Delete diffsketcher_handler.py with huggingface_hub

diffsketcher_handler.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import os
-import sys
-import torch
-import numpy as np
-from PIL import Image
-import io
-import base64
-from handler_template import BaseHandler
-
-# Add DiffSketcher to path
-sys.path.append("/app/model")
-
-class Handler(BaseHandler):
-    def initialize(self):
-        """Load the DiffSketcher model"""
-        try:
-            from models.clip_text_encoder import CLIPTextEncoder
-            from models.sketch_generator import SketchGenerator
-            
-            # Load text encoder
-            self.text_encoder = CLIPTextEncoder()
-            self.text_encoder.to(self.device)
-            self.text_encoder.eval()
-            
-            # Load sketch generator
-            self.model = SketchGenerator()
-            weights_path = os.path.join("/app/model/weights", "diffsketcher_model.pth")
-            if os.path.exists(weights_path):
-                state_dict = torch.load(weights_path, map_location=self.device)
-                self.model.load_state_dict(state_dict)
-            else:
-                raise FileNotFoundError(f"Model weights not found at {weights_path}")
-                
-            self.model.to(self.device)
-            self.model.eval()
-            
-            self.initialized = True
-            print("DiffSketcher model initialized successfully")
-        except Exception as e:
-            print(f"Error initializing DiffSketcher model: {str(e)}")
-            raise
-    
-    def preprocess(self, data):
-        """Process the input data"""
-        try:
-            # Extract prompt from the request
-            prompt = data.get("prompt", "")
-            if not prompt:
-                raise ValueError("No prompt provided in the request")
-                
-            # Encode text with CLIP
-            with torch.no_grad():
-                text_embedding = self.text_encoder.encode_text(prompt)
-                
-            return {
-                "text_embedding": text_embedding,
-                "prompt": prompt
-            }
-        except Exception as e:
-            print(f"Error in preprocessing: {str(e)}")
-            raise
-    
-    def inference(self, inputs):
-        """Generate SVG from text embedding"""
-        try:
-            text_embedding = inputs["text_embedding"]
-            
-            # Run inference
-            with torch.no_grad():
-                svg_data = self.model.generate(text_embedding)
-                
-            return svg_data
-        except Exception as e:
-            print(f"Error during inference: {str(e)}")
-            raise
-    
-    def postprocess(self, inference_output):
-        """Format the model output"""
-        try:
-            svg_content = inference_output["svg_content"]
-            
-            # Return both the SVG content and base64 encoded version
-            return {
-                "svg_content": svg_content,
-                "svg_base64": self.svg_to_base64(svg_content)
-            }
-        except Exception as e:
-            print(f"Error in postprocessing: {str(e)}")
-            return {"error": str(e)}