app.py

# 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 vidéo 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
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://labeling-tools.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 vidéo 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)}")


# # Endpoint pour analyser la vidéo via une URL
# @app.post("/analyze_video/")
# async def analyze_video(user_name: str, video_url: Optional[str] = None):
#     try:
#         # Assurez-vous d'avoir une URL valide à ce stade
#         if not video_url:
#             raise HTTPException(status_code=400, detail="No valid video URL 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 analyzed successfully!",
#             "file_url": video_url,
#             "analysis": json_result
#         }

#     except Exception as e:
#         raise HTTPException(status_code=500, detail=f"Failed to analyze video: {str(e)}")




@app.post("/upload_video/")
async def upload_video(user_name: str, file: UploadFile = File(...)):
    try:
        print(f"Received request to upload video for user: {user_name}")

        # Sauvegarder le fichier temporairement
        temp_file_path = f"/tmp/{file.filename}"
        with open(temp_file_path, "wb") as buffer:
            buffer.write(await file.read())

        # Préparer l'upload sur Hugging Face
        dataset_name = "2nzi/Video-Sequence-Labeling"
        repo_path = f"{user_name}/raw/{file.filename}"

        print(f"Uploading {temp_file_path} to Hugging Face at {repo_path}")

        hf_api.upload_file(
            path_or_fileobj=temp_file_path,
            path_in_repo=repo_path,
            repo_id=dataset_name,
            repo_type="dataset",
            token=api_key
        )

        # Générer l'URL finale
        file_url = f"https://huggingface.co/datasets/{dataset_name}/resolve/main/{repo_path}"
        print(f"File successfully uploaded to: {file_url}")

        return {"status": "success", "file_url": file_url}

    except Exception as e:
        print(f"Error during upload: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Upload failed: {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, #chelou
            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