
+        p_diff = (cv2.countNonZero(mask) / float(W * H)) * 100
+
+        if p_diff < MIN_PERCENT and not captured and frame_count > WARMUP:
+            captured = True
+            filename = f"{screenshoots_count:03}_{round(frame_time/60, 2)}.png"
+            path = os.path.join(output_folder_screenshot_path, filename)
+
+            image_ssim = 0.0
+            if last_screenshot is not None:
+                image_ssim = structural_similarity(last_screenshot, orig, channel_axis=2, data_range=255)
+
+            if image_ssim < SSIM_THRESHOLD:
+                try:
+                    progress(0.7 + (screenshoots_count * 0.1), desc=f"保存截图 {screenshoots_count + 1}")
+                    print("saving {}".format(path))
+                    cv2.imwrite(str(path), orig)
+                    last_screenshot = orig
+                    saved_files.append(path)
+                    screenshoots_count += 1
+                except Exception as e:
+                    print(f"Error saving image: {str(e)}")
+                    continue
+
+        elif captured and p_diff >= MAX_PERCENT:
+            captured = False
+
+    progress(0.8, desc="截图提取完成")
+    print(f'{screenshoots_count} screenshots Captured!')
+    print(f'Time taken {time.time()-start_time}s')
+    return saved_files
+
+
+def initialize_output_folder(video_path):
+    '''Clean the output folder if already exists'''
+    # Create a safe folder name from video filename
+    video_filename = os.path.splitext(os.path.basename(video_path))[0]
+    # Replace potentially problematic characters
+    safe_filename = "".join(x for x in video_filename if x.isalnum() or x in (' ', '-', '_'))
+    output_folder_screenshot_path = os.path.join(OUTPUT_SLIDES_DIR, safe_filename)
+
+    if os.path.exists(output_folder_screenshot_path):
+        shutil.rmtree(output_folder_screenshot_path)
+
+    os.makedirs(output_folder_screenshot_path, exist_ok=True)
+    print('initialized output folder', output_folder_screenshot_path)
+    return output_folder_screenshot_path
+
+
+def convert_screenshots_to_pdf(video_path, output_folder_screenshot_path):
+    # Create a safe filename
+    video_filename = os.path.splitext(os.path.basename(video_path))[0]
+    safe_filename = "".join(x for x in video_filename if x.isalnum() or x in (' ', '-', '_'))
+    output_pdf_path = os.path.join(OUTPUT_SLIDES_DIR, f"{safe_filename}.pdf")
+    
+    try:
+        print('output_folder_screenshot_path', output_folder_screenshot_path)
+        print('output_pdf_path', output_pdf_path)
+        print('converting images to pdf..')
+        
+        # Get all PNG files and ensure they exist
+        png_files = sorted(glob.glob(os.path.join(output_folder_screenshot_path, "*.png")))
+        if not png_files:
+            raise Exception("No PNG files found to convert to PDF")
+            
+        with open(output_pdf_path, "wb") as f:
+            f.write(img2pdf.convert(png_files))
+            
+        print('Pdf Created!')
+        print('pdf saved at', output_pdf_path)
+        return output_pdf_path
+    except Exception as e:
+        print(f"Error creating PDF: {str(e)}")
+        raise
+
+
+def video_to_slides(video_path, progress=gr.Progress()):
+    progress(0.1, desc="准备处理视频...")
+    output_folder_screenshot_path = initialize_output_folder(video_path)
+    saved_files = detect_unique_screenshots(video_path, output_folder_screenshot_path, progress)
+    return output_folder_screenshot_path, saved_files
+
+
+def slides_to_pdf(video_path, output_folder_screenshot_path, saved_files, progress=gr.Progress()):
+    video_filename = os.path.splitext(os.path.basename(video_path))[0]
+    safe_filename = "".join(x for x in video_filename if x.isalnum() or x in (' ', '-', '_'))
+    output_pdf_path = os.path.join(OUTPUT_SLIDES_DIR, f"{safe_filename}.pdf")
+    
+    try:
+        progress(0.9, desc="正在生成PDF...")
+        print('output_folder_screenshot_path', output_folder_screenshot_path)
+        print('output_pdf_path', output_pdf_path)
+        
+        if not saved_files:
+            raise Exception("未从视频中捕获到截图")
+            
+        existing_files = [f for f in saved_files if os.path.exists(f)]
+        if not existing_files:
+            raise Exception("未找到保存的截图文件")
+            
+        with open(output_pdf_path, "wb") as f:
+            f.write(img2pdf.convert(existing_files))
+            
+        progress(1.0, desc="处理完成！")
+        print('PDF创建成功！')
+        print('PDF保存���置:', output_pdf_path)
+        return output_pdf_path
+    except Exception as e:
+        print(f"创建PDF时出错: {str(e)}")
+        raise
+
+
+def run_app(video_path, progress=gr.Progress()):
+    try:
+        if not video_path:
+            raise gr.Error("请选择要处理的视频文件")
+            
+        progress(0, desc="开始处理...")
+        output_folder_screenshot_path, saved_files = video_to_slides(video_path, progress)
+        return slides_to_pdf(video_path, output_folder_screenshot_path, saved_files, progress)
+    except Exception as e:
+        raise gr.Error(f"处理失败: {str(e)}")
+
+
+def process_video_file(video_file):
+    """Handle uploaded video file and return PDF"""
+    try:
+        # If video_file is a string (path), use it directly
+        if isinstance(video_file, str):
+            if video_file.strip() == "":
+                return None
+            return run_app(video_file)
+            
+        # If it's an uploaded file, create a temporary file
+        if video_file is not None:
+            # Generate a unique filename for the temporary video
+            temp_filename = f"temp_video_{int(time.time())}.mp4"
+            temp_path = os.path.join(tempfile.gettempdir(), temp_filename)
+            
+            try:
+                if hasattr(video_file, 'name'):  # If it's already a file path
+                    shutil.copyfile(video_file, temp_path)
+                else:  # If it's file content
+                    with open(temp_path, 'wb') as f:
+                        f.write(video_file)
+                
+                # Process the video
+                output_folder_screenshot_path, saved_files = video_to_slides(temp_path)
+                pdf_path = slides_to_pdf(temp_path, output_folder_screenshot_path, saved_files)
+                
+                # Cleanup
+                if os.path.exists(temp_path):
+                    os.unlink(temp_path)
+                return pdf_path
+                
+            except Exception as e:
+                if os.path.exists(temp_path):
+                    os.unlink(temp_path)
+                raise gr.Error(f"处理视频时出错: {str(e)}")
+        return None
+    except Exception as e:
+        raise gr.Error(f"处理视频时出错: {str(e)}")
+
+
+def extract_audio_and_transcribe(video_path, progress=gr.Progress()):
+    """Extract audio from video and transcribe it using Whisper"""
+    progress(0, desc="正在提取音频...")
+    
+    # Load the video and extract audio
+    video = VideoFileClip(video_path)
+    audio = video.audio
+    
+    # Save audio to temporary file
+    temp_audio = tempfile.mktemp(suffix='.wav')
+    audio.write_audiofile(temp_audio)
+    
+    progress(0.3, desc="正在转录音频...")
+    
+    # Load Whisper model and transcribe
+    model = whisper.load_model("base")
+    result = model.transcribe(temp_audio)
+    print("完成的转录文本结果如下："+result)
+    
+    # Clean up
+    os.remove(temp_audio)
+    video.close()
+    
+    # Process segments with timestamps
+    segments = []
+    for segment in result["segments"]:
+        segments.append({
+            "start": segment["start"],
+            "end": segment["end"],
+            "text": segment["text"].strip()
+        })
+    
+    return segments
+
+def add_text_to_image(image_path, text):
+    """Add text below the image"""
+    # Open image
+    img = Image.open(image_path)
+    width, height = img.size
+    
+    # Create new image with space for text
+    font_size = 30
+    font = ImageFont.truetype("arial.ttf", font_size)
+    text_height = font_size * (text.count('\n') + 2)  # Add padding
+    
+    new_img = Image.new('RGB', (width, height + text_height), 'white')
+    new_img.paste(img, (0, 0))
+    
+    # Add text
+    draw = ImageDraw.Draw(new_img)
+    draw.text((10, height + 10), text, font=font, fill='black')
+    
+    # Save the modified image
+    new_img.save(image_path)
+
+def process_video_with_transcription(video_path, output_folder_screenshot_path, progress=gr.Progress()):
+    """Process video with transcription and add text to images"""
+    # First, get the transcription
+    segments = extract_audio_and_transcribe(video_path, progress)
+    
+    # Then get the frames as before
+    saved_files = detect_unique_screenshots(video_path, output_folder_screenshot_path, progress)
+    
+    progress(0.8, desc="正在添加字幕...")
+    
+    # Match transcription segments with images
+    for i, image_path in enumerate(saved_files):
+        # Extract timestamp from filename (format: 000_1.23.png)
+        timestamp = float(os.path.basename(image_path).split('_')[1].split('.png')[0])
+        
+        # Find relevant text segments for this timestamp
+        relevant_text = []
+        for segment in segments:
+            if segment["start"] <= timestamp * 60 <= segment["end"]:
+                relevant_text.append(segment["text"])
+        
+        # Add text to image
+        if relevant_text:
+            text = "\n".join(relevant_text)
+            add_text_to_image(image_path, text)
+    
+    progress(0.9, desc="处理完成...")
+    return saved_files
+
+def run_app_with_transcription(video_path, progress=gr.Progress()):
+    try:
+        if not video_path:
+            raise gr.Error("请选择要处理的视频文件")
+            
+        progress(0, desc="开始处理...")
+        output_folder_screenshot_path = initialize_output_folder(video_path)
+        saved_files = process_video_with_transcription(video_path, output_folder_screenshot_path, progress)
+        return slides_to_pdf(video_path, output_folder_screenshot_path, saved_files, progress)
+    except Exception as e:
+        raise gr.Error(f"处理失败: {str(e)}")
+
+def process_video_file_with_transcription(video_file):
+    """Handle uploaded video file and return PDF with transcription"""
+    try:
+        # If video_file is a string (path), use it directly
+        if isinstance(video_file, str):
+            if video_file.strip() == "":
+                return None
+            return run_app_with_transcription(video_file)
+            
+        # If it's an uploaded file, create a temporary file
+        if video_file is not None:
+            # Generate a unique filename for the temporary video
+            temp_filename = f"temp_video_{int(time.time())}.mp4"
+            temp_path = os.path.join(tempfile.gettempdir(), temp_filename)
+            
+            try:
+                if hasattr(video_file, 'name'):  # If it's already a file path
+                    shutil.copyfile(video_file, temp_path)
+                else:  # If it's file content
+                    with open(temp_path, 'wb') as f:
+                        f.write(video_file)
+                
+                # Process the video
+                output_folder_screenshot_path, saved_files = video_to_slides(temp_path)
+                saved_files = process_video_with_transcription(temp_path, output_folder_screenshot_path)
+                pdf_path = slides_to_pdf(temp_path, output_folder_screenshot_path, saved_files)
+                
+                # Cleanup
+                if os.path.exists(temp_path):
+                    os.unlink(temp_path)
+                return pdf_path
+                
+            except Exception as e:
+                if os.path.exists(temp_path):
+                    os.unlink(temp_path)
+                raise gr.Error(f"处理视频时出错: {str(e)}")
+        return None
+    except Exception as e:
+        raise gr.Error(f"处理视频时出错: {str(e)}")
+
+
+def process_video(video, path):
+    if video:
+        return run_app(video)
+    elif path:
+        return run_app(path)
+    else:
+        raise gr.Error("请上传视频或输入视频路径")
+
+def handle_video_with_transcription(video, path):
+    if video:
+        return run_app_with_transcription(video)
+    elif path:
+        return run_app_with_transcription(path)
+    else:
+        raise gr.Error("请上传视频或输入视频路径")
+
+# Create a modern interface with custom CSS
+css = """
+.gradio-container {
+    font-family: 'SF Pro Display', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
+}
+.container {
+    max-width: 900px;
+    margin: auto;
+    padding: 20px;
+}
+.gr-button {
+    background: linear-gradient(90deg, #2563eb, #3b82f6);
+    border: none;
+    color: white;
+}
+.gr-button:hover {
+    background: linear-gradient(90deg, #1d4ed8, #2563eb);
+    transform: translateY(-1px);
+    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
+}
+.status-info {
+    margin-top: 10px;
+    padding: 10px;
+    border-radius: 4px;
+    background-color: #f3f4f6;
+}
+"""
+
+if __name__ == "__main__":
+    with gr.Blocks(css=css) as iface:
+        gr.Markdown("# 视频转PDF工具")
+        
+        with gr.Tab("基础转换"):
+            with gr.Row():
+                with gr.Column():
+                    video_input = gr.Video(label="上传视频")
+                    video_path = gr.Textbox(label="或输入视频路径", placeholder="例如: ./input/video.mp4")
+                    convert_btn = gr.Button("开始转换", variant="primary")
+                
+            with gr.Row():
+                output_file = gr.File(label="下载PDF")
+        
+        with gr.Tab("带语音转文字"):
+            with gr.Row():
+                with gr.Column():
+                    video_input_with_transcription = gr.Video(label="上传视频")
+                    video_path_with_transcription = gr.Textbox(label="或输入视频路径", placeholder="例如: ./input/video.mp4")
+                    convert_btn_with_transcription = gr.Button("开始转换（带字幕）", variant="primary")
+                
+            with gr.Row():
+                output_file_with_transcription = gr.File(label="下载PDF（带字幕）")
+        
+        convert_btn.click(
+            fn=process_video,
+            inputs=[video_input, video_path],
+            outputs=[output_file],
+        )
+        
+        convert_btn_with_transcription.click(
+            fn=handle_video_with_transcription,
+            inputs=[video_input_with_transcription, video_path_with_transcription],
+            outputs=[output_file_with_transcription],
+        )
+        
+    iface.launch()