Update app.py

app.py

@@ -1,239 +1,3 @@
-# from fastapi import FastAPI, UploadFile, File, HTTPException
-# import cv2
-# import torch
-# import pandas as pd
-# from PIL import Image
-# from transformers import AutoImageProcessor, AutoModelForImageClassification
-# from tqdm import tqdm
-# import shutil
-# from fastapi.middleware.cors import CORSMiddleware
-# from fastapi.responses import HTMLResponse
-# from huggingface_hub import HfApi
-# import os
-# from dotenv import load_dotenv
-# from typing import Optional
-
-# # Charger les variables d'environnement, y compris la clé API Hugging Face
-# load_dotenv()
-
-# api_key = os.getenv("HUGGINGFACE_API_KEY")
-# if not api_key:
-#     raise ValueError("La clé API Hugging Face n'est pas définie dans le fichier .env.")
-
-# # Initialiser l'API Hugging Face
-# hf_api = HfApi()
-
-# app = FastAPI()
-
-# # Add CORS middleware to allow requests from Vue.js frontend
-# app.add_middleware(
-#     CORSMiddleware,
-#     allow_origins=[
-#         "http://localhost:8080",
-#         "https://labeling2-163849140747.europe-west9.run.app/",
-#         "https://my-vue-app-latest-qqzd.onrender.com/",
-#     ],
-#     allow_credentials=True,
-#     allow_methods=["*"],  # Permet toutes les méthodes HTTP (GET, POST, etc.)
-#     allow_headers=["*"],  # Permet tous les en-têtes (Content-Type, Authorization, etc.)
-# )
-
-# # Charger le processeur d'image et le modèle fine-tuné localement
-# local_model_path = r'./vit-finetuned-ucf101'
-# processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
-# model = AutoModelForImageClassification.from_pretrained(local_model_path)
-# model.eval()
-
-# # Fonction pour classifier une image
-# def classifier_image(image):
-#     image_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-#     inputs = processor(images=image_pil, return_tensors="pt")
-#     with torch.no_grad():
-#         outputs = model(**inputs)
-#         logits = outputs.logits
-#     predicted_class_idx = logits.argmax(-1).item()
-#     predicted_class = model.config.id2label[predicted_class_idx]
-#     return predicted_class
-
-# # Fonction pour traiter la vidéo et identifier les séquences de "Surfing"
-# def identifier_sequences_surfing(video_path, intervalle=0.5):
-#     cap = cv2.VideoCapture(video_path)
-#     frame_rate = cap.get(cv2.CAP_PROP_FPS)
-#     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-#     frame_interval = int(frame_rate * intervalle)
-
-#     sequences_surfing = []
-#     frame_index = 0
-#     in_surf_sequence = False
-#     start_timestamp = None
-
-#     with tqdm(total=total_frames, desc="Traitement des frames de la vidéo", unit="frame") as pbar:
-#         success, frame = cap.read()
-#         while success:
-#             if frame_index % frame_interval == 0:
-#                 timestamp = round(frame_index / frame_rate, 2)
-#                 classe = classifier_image(frame)
-
-#                 if classe == "Surfing" and not in_surf_sequence:
-#                     in_surf_sequence = True
-#                     start_timestamp = timestamp
-#                 elif classe != "Surfing" and in_surf_sequence:
-#                     in_surf_sequence = False
-#                     end_timestamp = timestamp
-#                     sequences_surfing.append((start_timestamp, end_timestamp))
-
-#             success, frame = cap.read()
-#             frame_index += 1
-#             pbar.update(1)
-
-#     if in_surf_sequence:
-#         sequences_surfing.append((start_timestamp, round(frame_index / frame_rate, 2)))
-
-#     cap.release()
-#     dataframe_sequences = pd.DataFrame(sequences_surfing, columns=["Début", "Fin"])
-#     return dataframe_sequences
-
-# # Fonction pour convertir les séquences en format JSON
-# def convertir_sequences_en_json(dataframe):
-#     events = []
-#     blocks = []
-#     for idx, row in dataframe.iterrows():
-#         block = {
-#             "id": f"Surfing{idx + 1}",
-#             "start": round(row["Début"], 2),
-#             "end": round(row["Fin"], 2)
-#         }
-#         blocks.append(block)
-#     event = {
-#         "event": "Surfing",
-#         "blocks": blocks
-#     }
-#     events.append(event)
-#     return events
-
-# # # Endpoint pour analyser la vidéo et uploader sur Hugging Face
-# # @app.post("/analyze_video/")
-# # async def analyze_video(user_name: str, file: UploadFile = File(...)):
-# #     try:
-# #         # Sauvegarder la vidéo temporairement
-# #         temp_file_path = f"/tmp/{file.filename}"
-# #         with open(temp_file_path, "wb") as buffer:
-# #             shutil.copyfileobj(file.file, buffer)
-
-# #         # Uploader la vidéo sur Hugging Face Hub
-# #         dataset_name = "2nzi/Video-Sequence-Labeling"
-# #         target_path_in_repo = f"{user_name}/raw/{file.filename}"
-
-# #         hf_api.upload_file(
-# #             path_or_fileobj=temp_file_path,
-# #             path_in_repo=target_path_in_repo,
-# #             repo_id=dataset_name,
-# #             repo_type="dataset",
-# #             token=api_key
-# #         )
-
-# #         # Analyser la vid��o pour trouver des séquences "Surfing"
-# #         dataframe_sequences = identifier_sequences_surfing(temp_file_path, intervalle=1)
-# #         json_result = convertir_sequences_en_json(dataframe_sequences)
-
-# #         # Supprimer le fichier temporaire après l'upload
-# #         os.remove(temp_file_path)
-
-# #         return {"message": "Video uploaded and analyzed successfully!", 
-# #                 "file_url": f"https://huggingface.co/datasets/{dataset_name}/resolve/main/{target_path_in_repo}", 
-# #                 "analysis": json_result}
-
-# #     except Exception as e:
-# #         raise HTTPException(status_code=500, detail=f"Failed to upload or analyze video: {str(e)}")
-
-
-# @app.post("/analyze_video/")
-# async def analyze_video(user_name: str, file: Optional[UploadFile] = File(None), video_url: Optional[str] = None):
-#     try:
-#         # Vérifier si la vidéo est fournie sous forme de fichier ou d'URL
-#         if file:
-#             # Sauvegarder la vidéo temporairement
-#             temp_file_path = f"/tmp/{file.filename}"
-#             with open(temp_file_path, "wb") as buffer:
-#                 shutil.copyfileobj(file.file, buffer)
-
-#             # Uploader la vidéo sur Hugging Face Hub
-#             dataset_name = "2nzi/Video-Sequence-Labeling"
-#             target_path_in_repo = f"{user_name}/raw/{file.filename}"
-
-#             hf_api.upload_file(
-#                 path_or_fileobj=temp_file_path,
-#                 path_in_repo=target_path_in_repo,
-#                 repo_id=dataset_name,
-#                 repo_type="dataset",
-#                 token=os.getenv("HUGGINGFACE_WRITE_API_KEY")
-#             )
-
-#             # URL de la vidéo sur Hugging Face
-#             video_url = f"https://huggingface.co/datasets/{dataset_name}/resolve/main/{target_path_in_repo}"
-#             # Supprimer le fichier temporaire après l'upload
-#             os.remove(temp_file_path)
-
-#         # Assurez-vous d'avoir une URL valide à ce stade
-#         if not video_url:
-#             raise HTTPException(status_code=400, detail="No valid video URL or file provided.")
-
-#         # Analyser la vidéo via l'URL
-#         dataframe_sequences = identifier_sequences_surfing(video_url, intervalle=1)
-#         json_result = convertir_sequences_en_json(dataframe_sequences)
-
-#         return {
-#             "message": "Video uploaded and analyzed successfully!",
-#             "file_url": video_url,
-#             "analysis": json_result
-#         }
-
-#     except Exception as e:
-#         raise HTTPException(status_code=500, detail=f"Failed to upload or analyze video: {str(e)}")
-
-
-# # Fonction pour uploader une vidéo vers un dataset Hugging Face
-# def upload_to_hf_dataset(user_name: str, video_path: str):
-#     dataset_name = "2nzi/Video-Sequence-Labeling"
-#     repo_path = f"{user_name}/raw/{os.path.basename(video_path)}"
-
-#     try:
-#         hf_api.upload_file(
-#             path_or_fileobj=video_path,
-#             path_in_repo=repo_path,
-#             repo_id=dataset_name,
-#             repo_type="dataset",
-#             token=api_key
-#         )
-        
-#         # Retourner l'URL de la vidéo après l'upload
-#         url = f"https://huggingface.co/datasets/{dataset_name}/resolve/main/{repo_path}"
-#         return {"status": "success", "url": url}
-#     except Exception as e:
-#         return {"status": "error", "message": str(e)}
-
-
-# @app.get("/", response_class=HTMLResponse)
-# async def index():
-#     return (
-#         """
-#         <html>
-#             <body>
-#                 <h1>Hello world!</h1>
-#                 <p>This `/` is the most simple and default endpoint.</p>
-#                 <p>If you want to learn more, check out the documentation of the API at 
-#                 <a href='/docs'>/docs</a> or 
-#                 <a href='https://2nzi-video-sequence-labeling.hf.space/docs' target='_blank'>external docs</a>.
-#                 </p>
-#             </body>
-#         </html>
-#         """
-#     )
-
-# # Lancer l'application avec uvicorn (command line)
-# # uvicorn main:app --reload
-# # http://localhost:8000/docs#/
-# # (.venv) PS C:\Users\antoi\Documents\Work_Learn\Labeling-Deploy\FastAPI> uvicorn main:app --host 0.0.0.0 --port 8000 --workers 1
 
 from fastapi import FastAPI, UploadFile, File, HTTPException
 import cv2