Clean up project: remove unnecessary files and fix insightface installation

- Fixed insightface installation: install from git directly for better compatibility
- Removed test files: images, videos, database files
- Removed duplicate/unused files: app.py, app2.py, app3.py, del.py, run.py, api/main.py
- Removed config files: apt.txt, gitattributes, mypy.ini, requirements2.txt
- Added gfpgan dependency for face enhancement feature
- Simplified Dockerfile with better error handling

Dockerfile

@@ -66,7 +66,8 @@ RUN pip install --no-cache-dir \
     tqdm==4.65.0 \
     Pillow \
     imageio-ffmpeg \
-    huggingface_hub>=0.23.0
+    huggingface_hub>=0.23.0 \
+    gfpgan
 
 # Install OpenCV first (needed by insightface)
 RUN pip install --no-cache-dir opencv-python-headless
@@ -87,13 +88,15 @@ RUN python -c "import onnxruntime; print('ONNX Runtime version:', onnxruntime.__
 # Install moviepy after other dependencies
 RUN pip install --no-cache-dir moviepy==1.0.3
 
-# Install insightface last - try multiple approaches with better error handling
-RUN pip install --no-cache-dir --no-build-isolation insightface==0.7.3 || \
-    pip install --no-cache-dir "insightface>=0.7.0,<0.8.0" || \
+# Install insightface - install from git directly for better compatibility
+RUN pip install --no-cache-dir --no-build-isolation git+https://github.com/deepinsight/insightface.git || \
     pip install --no-cache-dir insightface || \
-    pip install --no-cache-dir --no-build-isolation git+https://github.com/deepinsight/insightface.git@v0.7.3 || \
-    pip install --no-cache-dir git+https://github.com/deepinsight/insightface.git || \
-    (echo "ERROR: All insightface installation methods failed. Check build logs above for details." && exit 1)
+    pip install --no-cache-dir "insightface>=0.7.0" || \
+    (echo "ERROR: Failed to install insightface" && exit 1)
+
+# Verify insightface installation
+RUN python -c "import insightface; print('InsightFace installed successfully')" || \
+    (echo "ERROR: InsightFace verification failed" && exit 1)
 
 # Copy only essential application files
 COPY --chown=user:user api_server.py /app/

api/main.py

@@ -1,374 +0,0 @@
-from fastapi import FastAPI, File, UploadFile, HTTPException, BackgroundTasks
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-from typing import Optional, List
-import os
-import uuid
-import asyncio
-from datetime import datetime
-import motor.motor_asyncio
-from bson import ObjectId
-import json
-import shutil
-from pathlib import Path
-from fastapi.responses import FileResponse, StreamingResponse, JSONResponse
-
-# Import face swap functionality
-import sys
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from app import _run_local_faceswap
-
-app = FastAPI(title="Face Swap Video API", version="1.0.0")
-
-# CORS middleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# MongoDB connection
-MONGODB_URL = os.getenv("MONGODB_URL", "mongodb+srv://itishalogicgo_db_user:HR837xi0B9yh2vZK@cluster0.jeeytpz.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0")
-DATABASE_NAME = "face_swap_video"
-client = motor.motor_asyncio.AsyncIOMotorClient(MONGODB_URL)
-db = client[DATABASE_NAME]
-
-# Collections
-source_images_collection = db["source_images"]
-target_videos_collection = db["target_videos"]
-result_videos_collection = db["result_videos"]
-jobs_collection = db["processing_jobs"]
-
-# Upload directories
-UPLOAD_DIR = Path("uploads")
-SOURCE_IMAGES_DIR = UPLOAD_DIR / "source_images"
-TARGET_VIDEOS_DIR = UPLOAD_DIR / "target_videos"
-RESULT_VIDEOS_DIR = UPLOAD_DIR / "result_videos"
-
-# Create directories
-for dir_path in [UPLOAD_DIR, SOURCE_IMAGES_DIR, TARGET_VIDEOS_DIR, RESULT_VIDEOS_DIR]:
-    dir_path.mkdir(parents=True, exist_ok=True)
-
-# Pydantic models
-class SourceImageResponse(BaseModel):
-    id: str
-    filename: str
-    file_path: str
-    uploaded_at: datetime
-    status: str
-
-class TargetVideoResponse(BaseModel):
-    id: str
-    filename: str
-    file_path: str
-    uploaded_at: datetime
-    status: str
-
-class ResultVideoResponse(BaseModel):
-    id: str
-    source_image_id: str
-    target_video_id: str
-    result_file_path: str
-    created_at: datetime
-    status: str
-    processing_time: Optional[float] = None
-
-class FaceSwapRequest(BaseModel):
-    source_image_id: str
-    target_video_id: str
-
-class JobStatus(BaseModel):
-    job_id: str
-    status: str
-    progress: Optional[float] = None
-    result_video_id: Optional[str] = None
-    result_video_url: Optional[str] = None  # HTTPS download URL
-    error: Optional[str] = None
-
-# Base URL for generating download links
-BASE_URL = os.getenv("BASE_URL", "https://logicgoinfotechspaces-face-swap-video.hf.space")
-
-def get_result_video_url(result_video_id: str) -> str:
-    """Generate HTTPS download URL for result video"""
-    return f"{BASE_URL}/api/result-video/{result_video_id}"
-
-# Helper functions
-def save_file_to_disk(file: UploadFile, directory: Path) -> str:
-    """Save uploaded file to disk and return the file path"""
-    file_extension = Path(file.filename).suffix
-    unique_filename = f"{uuid.uuid4().hex}{file_extension}"
-    file_path = directory / unique_filename
-    
-    with open(file_path, "wb") as buffer:
-        shutil.copyfileobj(file.file, buffer)
-    
-    return str(file_path)
-
-async def process_face_swap(job_id: str, source_image_path: str, target_video_path: str):
-    """Background task to process face swap"""
-    try:
-        # Update job status to processing
-        await jobs_collection.update_one(
-            {"job_id": job_id},
-            {"$set": {"status": "processing", "progress": 0.0}}
-        )
-        
-        # Run face swap
-        result_path = _run_local_faceswap(source_image_path, target_video_path)
-        
-        if result_path and os.path.exists(result_path):
-            # Save result to MongoDB
-            result_doc = {
-                "source_image_path": source_image_path,
-                "target_video_path": target_video_path,
-                "result_file_path": result_path,
-                "created_at": datetime.utcnow(),
-                "status": "completed",
-                "job_id": job_id
-            }
-            
-            result = await result_videos_collection.insert_one(result_doc)
-            result_video_id = str(result.inserted_id)
-            
-            # Update job status to completed
-            await jobs_collection.update_one(
-                {"job_id": job_id},
-                {"$set": {
-                    "status": "completed", 
-                    "progress": 100.0,
-                    "result_video_id": result_video_id,
-                    "result_video_url": get_result_video_url(result_video_id)
-                }}
-            )
-        else:
-            # Update job status to failed
-            await jobs_collection.update_one(
-                {"job_id": job_id},
-                {"$set": {
-                    "status": "failed", 
-                    "error": "Face swap processing failed"
-                }}
-            )
-            
-    except Exception as e:
-        # Update job status to failed
-        await jobs_collection.update_one(
-            {"job_id": job_id},
-            {"$set": {
-                "status": "failed", 
-                "error": str(e)
-            }}
-        )
-
-# API Endpoints
-
-@app.post("/api/source-image", response_model=SourceImageResponse)
-async def upload_source_image(file: UploadFile = File(...)):
-    """Upload and store source image in MongoDB"""
-    if not file.content_type.startswith('image/'):
-        raise HTTPException(status_code=400, detail="File must be an image")
-    
-    try:
-        # Save file to disk
-        file_path = save_file_to_disk(file, SOURCE_IMAGES_DIR)
-        
-        # Store metadata in MongoDB
-        doc = {
-            "filename": file.filename,
-            "file_path": file_path,
-            "uploaded_at": datetime.utcnow(),
-            "status": "uploaded",
-            "content_type": file.content_type,
-            "file_size": os.path.getsize(file_path)
-        }
-        
-        result = await source_images_collection.insert_one(doc)
-        
-        return SourceImageResponse(
-            id=str(result.inserted_id),
-            filename=file.filename,
-            file_path=file_path,
-            uploaded_at=doc["uploaded_at"],
-            status=doc["status"]
-        )
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error uploading source image: {str(e)}")
-
-@app.post("/api/target-video", response_model=TargetVideoResponse)
-async def upload_target_video(file: UploadFile = File(...)):
-    """Upload and store target video in MongoDB"""
-    if not file.content_type.startswith('video/'):
-        raise HTTPException(status_code=400, detail="File must be a video")
-    
-    try:
-        # Save file to disk
-        file_path = save_file_to_disk(file, TARGET_VIDEOS_DIR)
-        
-        # Store metadata in MongoDB
-        doc = {
-            "filename": file.filename,
-            "file_path": file_path,
-            "uploaded_at": datetime.utcnow(),
-            "status": "uploaded",
-            "content_type": file.content_type,
-            "file_size": os.path.getsize(file_path)
-        }
-        
-        result = await target_videos_collection.insert_one(doc)
-        
-        return TargetVideoResponse(
-            id=str(result.inserted_id),
-            filename=file.filename,
-            file_path=file_path,
-            uploaded_at=doc["uploaded_at"],
-            status=doc["status"]
-        )
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error uploading target video: {str(e)}")
-
-@app.post("/api/face-swap", response_model=JobStatus)
-async def start_face_swap(request: FaceSwapRequest, background_tasks: BackgroundTasks):
-    """Start face swap processing"""
-    try:
-        # Get source image and target video from MongoDB
-        source_image = await source_images_collection.find_one({"_id": ObjectId(request.source_image_id)})
-        target_video = await target_videos_collection.find_one({"_id": ObjectId(request.target_video_id)})
-        
-        if not source_image:
-            raise HTTPException(status_code=404, detail="Source image not found")
-        if not target_video:
-            raise HTTPException(status_code=404, detail="Target video not found")
-        
-        # Create job record
-        job_id = str(uuid.uuid4())
-        job_doc = {
-            "job_id": job_id,
-            "source_image_id": request.source_image_id,
-            "target_video_id": request.target_video_id,
-            "status": "queued",
-            "created_at": datetime.utcnow(),
-            "progress": 0.0
-        }
-        
-        await jobs_collection.insert_one(job_doc)
-        
-        # Start background processing
-        background_tasks.add_task(
-            process_face_swap,
-            job_id,
-            source_image["file_path"],
-            target_video["file_path"]
-        )
-        
-        return JobStatus(
-            job_id=job_id,
-            status="queued",
-            progress=0.0
-        )
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error starting face swap: {str(e)}")
-
-@app.get("/api/job/{job_id}", response_model=JobStatus)
-async def get_job_status(job_id: str):
-    """Get job status"""
-    job = await jobs_collection.find_one({"job_id": job_id})
-    if not job:
-        raise HTTPException(status_code=404, detail="Job not found")
-    
-    result_video_url = None
-    if job.get("result_video_id"):
-        result_video_url = get_result_video_url(job["result_video_id"])
-    
-    return JobStatus(
-        job_id=job["job_id"],
-        status=job["status"],
-        progress=job.get("progress"),
-        result_video_id=job.get("result_video_id"),
-        result_video_url=result_video_url,
-        error=job.get("error")
-    )
-
-@app.get("/api/result-video/{result_video_id}")
-async def get_result_video(result_video_id: str):
-    """Get result video file"""
-    result = await result_videos_collection.find_one({"_id": ObjectId(result_video_id)})
-    if not result:
-        raise HTTPException(status_code=404, detail="Result video not found")
-    
-    if not os.path.exists(result["result_file_path"]):
-        raise HTTPException(status_code=404, detail="Result video file not found")
-    
-    return FileResponse(
-        path=result["result_file_path"],
-        media_type="video/mp4",
-        filename=f"face_swap_result_{result_video_id}.mp4"
-    )
-
-@app.get("/api/source-images", response_model=List[SourceImageResponse])
-async def list_source_images():
-    """List all source images"""
-    cursor = source_images_collection.find().sort("uploaded_at", -1)
-    images = []
-    async for doc in cursor:
-        images.append(SourceImageResponse(
-            id=str(doc["_id"]),
-            filename=doc["filename"],
-            file_path=doc["file_path"],
-            uploaded_at=doc["uploaded_at"],
-            status=doc["status"]
-        ))
-    return images
-
-@app.get("/api/target-videos", response_model=List[TargetVideoResponse])
-async def list_target_videos():
-    """List all target videos"""
-    cursor = target_videos_collection.find().sort("uploaded_at", -1)
-    videos = []
-    async for doc in cursor:
-        videos.append(TargetVideoResponse(
-            id=str(doc["_id"]),
-            filename=doc["filename"],
-            file_path=doc["file_path"],
-            uploaded_at=doc["uploaded_at"],
-            status=doc["status"]
-        ))
-    return videos
-
-@app.get("/api/result-videos", response_model=List[ResultVideoResponse])
-async def list_result_videos():
-    """List all result videos"""
-    cursor = result_videos_collection.find().sort("created_at", -1)
-    results = []
-    async for doc in cursor:
-        results.append(ResultVideoResponse(
-            id=str(doc["_id"]),
-            source_image_id=doc.get("source_image_path", ""),
-            target_video_id=doc.get("target_video_path", ""),
-            result_file_path=doc["result_file_path"],
-            created_at=doc["created_at"],
-            status=doc["status"],
-            processing_time=doc.get("processing_time")
-        ))
-    return results
-
-@app.get("/api/health")
-async def api_health():
-    return {"status": "ok", "time": datetime.utcnow().isoformat()}
-
-# -------- Optional: GridFS variants (disabled - using file system storage) -------- #
-# GridFS endpoints removed - using file system storage instead
-# If needed, can be re-enabled with motor's GridFSBucketAsync for async operations
-
-@app.get("/")
-async def root():
-    """Health check endpoint"""
-    return {"message": "Face Swap Video API is running", "version": "1.0.0"}
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)

app.py

@@ -1,267 +0,0 @@
-import gradio as gr
-import os, uuid, time
-from gradio_client import Client, handle_file
-from moviepy.editor import VideoFileClip
-from typing import Optional
-import traceback
-import os as _os
-
-# Ensure sane OMP threads on startup (prevents libgomp warning in Spaces)
-try:
-	_os.environ["OMP_NUM_THREADS"] = "1"
-except Exception:
-	pass
-
-# Local pipeline imports
-import DeepFakeAI.globals as DF_G
-from DeepFakeAI import utilities as DF_U
-from DeepFakeAI.processors.frame.modules import face_swapper as DF_FS
-from huggingface_hub import hf_hub_download
-
-# ── config ───────────────────────────────────────────────────────────
-hf_token      = os.environ.get("TOKEN")
-backend_space = os.environ.get("BACKEND_SPACE", "tonyassi/vfs2-cpu")
-output_dir    = "uploads/output"
-os.makedirs(output_dir, exist_ok=True)
-
-# Initialize client to upstream backend Space; handle unauthorized/404 gracefully
-client = None
-client_init_error = None
-try:
-    client = Client(backend_space, hf_token=hf_token, download_files=output_dir)
-except Exception as e:
-    client_init_error = str(e)
-    client = None
-
-UTM = "utm_source=hugging_face_space&utm_medium=banner&utm_campaign=pro_cta"
-PRO_URL = f"https://www.face-swap.co/?{UTM}"
-
-# ── helpers ──────────────────────────────────────────────────────────
-def preprocess_video(path: str, target_fps: int = 12,
-                     target_size: int = 800, target_len: int = 4) -> str:
-    clip = VideoFileClip(path)
-    if clip.duration > target_len:
-        clip = clip.subclip(0, target_len)
-    w, h = clip.size
-    clip = clip.resize(width=target_size) if w >= h else clip.resize(height=target_size)
-    clip = clip.set_fps(target_fps)
-    out_path = os.path.join(output_dir, f"pre_{uuid.uuid4().hex}.mp4")
-    clip.write_videofile(out_path, codec="libx264", audio_codec="aac",
-                         fps=target_fps, verbose=False, logger=None)
-    clip.close()
-    return out_path
-
-def _run_local_faceswap(source_image_path: str, target_video_path: str) -> Optional[str]:
-    # Configure defaults for local pipeline
-    DF_G.source_path = source_image_path
-    DF_G.target_path = target_video_path
-    DF_G.output_video_encoder = 'libx264'
-    DF_G.output_video_quality = 20
-    DF_G.temp_frame_format = 'png'
-    DF_G.temp_frame_quality = 95
-    DF_G.keep_temp = False
-    DF_G.skip_audio = False
-    # Face processing options
-    DF_G.face_recognition = ['many']
-    DF_G.reference_frame_number = 0
-    DF_G.execution_thread_count = 2
-    DF_G.execution_queue_count = 2
-    # Prefer CUDA (T4 GPU in HF Spaces); will fall back internally if unavailable
-    DF_G.execution_providers = DF_U.decode_execution_providers(['cuda'])
-    # Fix invalid OMP thread settings in container
-    try:
-        _os.environ["OMP_NUM_THREADS"] = "1"
-    except Exception:
-        pass
-
-    # Ensure model exists (try bundled path; if missing, download via hf_hub)
-    model_dir = DF_U.resolve_relative_path('../.assets/models')
-    os.makedirs(model_dir, exist_ok=True)
-    model_path = os.path.join(model_dir, 'inswapper_128.onnx')
-    if not os.path.exists(model_path):
-        token = os.environ.get('TOKEN') or os.environ.get('HF_TOKEN')
-        # Prefer HF mirrors (avoid GitHub 404 in Spaces). Try most reliable first.
-        for repo_id in [
-            'zihaomu/inswapper_128.onnx',
-            'linyi/inswapper_128.onnx',
-            'banodoco/inswapper_128.onnx',
-        ]:
-            try:
-                model_path = hf_hub_download(repo_id=repo_id, filename='inswapper_128.onnx', token=token)
-                break
-            except Exception:
-                continue
-    # Expose resolved model path for the frame processor
-    if os.path.exists(model_path):
-        os.environ['INSWAPPER_PATH'] = model_path
-    DF_FS.pre_check()
-
-    # Extract frames
-    fps = DF_U.detect_fps(target_video_path) or 12.0
-    DF_U.create_temp(target_video_path)
-    ok = DF_U.extract_frames(target_video_path, fps)
-    if not ok:
-        return None
-    temp_frames = DF_U.get_temp_frame_paths(target_video_path)
-    if not temp_frames:
-        return None
-
-    # Process frames
-    DF_FS.process_video(source_image_path, temp_frames)
-
-    # Rebuild video and restore audio
-    if not DF_U.create_video(target_video_path, fps):
-        return None
-    out_path = DF_U.normalize_output_path(source_image_path, target_video_path, os.path.join(output_dir, f"out_{uuid.uuid4().hex}.mp4"))
-    DF_U.restore_audio(target_video_path, out_path)
-    DF_U.clear_temp(target_video_path)
-    return out_path
-
-
-# ── main generate ────────────────────────────────────────────────────
-def generate(input_image, input_video):
-    # Minimal mode: no gender selector; if remote client is missing, we fall back to local
-
-    try:
-        gr.Info("Processing started. This may take 1-3 minutes on first run.")
-        pre_video = preprocess_video(input_video)
-
-        if client is not None:
-            job = client.submit(
-                input_image=handle_file(input_image),
-                input_video={"video": handle_file(pre_video)},
-                device='cpu',
-                selector='many',
-                gender=None,
-                race=None,
-                order=None,
-                api_name="/predict"
-            )
-            while not job.done():
-                time.sleep(5)
-            if not job.status().success:
-                return None
-            outp = job.outputs()[0]["video"]
-            gr.Info("Done.")
-            return outp
-        else:
-            # Local fallback
-            outp = _run_local_faceswap(input_image, pre_video)
-            if outp:
-                gr.Info("Done.")
-            else:
-                gr.Error("Local processing failed. Check logs for details.")
-            return outp
-
-    except Exception as e:
-        gr.Error(f"Generation failed: {e}")
-        print("Generation error:\n", traceback.format_exc())
-        return None
-
-def open_side():  # tiny helper
-    return gr.Sidebar(open=True)
-
-# ── UI (Blocks) ──────────────────────────────────────────────────────
-CUSTOM_CSS = """
-.sticky-cta {
-  position: sticky; top: 0; z-index: 1000;
-  background: #a5b4fc;
-  color: #0f172a;
-  padding: 10px 14px;
-  text-align: center;
-  border-bottom: 1px solid #333;
-  display: block;                 /* full-width clickable */
-  text-decoration: none;          /* remove underline */
-  cursor: pointer;
-}
-.sticky-cta:hover { filter: brightness(0.97); }
-.sticky-cta .pill { background:#4f46e5; color:#fff; padding:4px 10px; border-radius:999px; margin-left:10px; }
-.sticky-cta .cta-link { font-weight:600; text-decoration: underline; }
-
-
-/* centered, single-label API CTA */
-.api-cta-wrap { text-align:center; margin-top:10px; }
-.api-cta-hero {
-  display:inline-flex; align-items:center; gap:10px;
-  padding:10px 14px; border-radius:14px;
-  background: linear-gradient(90deg,#0ea5e9 0%, #a8a9de 100%);
-  color:#fff; font-weight:800; letter-spacing:0.1px;
-  box-shadow: 0 6px 22px rgba(99,102,241,0.35);
-  border: 1px solid rgba(255,255,255,0.22);
-  text-decoration:none;
-}
-.api-cta-hero:hover { filter:brightness(1.05); transform: translateY(-1px); transition: all .15s ease; }
-
-.api-cta-hero .new {
-  background:#fff; color:#0ea5e9; font-weight:900;
-  padding:2px 8px; border-radius:999px; font-size:12px; line-height:1;
-}
-.api-cta-hero .txt { font-weight:800; }
-.api-cta-hero .chev { opacity:.95; }
-@media (max-width: 520px){
-  .api-cta-hero { padding:9px 12px; gap:8px; font-size:14px; }
-  .api-cta-hero .new { display:none; } /* keep it tidy on phones */
-}
-
-
-/* floating bottom promo */
-.bottom-promo {
-  position: fixed; left: 50%; transform: translateX(-50%);
-  bottom: 16px; z-index: 1001; background:#0b0b0b; color:#fff;
-  border: 1px solid #2a2a2a; border-radius: 12px; padding: 10px 14px;
-  box-shadow: 0 8px 24px rgba(0,0,0,0.3);
-}
-.bottom-promo a { color:#4ea1ff; text-decoration:none; font-weight:600; }
-
-/* big CTA button */
-.upgrade-btn { width: 100%; font-size: 16px; padding: 10px 14px; }
-
-/* hero markdown centering + larger heading */
-#hero-md {
-  text-align: center;
-}
-#hero-md h3, /* standard markdown h3 */
-#hero-md .prose h3 { /* some gradio themes wrap markdown with .prose */
-  font-size: 2.1rem;   /* ~34px */
-  line-height: 1.2;
-  font-weight: 800;
-  margin-bottom: 0.25rem;
-}
-#hero-md p,
-#hero-md .prose p {
-  font-size: 1.05rem;  /* slightly larger body text */
-}
-
-"""
-
-with gr.Blocks(title="Video Face Swap", theme=gr.themes.Soft()) as demo:
-    with gr.Row():
-        with gr.Column(scale=5):
-            in_img  = gr.Image(type="filepath", label="Source Image")
-            in_vid  = gr.Video(label="Target Video")
-            go = gr.Button("Generate", variant="primary")
-        with gr.Column(scale=5):
-            out_vid = gr.Video(label="Result")
-
-    # Wire events
-    go.click(fn=generate, inputs=[in_img, in_vid], outputs=out_vid)
-
-    # Queue for long jobs
-    demo.queue()
-
-if __name__ == "__main__":
-    # Mount FastAPI routes into Gradio's FastAPI app
-    try:
-        from api.main import app as api_app
-        
-        # Mount API routes directly to Gradio's FastAPI app
-        # demo.app is the FastAPI instance used by Gradio
-        demo.app.include_router(api_app.router)
-        print("✅ FastAPI routes mounted to Gradio app")
-    except Exception as e:
-        print(f"API integration error: {e}")
-        import traceback
-        traceback.print_exc()
-    
-    # Launch Gradio app (FastAPI routes are now accessible on same port 7860)
-    demo.launch(server_port=7860, share=False)

app2.py

@@ -1,4 +0,0 @@
-import os
-
-
-exec(os.environ.get('CODE'))

app3.py

@@ -1,114 +0,0 @@
-import gradio as gr
-import os, uuid, time
-from gradio_client import Client, handle_file
-from moviepy.editor import VideoFileClip   # NEW
-
-hf_token   = os.environ.get("TOKEN")
-output_dir = "uploads/output"
-os.makedirs(output_dir, exist_ok=True)
-
-client = Client("tonyassi/vfs2-cpu", hf_token=hf_token, download_files=output_dir)
-
-# ── helper ───────────────────────────────────────────────────────────
-def preprocess_video(path: str, target_fps: int = 12,
-                     target_size: int = 800, target_len: int = 4) -> str:
-    """
-    Returns a path to a temp-file that is
-    ≤ target_len seconds, target_fps FPS,
-    and resized so the longest side == target_size.
-    """
-    clip = VideoFileClip(path)
-
-    # 1) trim
-    if clip.duration > target_len:
-        clip = clip.subclip(0, target_len)
-
-    # 2) resize (longest side)
-    w, h = clip.size
-    if w >= h:
-        clip = clip.resize(width=target_size)
-    else:
-        clip = clip.resize(height=target_size)
-
-    # 3) FPS
-    clip = clip.set_fps(target_fps)
-
-    # 4) write to temp file
-    out_path = os.path.join(
-        output_dir, f"pre_{uuid.uuid4().hex}.mp4"
-    )
-    clip.write_videofile(
-        out_path,
-        codec="libx264",
-        audio_codec="aac",
-        fps=target_fps,
-        verbose=False,
-        logger=None
-    )
-    clip.close()
-    return out_path
-
-# ── main generate ────────────────────────────────────────────────────
-def generate(input_image, input_video, gender):
-
-    gr.Warning('Skip the line, generate 1-5 minute HD face swap videos at <a href="https://www.face-swap.co/?utm_source=hugging_face_popup" target="_blank" style="color:#007bff; text-decoration:none;">face-swap.co</a>')
-
-    if (gender=="all"): gender = None
-        
-    try:
-        # ⇢ preprocess video locally
-        pre_video = preprocess_video(input_video)
-
-        # ⇢ submit job to remote Space
-        job = client.submit(
-            input_image=handle_file(input_image),
-            input_video={"video": handle_file(pre_video)},
-            device='cpu',
-            selector='many',
-            gender=gender,
-            race=None,
-            order=None,
-            api_name="/predict"
-        )
-
-        # wait for completion
-        while not job.done():
-            print("Waiting for remote job to finish...")
-            time.sleep(5)
-
-        if not job.status().success:
-            print("Job failed or was cancelled:", job.status())
-            return None
-
-        video_path = job.outputs()[0]["video"]
-        return video_path
-
-    except Exception as e:
-        print("Error occurred:", e)
-        return None
-
-# ── Gradio interface (unchanged) ─────────────────────────────────────
-demo = gr.Interface(
-    fn=generate,
-    title="Video Face Swap",
-    description="""
-    ### [face-swap.co](https://www.face-swap.co/?utm_source=hugging_face_spaces)
-    ***Skip the line, generate 1-5 minute HD videos starting at $3.00***
-
-    ---
-    
-    Swaps the source image face onto the target video. Videos get downsampled to 800 pixels, 4 sec, and 12 fps to cut down render time (~5 minutes). Please ❤️ this Space.
-    """,
-    inputs=[
-        gr.Image(type="filepath", label="Source Image"),
-        gr.Video(label="Target Video"),
-        gr.Radio(choices=["all", "female", "male"], label="Gender", value="all"),
-    ],
-    outputs=gr.Video(label="Result"),
-    flagging_mode="never",
-    examples=[['bella.jpg', 'wizard.mp4', 'all']],
-    cache_examples=True,
-)
-
-if __name__ == "__main__":
-    demo.launch()

apt.txt

@@ -1,3 +0,0 @@
-ffmpeg
-
-

del.py

@@ -1,9 +0,0 @@
-import shutil
-import gradio as gr
-
-def delt(text):
-    txt = text
-    shutil.rmtree("./output")
-    return "Removed successfully..."
-
-gr.Interface(delt, "text","text").launch(debug=True)

gitattributes

@@ -1,38 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-.github/preview.png filter=lfs diff=lfs merge=lfs -text
-1685074910001_vtqikl_2_0-rayul-_M6gy9oHgII-unsplash.jpg filter=lfs diff=lfs merge=lfs -text
-wiz-ex1.mp4 filter=lfs diff=lfs merge=lfs -text

mypy.ini

@@ -1,7 +0,0 @@
-[mypy]
-check_untyped_defs = True
-disallow_any_generics = True
-disallow_untyped_calls = True
-disallow_untyped_defs = True
-ignore_missing_imports = True
-strict_optional = False

requirements2.txt

@@ -1,18 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-pysqlite3
-python-telegram-bot
-gfpgan==1.3.8
-gradio==3.40.1
-insightface==0.7.3
-numpy==1.24.3
-onnxruntime==1.17.3
-opencv-python==4.8.0.74
-opennsfw2==0.10.2
-pillow==10.0.0
-protobuf==4.23.4
-psutil==5.9.5
-realesrgan==0.3.0
-tensorflow==2.13.0
-tqdm==4.65.0
-moviepy
-cloudinary

run.py

@@ -1,6 +0,0 @@
-#!/usr/bin/env python3
-
-from DeepFakeAI import core
-
-if __name__ == '__main__':
-    core.run()