Switch to minimal FastAPI for testing deployment

app.py

@@ -1,365 +1,32 @@
 """
-FastAPI Backend for Text-to-3D Model Converter
-Deployed on Hugging Face Spaces with direct model loading
+Minimal FastAPI test for debugging HF Spaces deployment
 """
 
-import os
+from fastapi import FastAPI
 import logging
-import time
-import uuid
-import asyncio
-from typing import Optional
-from contextlib import asynccontextmanager
-
-import uvicorn
-from fastapi import FastAPI, File, UploadFile, HTTPException, BackgroundTasks
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel
-
-from models.depth_processor import DepthProcessor
-from models.image_generator import ImageGenerator
-from utils.job_manager import JobManager
-from utils.cloudinary_client import CloudinaryClient
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Global variables for models
-depth_processor = None
-image_generator = None
-job_manager = None
-cloudinary_client = None
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    """Initialize models on startup"""
-    global depth_processor, image_generator, job_manager, cloudinary_client
-    
-    logger.info("🚀 Starting Text-to-3D Backend...")
-    
-    # Initialize utilities
-    job_manager = JobManager()
-    cloudinary_client = CloudinaryClient()
-    
-    # Initialize models
-    logger.info("📦 Loading AI models...")
-    try:
-        # Initialize depth processor
-        depth_processor = DepthProcessor()
-        await asyncio.to_thread(depth_processor.load_model)
-        logger.info("✅ Depth estimation model loaded")
-        
-        # Initialize image generator
-        image_generator = ImageGenerator()
-        await asyncio.to_thread(image_generator.load_model)
-        logger.info("✅ Image generation model loaded")
-        
-        logger.info("🎉 All models loaded successfully!")
-        
-    except Exception as e:
-        logger.error(f"❌ Failed to load models: {str(e)}")
-        raise e
-    
-    yield
-    
-    # Cleanup on shutdown
-    logger.info("🔄 Shutting down...")
-
 # Initialize FastAPI app
-app = FastAPI(
-    title="Text-to-3D Backend",
-    description="Convert text prompts and images to 3D models",
-    version="1.0.0",
-    lifespan=lifespan
-)
-
-# Configure CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=[
-        "http://localhost:3000",  # Local development
-        "https://*.render.com",   # Render deployment
-        "*"  # Allow all for now, restrict in production
-    ],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Request/Response models
-class GenerateRequest(BaseModel):
-    prompt: str
-    user_id: Optional[str] = None
-
-class GenerateResponse(BaseModel):
-    success: bool
-    job_id: str
-    image_url: Optional[str] = None
-    model_url: Optional[str] = None
-    depth_map_url: Optional[str] = None
-    error: Optional[str] = None
-
-class ProgressResponse(BaseModel):
-    stage: str
-    progress: int
-    message: str
-    timestamp: Optional[float] = None
+app = FastAPI(title="Text-to-3D Backend Test", version="1.0.0")
 
 @app.get("/")
 async def root():
-    """Health check endpoint"""
+    """Simple health check"""
+    logger.info("Health check requested")
     return {
-        "status": "Text-to-3D Backend is running! 🚀",
-        "version": "1.0.0",
-        "models_loaded": {
-            "depth_processor": depth_processor is not None,
-            "image_generator": image_generator is not None
-        },
-        "gpu_available": depth_processor.device.type == "cuda" if depth_processor else False
+        "status": "FastAPI is running! 🚀",
+        "message": "Basic setup working",
+        "test": True
     }
 
 @app.get("/health")
-async def health_check():
-    """Detailed health check"""
-    return {
-        "status": "healthy",
-        "models": {
-            "depth_estimation": "loaded" if depth_processor else "not_loaded",
-            "image_generation": "loaded" if image_generator else "not_loaded"
-        },
-        "device": str(depth_processor.device) if depth_processor else "unknown",
-        "active_jobs": job_manager.get_active_job_count() if job_manager else 0
-    }
-
-@app.post("/generate", response_model=GenerateResponse)
-async def generate_from_text(
-    request: GenerateRequest,
-    background_tasks: BackgroundTasks
-):
-    """Generate 3D model from text prompt"""
-    try:
-        if not request.prompt.strip():
-            raise HTTPException(status_code=400, detail="Prompt cannot be empty")
-        
-        # Create job ID
-        job_id = str(uuid.uuid4())
-        job_manager.register_job(job_id)
-        
-        logger.info(f"🎨 Starting text-to-3D generation: '{request.prompt}' (Job: {job_id})")
-        
-        # Start background processing
-        background_tasks.add_task(
-            process_text_to_3d,
-            job_id, 
-            request.prompt, 
-            request.user_id
-        )
-        
-        return GenerateResponse(
-            success=True,
-            job_id=job_id,
-            message="Generation started"
-        )
-        
-    except Exception as e:
-        logger.error(f"❌ Error in generate endpoint: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/upload")
-async def upload_image(
-    file: UploadFile = File(...),
-    background_tasks: BackgroundTasks = None,
-    user_id: Optional[str] = None
-):
-    """Convert uploaded image to 3D model"""
-    try:
-        # Validate file type
-        if not file.content_type.startswith('image/'):
-            raise HTTPException(status_code=400, detail="File must be an image")
-        
-        # Create job ID
-        job_id = str(uuid.uuid4())
-        job_manager.register_job(job_id)
-        
-        logger.info(f"📤 Processing uploaded image: {file.filename} (Job: {job_id})")
-        
-        # Read file content
-        file_content = await file.read()
-        
-        # Start background processing
-        background_tasks.add_task(
-            process_upload_to_3d,
-            job_id,
-            file_content,
-            file.filename,
-            user_id
-        )
-        
-        return {
-            "success": True,
-            "job_id": job_id,
-            "message": "Upload processing started"
-        }
-        
-    except Exception as e:
-        logger.error(f"❌ Error in upload endpoint: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/progress/{job_id}", response_model=ProgressResponse)
-async def get_progress(job_id: str):
-    """Get job progress"""
-    try:
-        progress = job_manager.get_job_progress(job_id)
-        if not progress:
-            raise HTTPException(status_code=404, detail="Job not found")
-        
-        return ProgressResponse(**progress)
-        
-    except Exception as e:
-        logger.error(f"❌ Error getting progress: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/cancel")
-async def cancel_job(job_id: str):
-    """Cancel a running job"""
-    try:
-        success = job_manager.cancel_job(job_id)
-        if success:
-            return {"success": True, "message": f"Job {job_id} cancelled"}
-        else:
-            raise HTTPException(status_code=404, detail="Job not found")
-            
-    except Exception as e:
-        logger.error(f"❌ Error cancelling job: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-async def process_text_to_3d(job_id: str, prompt: str, user_id: Optional[str]):
-    """Background task to process text to 3D"""
-    try:
-        # Update progress
-        job_manager.update_job_progress(job_id, "generating_image", 10, "Generating image from text...")
-        
-        # Generate image from text
-        image_result = await asyncio.to_thread(
-            image_generator.generate_image, 
-            prompt
-        )
-        
-        if job_manager.is_job_cancelled(job_id):
-            return
-        
-        job_manager.update_job_progress(job_id, "uploading_image", 40, "Uploading generated image...")
-        
-        # Upload image to Cloudinary
-        image_url = await asyncio.to_thread(
-            cloudinary_client.upload_image_from_bytes,
-            image_result['image_bytes'],
-            f"generated_{job_id}"
-        )
-        
-        if job_manager.is_job_cancelled(job_id):
-            return
-        
-        job_manager.update_job_progress(job_id, "creating_depth", 60, "Creating depth map...")
-        
-        # Generate depth map and 3D model
-        depth_result = await asyncio.to_thread(
-            depth_processor.process_image_to_3d,
-            image_result['image_pil'],
-            job_id
-        )
-        
-        if job_manager.is_job_cancelled(job_id):
-            return
-        
-        job_manager.update_job_progress(job_id, "uploading_results", 90, "Uploading 3D model...")
-        
-        # Upload results
-        model_url = await asyncio.to_thread(
-            cloudinary_client.upload_file,
-            depth_result['obj_path'],
-            f"model_{job_id}.obj"
-        )
-        
-        depth_map_url = await asyncio.to_thread(
-            cloudinary_client.upload_image_from_path,
-            depth_result['depth_map_path'],
-            f"depth_{job_id}"
-        )
-        
-        # Complete job
-        job_manager.complete_job(job_id, {
-            "image_url": image_url,
-            "model_url": model_url,
-            "depth_map_url": depth_map_url
-        })
-        
-        logger.info(f"✅ Text-to-3D generation completed: {job_id}")
-        
-    except Exception as e:
-        logger.error(f"❌ Error in text-to-3D processing: {str(e)}")
-        job_manager.fail_job(job_id, str(e))
-
-async def process_upload_to_3d(job_id: str, file_content: bytes, filename: str, user_id: Optional[str]):
-    """Background task to process uploaded image to 3D"""
-    try:
-        job_manager.update_job_progress(job_id, "uploading", 20, "Uploading image to cloud...")
-        
-        # Upload original image
-        image_url = await asyncio.to_thread(
-            cloudinary_client.upload_image_from_bytes,
-            file_content,
-            f"upload_{job_id}_{filename}"
-        )
-        
-        if job_manager.is_job_cancelled(job_id):
-            return
-        
-        job_manager.update_job_progress(job_id, "processing", 50, "Processing image to 3D...")
-        
-        # Convert to PIL Image
-        from PIL import Image
-        image_pil = Image.open(io.BytesIO(file_content))
-        
-        # Generate depth map and 3D model
-        depth_result = await asyncio.to_thread(
-            depth_processor.process_image_to_3d,
-            image_pil,
-            job_id
-        )
-        
-        if job_manager.is_job_cancelled(job_id):
-            return
-        
-        job_manager.update_job_progress(job_id, "uploading_results", 90, "Uploading 3D model...")
-        
-        # Upload results
-        model_url = await asyncio.to_thread(
-            cloudinary_client.upload_file,
-            depth_result['obj_path'],
-            f"model_{job_id}.obj"
-        )
-        
-        depth_map_url = await asyncio.to_thread(
-            cloudinary_client.upload_image_from_path,
-            depth_result['depth_map_path'],
-            f"depth_{job_id}"
-        )
-        
-        # Complete job
-        job_manager.complete_job(job_id, {
-            "image_url": image_url,
-            "model_url": model_url,
-            "depth_map_url": depth_map_url
-        })
-        
-        logger.info(f"✅ Upload-to-3D processing completed: {job_id}")
-        
-    except Exception as e:
-        logger.error(f"❌ Error in upload-to-3D processing: {str(e)}")
-        job_manager.fail_job(job_id, str(e))
+async def health():
+    """Health endpoint"""
+    return {"status": "healthy", "service": "text-to-3d-backend"}
 
 if __name__ == "__main__":
+    import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=7860)

app_full.py

@@ -0,0 +1,365 @@
+"""
+FastAPI Backend for Text-to-3D Model Converter
+Deployed on Hugging Face Spaces with direct model loading
+"""
+
+import os
+import logging
+import time
+import uuid
+import asyncio
+from typing import Optional
+from contextlib import asynccontextmanager
+
+import uvicorn
+from fastapi import FastAPI, File, UploadFile, HTTPException, BackgroundTasks
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+
+from models.depth_processor import DepthProcessor
+from models.image_generator import ImageGenerator
+from utils.job_manager import JobManager
+from utils.cloudinary_client import CloudinaryClient
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Global variables for models
+depth_processor = None
+image_generator = None
+job_manager = None
+cloudinary_client = None
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Initialize models on startup"""
+    global depth_processor, image_generator, job_manager, cloudinary_client
+    
+    logger.info("🚀 Starting Text-to-3D Backend...")
+    
+    # Initialize utilities
+    job_manager = JobManager()
+    cloudinary_client = CloudinaryClient()
+    
+    # Initialize models
+    logger.info("📦 Loading AI models...")
+    try:
+        # Initialize depth processor
+        depth_processor = DepthProcessor()
+        await asyncio.to_thread(depth_processor.load_model)
+        logger.info("✅ Depth estimation model loaded")
+        
+        # Initialize image generator
+        image_generator = ImageGenerator()
+        await asyncio.to_thread(image_generator.load_model)
+        logger.info("✅ Image generation model loaded")
+        
+        logger.info("🎉 All models loaded successfully!")
+        
+    except Exception as e:
+        logger.error(f"❌ Failed to load models: {str(e)}")
+        raise e
+    
+    yield
+    
+    # Cleanup on shutdown
+    logger.info("🔄 Shutting down...")
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="Text-to-3D Backend",
+    description="Convert text prompts and images to 3D models",
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+# Configure CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=[
+        "http://localhost:3000",  # Local development
+        "https://*.render.com",   # Render deployment
+        "*"  # Allow all for now, restrict in production
+    ],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Request/Response models
+class GenerateRequest(BaseModel):
+    prompt: str
+    user_id: Optional[str] = None
+
+class GenerateResponse(BaseModel):
+    success: bool
+    job_id: str
+    image_url: Optional[str] = None
+    model_url: Optional[str] = None
+    depth_map_url: Optional[str] = None
+    error: Optional[str] = None
+
+class ProgressResponse(BaseModel):
+    stage: str
+    progress: int
+    message: str
+    timestamp: Optional[float] = None
+
+@app.get("/")
+async def root():
+    """Health check endpoint"""
+    return {
+        "status": "Text-to-3D Backend is running! 🚀",
+        "version": "1.0.0",
+        "models_loaded": {
+            "depth_processor": depth_processor is not None,
+            "image_generator": image_generator is not None
+        },
+        "gpu_available": depth_processor.device.type == "cuda" if depth_processor else False
+    }
+
+@app.get("/health")
+async def health_check():
+    """Detailed health check"""
+    return {
+        "status": "healthy",
+        "models": {
+            "depth_estimation": "loaded" if depth_processor else "not_loaded",
+            "image_generation": "loaded" if image_generator else "not_loaded"
+        },
+        "device": str(depth_processor.device) if depth_processor else "unknown",
+        "active_jobs": job_manager.get_active_job_count() if job_manager else 0
+    }
+
+@app.post("/generate", response_model=GenerateResponse)
+async def generate_from_text(
+    request: GenerateRequest,
+    background_tasks: BackgroundTasks
+):
+    """Generate 3D model from text prompt"""
+    try:
+        if not request.prompt.strip():
+            raise HTTPException(status_code=400, detail="Prompt cannot be empty")
+        
+        # Create job ID
+        job_id = str(uuid.uuid4())
+        job_manager.register_job(job_id)
+        
+        logger.info(f"🎨 Starting text-to-3D generation: '{request.prompt}' (Job: {job_id})")
+        
+        # Start background processing
+        background_tasks.add_task(
+            process_text_to_3d,
+            job_id, 
+            request.prompt, 
+            request.user_id
+        )
+        
+        return GenerateResponse(
+            success=True,
+            job_id=job_id,
+            message="Generation started"
+        )
+        
+    except Exception as e:
+        logger.error(f"❌ Error in generate endpoint: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/upload")
+async def upload_image(
+    file: UploadFile = File(...),
+    background_tasks: BackgroundTasks = None,
+    user_id: Optional[str] = None
+):
+    """Convert uploaded image to 3D model"""
+    try:
+        # Validate file type
+        if not file.content_type.startswith('image/'):
+            raise HTTPException(status_code=400, detail="File must be an image")
+        
+        # Create job ID
+        job_id = str(uuid.uuid4())
+        job_manager.register_job(job_id)
+        
+        logger.info(f"📤 Processing uploaded image: {file.filename} (Job: {job_id})")
+        
+        # Read file content
+        file_content = await file.read()
+        
+        # Start background processing
+        background_tasks.add_task(
+            process_upload_to_3d,
+            job_id,
+            file_content,
+            file.filename,
+            user_id
+        )
+        
+        return {
+            "success": True,
+            "job_id": job_id,
+            "message": "Upload processing started"
+        }
+        
+    except Exception as e:
+        logger.error(f"❌ Error in upload endpoint: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/progress/{job_id}", response_model=ProgressResponse)
+async def get_progress(job_id: str):
+    """Get job progress"""
+    try:
+        progress = job_manager.get_job_progress(job_id)
+        if not progress:
+            raise HTTPException(status_code=404, detail="Job not found")
+        
+        return ProgressResponse(**progress)
+        
+    except Exception as e:
+        logger.error(f"❌ Error getting progress: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/cancel")
+async def cancel_job(job_id: str):
+    """Cancel a running job"""
+    try:
+        success = job_manager.cancel_job(job_id)
+        if success:
+            return {"success": True, "message": f"Job {job_id} cancelled"}
+        else:
+            raise HTTPException(status_code=404, detail="Job not found")
+            
+    except Exception as e:
+        logger.error(f"❌ Error cancelling job: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+async def process_text_to_3d(job_id: str, prompt: str, user_id: Optional[str]):
+    """Background task to process text to 3D"""
+    try:
+        # Update progress
+        job_manager.update_job_progress(job_id, "generating_image", 10, "Generating image from text...")
+        
+        # Generate image from text
+        image_result = await asyncio.to_thread(
+            image_generator.generate_image, 
+            prompt
+        )
+        
+        if job_manager.is_job_cancelled(job_id):
+            return
+        
+        job_manager.update_job_progress(job_id, "uploading_image", 40, "Uploading generated image...")
+        
+        # Upload image to Cloudinary
+        image_url = await asyncio.to_thread(
+            cloudinary_client.upload_image_from_bytes,
+            image_result['image_bytes'],
+            f"generated_{job_id}"
+        )
+        
+        if job_manager.is_job_cancelled(job_id):
+            return
+        
+        job_manager.update_job_progress(job_id, "creating_depth", 60, "Creating depth map...")
+        
+        # Generate depth map and 3D model
+        depth_result = await asyncio.to_thread(
+            depth_processor.process_image_to_3d,
+            image_result['image_pil'],
+            job_id
+        )
+        
+        if job_manager.is_job_cancelled(job_id):
+            return
+        
+        job_manager.update_job_progress(job_id, "uploading_results", 90, "Uploading 3D model...")
+        
+        # Upload results
+        model_url = await asyncio.to_thread(
+            cloudinary_client.upload_file,
+            depth_result['obj_path'],
+            f"model_{job_id}.obj"
+        )
+        
+        depth_map_url = await asyncio.to_thread(
+            cloudinary_client.upload_image_from_path,
+            depth_result['depth_map_path'],
+            f"depth_{job_id}"
+        )
+        
+        # Complete job
+        job_manager.complete_job(job_id, {
+            "image_url": image_url,
+            "model_url": model_url,
+            "depth_map_url": depth_map_url
+        })
+        
+        logger.info(f"✅ Text-to-3D generation completed: {job_id}")
+        
+    except Exception as e:
+        logger.error(f"❌ Error in text-to-3D processing: {str(e)}")
+        job_manager.fail_job(job_id, str(e))
+
+async def process_upload_to_3d(job_id: str, file_content: bytes, filename: str, user_id: Optional[str]):
+    """Background task to process uploaded image to 3D"""
+    try:
+        job_manager.update_job_progress(job_id, "uploading", 20, "Uploading image to cloud...")
+        
+        # Upload original image
+        image_url = await asyncio.to_thread(
+            cloudinary_client.upload_image_from_bytes,
+            file_content,
+            f"upload_{job_id}_{filename}"
+        )
+        
+        if job_manager.is_job_cancelled(job_id):
+            return
+        
+        job_manager.update_job_progress(job_id, "processing", 50, "Processing image to 3D...")
+        
+        # Convert to PIL Image
+        from PIL import Image
+        image_pil = Image.open(io.BytesIO(file_content))
+        
+        # Generate depth map and 3D model
+        depth_result = await asyncio.to_thread(
+            depth_processor.process_image_to_3d,
+            image_pil,
+            job_id
+        )
+        
+        if job_manager.is_job_cancelled(job_id):
+            return
+        
+        job_manager.update_job_progress(job_id, "uploading_results", 90, "Uploading 3D model...")
+        
+        # Upload results
+        model_url = await asyncio.to_thread(
+            cloudinary_client.upload_file,
+            depth_result['obj_path'],
+            f"model_{job_id}.obj"
+        )
+        
+        depth_map_url = await asyncio.to_thread(
+            cloudinary_client.upload_image_from_path,
+            depth_result['depth_map_path'],
+            f"depth_{job_id}"
+        )
+        
+        # Complete job
+        job_manager.complete_job(job_id, {
+            "image_url": image_url,
+            "model_url": model_url,
+            "depth_map_url": depth_map_url
+        })
+        
+        logger.info(f"✅ Upload-to-3D processing completed: {job_id}")
+        
+    except Exception as e:
+        logger.error(f"❌ Error in upload-to-3D processing: {str(e)}")
+        job_manager.fail_job(job_id, str(e))
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)

app_simple.py

@@ -0,0 +1,32 @@
+"""
+Minimal FastAPI test for debugging HF Spaces deployment
+"""
+
+from fastapi import FastAPI
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize FastAPI app
+app = FastAPI(title="Text-to-3D Backend Test", version="1.0.0")
+
+@app.get("/")
+async def root():
+    """Simple health check"""
+    logger.info("Health check requested")
+    return {
+        "status": "FastAPI is running! 🚀",
+        "message": "Basic setup working",
+        "test": True
+    }
+
+@app.get("/health")
+async def health():
+    """Health endpoint"""
+    return {"status": "healthy", "service": "text-to-3d-backend"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -1,19 +1,2 @@
 fastapi==0.104.1
-uvicorn[standard]==0.24.0
-python-multipart==0.0.6
-pydantic==2.5.0
-torch==2.1.1
-torchvision==0.16.1
-torchaudio==2.1.1
-transformers==4.39.3
-diffusers==0.27.0
-accelerate==0.27.0
-Pillow==10.3.0
-numpy==1.24.3
-open3d==0.18.0
-matplotlib==3.7.2
-cloudinary==1.37.0
-python-dotenv==1.0.0
-safetensors==0.4.2
-huggingface_hub==0.20.2
-requests==2.31.0
+uvicorn[standard]==0.24.0

requirements_full.txt

@@ -0,0 +1,19 @@
+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+python-multipart==0.0.6
+pydantic==2.5.0
+torch==2.1.1
+torchvision==0.16.1
+torchaudio==2.1.1
+transformers==4.39.3
+diffusers==0.27.0
+accelerate==0.27.0
+Pillow==10.3.0
+numpy==1.24.3
+open3d==0.18.0
+matplotlib==3.7.2
+cloudinary==1.37.0
+python-dotenv==1.0.0
+safetensors==0.4.2
+huggingface_hub==0.20.2
+requests==2.31.0

requirements_simple.txt

@@ -0,0 +1,2 @@
+fastapi==0.104.1
+uvicorn[standard]==0.24.0