Update app.py

app.py

@@ -16,6 +16,19 @@ from kiui.op import recenter
 import kiui
 from gradio_litmodel3d import LitModel3D
 import shutil
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from pydantic import Field
+from typing import Optional
+import logging
+import os
+import boto3
+import uuid
+
+import datetime
+
+app = FastAPI()
 
 
 ACCESS = os.getenv("ACCESS")
@@ -246,67 +259,34 @@ def step_2_generate_video(image):
 def step_3_display_3d_model(mesh_file):
     return mesh_file
 
-# set up the example files from assets folder, we limit to 10
-example_folder = "assets"
-examples = [os.path.join(example_folder, f) for f in os.listdir(example_folder) if f.endswith(('.png', '.jpg', '.jpeg'))][:10]
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        
-        with gr.Column():
-            gr.Markdown("""
-            # Welcome to [VFusion3D](https://junlinhan.github.io/projects/vfusion3d.html) Demo
-
-            This demo allows you to upload an image and generate a 3D model or rendered videos from it. 
-
-            ## How to Use:
-            1. Click on "Click to Upload" to upload an image, or choose one example image.
-            
-            2: Choose between "Generate and Download Mesh" or "Generate and Download Video", then click it.
-            
-            3. Wait for the model to process; meshes should take approximately 10 seconds, and videos will take approximately 30 seconds.
-            
-            4. Download the generated mesh or video.
-
-            This demo does not aim to provide optimal results but rather to provide a quick look. See our [GitHub](https://github.com/facebookresearch/vfusion3d) for more. 
-
-            """)
-            img_input = gr.Image(type="pil", label="Input Image")
-            examples_component = gr.Examples(examples=examples, inputs=img_input, outputs=None, examples_per_page=3)
-            generate_mesh_button = gr.Button("Generate and Download Mesh")
-            generate_video_button = gr.Button("Generate and Download Video")
-            obj_file_output = gr.File(label="Download .obj File")
-            video_file_output = gr.File(label="Download Video")
-
-        with gr.Column():
-            model_output = LitModel3D(
-                clear_color=[0.1, 0.1, 0.1, 0],  # can adjust background color for better contrast
-                label="3D Model Visualization",
-                scale=1.0,
-                tonemapping="aces",  # can use aces tonemapping for more realistic lighting
-                exposure=1.0,        # can adjust exposure to control brightness
-                contrast=1.1,        # can slightly increase contrast for better depth
-                camera_position=(0, 0, 2),  # will set initial camera position to center the model
-                zoom_speed=0.5,      # will adjust zoom speed for better control
-                pan_speed=0.5,       # will adjust pan speed for better control
-                interactive=True     # this allow users to interact with the model
-            )
-            
-        
-    # clear outputs
-    def clear_model_viewer():
-        """Reset the Model3D component before loading a new model."""
-        return gr.update(value=None)
+def upload_file_to_s3(file_path, bucket_name, object_name):
+    try:
+        s3_client.upload_file(file_path, bucket_name, object_name)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to upload file to S3: {str(e)}")
     
-    def generate_and_visualize(image):
-        mesh_path = step_1_generate_obj(image)
-        return mesh_path, mesh_path
 
-    # first we clear the existing 3D model
-    img_input.change(clear_model_viewer, inputs=None, outputs=model_output)
+@app.post("/upload/")
+async def upload_image(file: UploadFile = File(...)):
+    try:
+        # Convert uploaded file to PIL Image
+        image = Image.open(io.BytesIO(await file.read()))
+
+        # Generate OBJ file
+        obj_file_path = step_1_generate_obj(image)
+
+        # Generate a unique name for the S3 object
+        obj_name = f"{uuid.uuid4()}.obj"
+
+        # Upload the OBJ file to S3
+        s3_url = save_file_to_s3(obj_file_path, bucket_name, obj_name)
+
+        return JSONResponse(content={"Output": s3_url})
 
-    # then, generate the mesh and video
-    generate_mesh_button.click(step_1_generate_obj, inputs=img_input, outputs=[obj_file_output, model_output])
-    generate_video_button.click(step_2_generate_video, inputs=img_input, outputs=video_file_output)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error processing the image: {str(e)}")
 
-demo.launch()
+# Run FastAPI
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)