Update app.py

app.py

@@ -1,44 +1,19 @@
 import gradio as gr
-import os
-from scenedetect import open_video, SceneManager
-from scenedetect.detectors import ContentDetector
+import torch
+from transformers import CLIPProcessor, CLIPModel
 
-OUTPUT_DIR = "outputs"
-os.makedirs(OUTPUT_DIR, exist_ok=True)
+# 載入 FunCLIP (假設 FunCLIP 是基於 CLIP 結構衍生)
+model_name = "modelscope/funclip"  # 可替換為真正FunCLIP的模型名稱
+device = "cpu"
 
-def detect_scenes(video_path):
-    if not video_path or not os.path.exists(video_path):
-        return "未找到影片檔案，請確認上傳檔案無誤", None
+processor = CLIPProcessor.from_pretrained(model_name)
+model = CLIPModel.from_pretrained(model_name).to(device)
 
-    try:
-        video = open_video(video_path)
-        manager = SceneManager()
-        manager.add_detector(ContentDetector())
-        manager.detect_scenes(video)
-        scene_list = manager.get_scene_list()
+def funclip_inference(image):
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    outputs = model.get_image_features(**inputs)
+    # 這裡可替換為 funclip 具體後處理
+    return outputs.detach().cpu().numpy()
 
-        # 組合分鏡資料為 CSV 內容
-        rows = [["Scene Number", "Start Time", "End Time"]]
-        for i, (start, end) in enumerate(scene_list):
-            rows.append([str(i + 1), str(start.get_timecode()), str(end.get_timecode())])
-
-        fname, _ = os.path.splitext(os.path.basename(video_path))
-        csv_path = os.path.join(OUTPUT_DIR, fname + "_scenes.csv")
-        with open(csv_path, "w", encoding='utf-8') as f:
-            for row in rows:
-                f.write(",".join(row) + "\n")
-
-        return f"偵測到 {len(scene_list)} 組分鏡，CSV 可下載", csv_path
-    except Exception as e:
-        return f"解析失敗：{e}", None
-
-iface = gr.Interface(
-    fn=detect_scenes,
-    inputs=gr.Video(label="上傳影片"),
-    outputs=[gr.Textbox(label="分鏡結果"), gr.File(label="下載分鏡 CSV")],
-    title="PySceneDetect 分鏡偵測（可儲存 CSV）",
-    description="上傳影片，自動產生分鏡資訊，並可下載 CSV。支援 CPU 部署。"
-)
-
-if __name__ == "__main__":
-    iface.launch()
+iface = gr.Interface(fn=funclip_inference, inputs=gr.Image(type="pil"), outputs=gr.Textbox())
+iface.launch()