Upload folder using huggingface_hub

src/app.py

@@ -28,95 +28,80 @@ def create_app():
     
     print("🚀 Starting Gradio + FastMCP server")
     
-    # Check if this is HF Spaces environment
-    is_hf_spaces = "SPACE_ID" in os.environ
-    
-    if is_hf_spaces:
-        # For HF Spaces: return pure Gradio app (simpler approach)
-        print("🤗 Creating HF Spaces compatible app")
-        ui_app = create_gradio_interface()
-        
-        print("✅ Server startup completed")
-        print("🎨 Gradio UI: /")
-        
-        return ui_app
-    
-    else:
-        # For other environments: full FastAPI + MCP + Gradio setup
-        # Create FastMCP server with new tools
-        mcp = FastMCP("Podcast MCP")
-        
-        # Register tools using the new service architecture
-        @mcp.tool(description="Transcribe audio files to text using Whisper model with speaker diarization support")
-        async def transcribe_audio_file_tool(
-            audio_file_path: str,
-            model_size: str = "turbo",
-            language: str = None,
-            output_format: str = "srt",
-            enable_speaker_diarization: bool = False
-        ):
-            return await mcp_tools.transcribe_audio_file(
-                audio_file_path, model_size, language, output_format, enable_speaker_diarization
-            )
-        
-        @mcp.tool(description="Download Apple Podcast audio files")
-        async def download_apple_podcast_tool(url: str):
-            return await mcp_tools.download_apple_podcast(url)
-        
-        @mcp.tool(description="Download XiaoYuZhou podcast audio files")
-        async def download_xyz_podcast_tool(url: str):
-            return await mcp_tools.download_xyz_podcast(url)
-        
-        @mcp.tool(description="Scan directory for MP3 audio files")
-        async def get_mp3_files_tool(directory: str):
-            return await mcp_tools.get_mp3_files(directory)
-        
-        @mcp.tool(description="Get basic file information")
-        async def get_file_info_tool(file_path: str):
-            return await mcp_tools.get_file_info(file_path)
-        
-        @mcp.tool(description="Read text file content in segments")
-        async def read_text_file_segments_tool(
-            file_path: str,
-            chunk_size: int = 65536,
-            start_position: int = 0
-        ):
-            return await mcp_tools.read_text_file_segments(file_path, chunk_size, start_position)
-        
-        # Create FastAPI wrapper
-        fastapi_wrapper = FastAPI(
-            title="Modal AudioTranscriber MCP",
-            description="Gradio UI + FastMCP Tool + Modal Integration AudioTranscriber MCP",
-            version="1.0.0",
-            lifespan=lambda app: mcp.session_manager.run()
-        )
-        
-        # Get FastMCP's streamable HTTP app
-        mcp_app = mcp.streamable_http_app()
-        
-        # Mount FastMCP application to /api path  
-        fastapi_wrapper.mount("/api", mcp_app)
-        
-        # Create Gradio interface
-        ui_app = create_gradio_interface()
-        
-        # Use Gradio's standard mounting approach
-        final_app = mount_gradio_app(
-            app=fastapi_wrapper,
-            blocks=ui_app,
-            path="/",
-            app_kwargs={
-                "docs_url": "/docs",
-                "redoc_url": "/redoc",
-            }
+    # Create FastMCP server with new tools
+    mcp = FastMCP("Podcast MCP")
+    
+    # Register tools using the new service architecture
+    @mcp.tool(description="Transcribe audio files to text using Whisper model with speaker diarization support")
+    async def transcribe_audio_file_tool(
+        audio_file_path: str,
+        model_size: str = "turbo",
+        language: str = None,
+        output_format: str = "srt",
+        enable_speaker_diarization: bool = False
+    ):
+        return await mcp_tools.transcribe_audio_file(
+            audio_file_path, model_size, language, output_format, enable_speaker_diarization
         )
-        
-        print("✅ Server startup completed")
-        print("🎨 Gradio UI: /")
-        print("🔧 MCP Streamable HTTP: /api/mcp")
-        print(f"📝 Server name: {mcp.name}")
-        
-        return final_app
+    
+    @mcp.tool(description="Download Apple Podcast audio files")
+    async def download_apple_podcast_tool(url: str):
+        return await mcp_tools.download_apple_podcast(url)
+    
+    @mcp.tool(description="Download XiaoYuZhou podcast audio files")
+    async def download_xyz_podcast_tool(url: str):
+        return await mcp_tools.download_xyz_podcast(url)
+    
+    @mcp.tool(description="Scan directory for MP3 audio files")
+    async def get_mp3_files_tool(directory: str):
+        return await mcp_tools.get_mp3_files(directory)
+    
+    @mcp.tool(description="Get basic file information")
+    async def get_file_info_tool(file_path: str):
+        return await mcp_tools.get_file_info(file_path)
+    
+    @mcp.tool(description="Read text file content in segments")
+    async def read_text_file_segments_tool(
+        file_path: str,
+        chunk_size: int = 65536,
+        start_position: int = 0
+    ):
+        return await mcp_tools.read_text_file_segments(file_path, chunk_size, start_position)
+    
+    # Create FastAPI wrapper
+    fastapi_wrapper = FastAPI(
+        title="Modal AudioTranscriber MCP",
+        description="Gradio UI + FastMCP Tool + Modal Integration AudioTranscriber MCP",
+        version="1.0.0",
+        lifespan=lambda app: mcp.session_manager.run()
+    )
+    
+    # Get FastMCP's streamable HTTP app
+    mcp_app = mcp.streamable_http_app()
+    
+    # Mount FastMCP application to /api path  
+    fastapi_wrapper.mount("/api", mcp_app)
+    
+    # Create Gradio interface
+    ui_app = create_gradio_interface()
+    
+    # Use Gradio's standard mounting approach
+    final_app = mount_gradio_app(
+        app=fastapi_wrapper,
+        blocks=ui_app,
+        path="/",
+        app_kwargs={
+            "docs_url": "/docs",
+            "redoc_url": "/redoc",
+        }
+    )
+    
+    print("✅ Server startup completed")
+    print("🎨 Gradio UI: /")
+    print("🔧 MCP Streamable HTTP: /api/mcp")
+    print(f"📝 Server name: {mcp.name}")
+    
+    return final_app
 
 # ==================== Modal Deployment Configuration ====================
 
@@ -143,56 +128,26 @@ if _modal_available:
 
 # ==================== Main Entry Point ====================
 
-def main():
-    """Main entry point for all deployment modes"""
-    
-    if is_modal_mode():
-        print("☁️ Modal mode: Use 'modal deploy src.app::gradio_mcp_app'")
-        return None
-    else:
-        print("🏠 Starting in local mode")
-        print("💡 GPU functions will be routed to Modal endpoints")
-        
-        app = create_app()
-        return app
-
 def run_local():
-    """Run local server with uvicorn (for direct execution)"""
-    # Double-check: don't run uvicorn in HF Spaces
-    if os.environ.get("HF_SPACES_MODE"):
-        print("⚠️ Skipping uvicorn.run() in HF Spaces mode")
-        return
-        
-    app = main()
-    if app:
-        # Use port 7860 for HF Spaces compatibility, 8000 for local
-        port = int(os.environ.get("PORT", 8000))  # Use 8000 for local dev
-        uvicorn.run(
-            app,
-            host="0.0.0.0",
-            port=port,
-            reload=False
-        )
-
-# ==================== Hugging Face Spaces Support ====================
-
-# For Hugging Face Spaces, directly create the app
-def get_app():
-    """Get app instance for HF Spaces"""
-    if "DEPLOYMENT_MODE" not in os.environ:
-        os.environ["DEPLOYMENT_MODE"] = "local"
-    return main()
-
-# HF Spaces compatibility: only create app when not in main execution
-if __name__ != "__main__":
-    # Check if we're in HF Spaces mode and app is already created
-    if not os.environ.get("HF_SPACES_MODE"):
-        # This will be called when imported by other environments (not HF Spaces)
-        app = get_app()
+    """Run local server with uvicorn"""
+    print("🏠 Starting in local mode")
+    
+    # Set default environment
+    os.environ.setdefault("DEPLOYMENT_MODE", "local")
+    
+    app = create_app()
+    
+    # Use port 7860 for HF Spaces compatibility
+    port = int(os.environ.get("PORT", 7860))
+    
+    print(f"🌐 Starting server on port {port}")
+    
+    uvicorn.run(
+        app,
+        host="0.0.0.0",
+        port=port,
+        reload=False
+    )
 
 if __name__ == "__main__":
-    # Check if we should run uvicorn (not in HF Spaces)
-    if not os.environ.get("HF_SPACES_MODE"):
-        run_local()
-    else:
-        print("⚠️ Skipping uvicorn in HF Spaces mode")
+    run_local()