Update run.py

run.py

@@ -1,135 +1,90 @@
-# app.py
 import gradio as gr
-from utils import VideoProcessor, AzureAPI, GoogleAPI, AnthropicAPI, OpenAIAPI
-from constraint import SYS_PROMPT, USER_PROMPT
-from datasets import load_dataset
-import tempfile
-import requests
-
-def load_hf_dataset(dataset_path, auth_token):
-    dataset = load_dataset(dataset_path, token=auth_token)
-    
-    video_paths = dataset
-    print("load done")
-    
-    return video_paths
-
-def fast_caption(sys_prompt, usr_prompt, temp, top_p, max_tokens, model, key, endpoint, video_src, video_hf, video_hf_auth, video_od, video_od_auth, video_gd, video_gd_auth, frame_format, frame_limit):
-    print("begin caption")
-    if video_src:
-        video = video_src
-        processor = VideoProcessor(frame_format=frame_format, frame_limit=frame_limit)
-        frames = processor._decode(video)
-        
-        base64_list = processor.to_base64_list(frames)
-        debug_image = processor.concatenate(frames)
-    
-        if not key or not endpoint:
-            return "", f"API key or endpoint is missing. Processed {len(frames)} frames.", debug_image
-        
-        api = AzureAPI(key=key, endpoint=endpoint, model=model, temp=temp, top_p=top_p, max_tokens=max_tokens)
-        caption = api.get_caption(sys_prompt, usr_prompt, base64_list)
-        return f"{caption}", f"Using model '{model}' with {len(frames)} frames extracted.", debug_image
-    elif video_hf and video_hf_auth:
-        print("begin video_hf")
-        # Handle Hugging Face dataset
-        video_paths = load_hf_dataset(video_hf, video_hf_auth)
-        video_paths = video_paths["train"]
-        # Process all videos in the dataset
-        all_captions = []
-        for video_path_url in video_paths:
-            print("video_path")
-            video_path_url = video_path_url["id"]
-            # 使用requests下载文件到临时文件
-            response = requests.get(video_path_url, stream=True)
-            if response.status_code == 200:
-                with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video_file:
-                    temp_video_file.write(response.content)
-                    video_path = temp_video_file.name
-            else:
-                raise Exception(f"Failed to download video, status code: {response.status_code}")
-                
-            if video_path.endswith('.mp4'):  # 假设我们只处理.mp4文件
-                processor = VideoProcessor(frame_format=frame_format, frame_limit=frame_limit)
-                frames = processor._decode(video_path)
-                base64_list = processor.to_base64_list(frames)
-                api = AzureAPI(key=key, endpoint=endpoint, model=model, temp=temp, top_p=top_p, max_tokens=max_tokens)
-                caption = api.get_caption(sys_prompt, usr_prompt, base64_list)
-                all_captions.append(caption)
-        return "\n\n\n".join(all_captions), f"Processed {len(video_paths)} videos.", None
-    # ... (Handle other sources)
-    else:
-        return "", "No video source selected.", None
-
-with gr.Blocks() as Core:
+from huggingface.user import HFUser, GR_CONF
+
+Theme = gr.Theme.load(GR_CONF["theme"])
+GR_CONF["theme"] = Theme
+
+def login(token):
+    u = HFUser.from_token(token)
+    return u, u.name, gr.Column(visible=False)
+
+def show_time(u, name):
+    return u.ping(name), gr.Column(visible=True)
+
+def list_dataset(u, repo):
+    files = u.list_dataset(repo)
+    return gr.Dropdown(value=files[0], choices=files), gr.Column(visible=True), gr.Column(visible=False)
+
+def fetch_parquet(u, fname):
+    _cache = u.fetch_file(fname)
+    return _cache
+
+def split_parquet(u, file, batch_size):
+    batch_size = int(batch_size)
+    file_slice = u.split_parquet(file, batch_size)
+    return file_slice[0][0], file_slice, gr.Slider(value=0, maximum=batch_size-1), gr.Column(visible=True)
+
+def select_video(chunks, epoch_idx , batch_idx):
+    epoch_idx = int(epoch_idx)
+    batch_idx = int(batch_idx)
+    return chunks[epoch_idx][batch_idx]
+
+def show_lables():
+    return gr.Column(visible=True)
+
+def next_chunks(video_chunks, epoch_idx):
+    length = len(video_chunks)
+    return (epoch_idx+1)%length, gr.Slider(value=0)
+
+with gr.Blocks(**GR_CONF) as Core:
+    user = gr.State()
+    epoch_idx = gr.State(0)
+    video_chunks = gr.State()
+
     with gr.Row(variant="panel"):
         with gr.Column(scale=6):
-            with gr.Accordion("Debug", open=False):
-                info = gr.Textbox(label="Info", interactive=False)
-                frame = gr.Image(label="Frame", interactive=False)
-            with gr.Accordion("Configuration", open=False):
-                with gr.Row():
-                    temp = gr.Slider(0, 1, 0.3, step=0.1, label="Temperature")
-                    top_p = gr.Slider(0, 1, 0.75, step=0.1, label="Top-P")
-                    max_tokens = gr.Slider(512, 4096, 1024, step=1, label="Max Tokens")
+            _video = gr.Video(height=720)
+        with gr.Column(scale=2):
+            with gr.Column() as Auth:
+                _token = gr.Textbox(label="Huggingface Token")
+                _auth = gr.Button("Auth", variant="primary", size="lg")
+
+            with gr.Row() as UUID:
+                name= gr.Textbox(label="Name", interactive=False, scale=1)
+                time= gr.Textbox(label="Time", interactive=False, scale=1)
+
+            with gr.Column(visible=False) as Repo:
+                raw_dataset= gr.Textbox("OpenVideo/pexels-raw", label="Raw Dataset")
+                _list = gr.Button("List", variant='secondary', size='sm')
+
+            with gr.Column(visible=False) as Batch:
+                file = gr.Dropdown(label="Parquet")
                 with gr.Row():
-                    frame_format = gr.Dropdown(label="Frame Format", value="JPEG", choices=["JPEG", "PNG"], interactive=False)
-                    frame_limit = gr.Slider(1, 100, 10, step=1, label="Frame Limits")
-            with gr.Tabs():
-                with gr.Tab("User"):
-                    usr_prompt = gr.Textbox(USER_PROMPT, label="User Prompt", lines=10, max_lines=100, show_copy_button=True)
-                with gr.Tab("System"):
-                    sys_prompt = gr.Textbox(SYS_PROMPT, label="System Prompt", lines=10, max_lines=100, show_copy_button=True)
-            with gr.Tabs():
-                with gr.Tab("Azure"):
-                    result = gr.Textbox(label="Result", lines=15, max_lines=100, show_copy_button=True, interactive=False)
-                with gr.Tab("Google"):
-                    result_gg = gr.Textbox(label="Result", lines=15, max_lines=100, show_copy_button=True, interactive=False)
-                with gr.Tab("Anthropic"):
-                    result_ac = gr.Textbox(label="Result", lines=15, max_lines=100, show_copy_button=True, interactive=False)
-                with gr.Tab("OpenAI"):
-                    result_oai = gr.Textbox(label="Result", lines=15, max_lines=100, show_copy_button=True, interactive=False)
+                    _cache= gr.Textbox("Downloading", label="Cache")
+                    batch_size= gr.Textbox("8", label="Batch")
+                _fetch = gr.Button("Fetch", variant='primary', size='sm')
 
-        with gr.Column(scale=2):
-            with gr.Column():
-                with gr.Accordion("Model Provider", open=True):
-                    with gr.Tabs():
-                        with gr.Tab("Azure"):
-                            model = gr.Dropdown(label="Model", value="GPT-4o", choices=["GPT-4o", "GPT-4v"], interactive=False)
-                            key = gr.Textbox(label="Azure API Key")
-                            endpoint = gr.Textbox(label="Azure Endpoint")
-                        with gr.Tab("Google"):
-                            model_gg = gr.Dropdown(label="Model", value="Gemini-1.5-Flash", choices=["Gemini-1.5-Flash", "Gemini-1.5-Pro"], interactive=False)
-                            key_gg = gr.Textbox(label="Gemini API Key")
-                            endpoint_gg = gr.Textbox(label="Gemini API Endpoint")
-                        with gr.Tab("Anthropic"):
-                            model_ac = gr.Dropdown(label="Model", value="Claude-3-Opus", choices=["Claude-3-Opus", "Claude-3-Sonnet"], interactive=False)
-                            key_ac = gr.Textbox(label="Anthropic API Key")
-                            endpoint_ac = gr.Textbox(label="Anthropic Endpoint")
-                        with gr.Tab("OpenAI"):
-                            model_oai = gr.Dropdown(label="Model", value="GPT-4o", choices=["GPT-4o", "GPT-4v"], interactive=False)
-                            key_oai = gr.Textbox(label="OpenAI API Key")
-                            endpoint_oai = gr.Textbox(label="OpenAI Endpoint")
-                with gr.Accordion("Data Source", open=True):
-                    with gr.Tabs():
-                        with gr.Tab("Upload"):
-                            video_src = gr.Video(sources="upload", show_label=False, show_share_button=False, mirror_webcam=False)
-                        with gr.Tab("HF"):
-                            video_hf = gr.Text(label="Huggingface File Path")
-                            video_hf_auth = gr.Text(label="Huggingface Token")
-                        with gr.Tab("Onedrive"):
-                            video_od = gr.Text("Microsoft Onedrive")
-                            video_od_auth = gr.Text(label="Microsoft Onedrive Token")
-                        with gr.Tab("Google Drive"):
-                            video_gd = gr.Text()
-                            video_gd_auth = gr.Text(label="Google Drive Access Token")
-                caption_button = gr.Button("Caption", variant="primary", size="lg")
-        caption_button.click(
-            fast_caption, 
-            inputs=[sys_prompt, usr_prompt, temp, top_p, max_tokens, model, key, endpoint, video_src, video_hf, video_hf_auth, video_od, video_od_auth, video_gd, video_gd_auth, frame_format, frame_limit], 
-            outputs=[result, info, frame]
-        )
+            with gr.Column(visible=False) as Pick:
+                _pick = gr.Slider(0, 7, value=0, step=1, label="Batch", info="Choose between 1 and $BATCH")
+                gr.Label()
 
-if __name__ == "__main__":
-    Core.launch()
+    with gr.Row(variant="panel", visible=False) as Tag:
+        _human_tag = gr.Textbox(label="Tag", scale=2)
+        with gr.Column():
+            submit = gr.Button("Submit", variant="primary", size="sm", scale=1)
+            with gr.Row():
+                rst = gr.Button("Reset", variant="stop", size="sm", scale=1)
+                nxt = gr.Button("Next Batch", variant="secondary", size="sm", scale=1)
 
+    _auth.click(fn=login, inputs=_token, outputs=[user, name,  Auth])
+    name.change(fn=show_time, inputs=[user, name], outputs=[time, Repo])
+    _list.click(fn=list_dataset, inputs=[user, raw_dataset], outputs=[file, Batch,  Repo])
+    _fetch.click(fn=fetch_parquet, inputs=[user, file], outputs=[_cache] )
+    file.change(fn=fetch_parquet, inputs=[user, file], outputs=[_cache] )
+    _cache.change(fn=split_parquet, inputs=[user, _cache, batch_size], outputs=[_video, video_chunks, _pick, Pick])
+    _pick.change(fn=select_video, inputs=[video_chunks, epoch_idx, _pick], outputs=_video)
+    _video.change(fn=show_lables, outputs=Tag)
+    nxt.click(fn=next_chunks, inputs=[video_chunks, epoch_idx], outputs=[epoch_idx, _pick])
+
+if __name__ == "__main__":
+    Core.launch()