add

README.md

@@ -1,19 +1,14 @@
----
-title: Background Remove
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: remove objects background in high resolution
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+---
+title: Backgraound Remove
+emoji: 🏆
+colorFrom: gray
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.44.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: remove_background
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,69 @@
+import sys
+import types
+import warnings
+
+# Suppress specific timm warning
+warnings.filterwarnings("ignore", category=FutureWarning, module="timm.models.layers")
+
+# Monkey-patch torch.classes path issue
+if not hasattr(sys.modules.get("torch"), "__path__"):
+    torch_classes = types.SimpleNamespace(_path=[])
+    sys.modules["torch.classes"] = torch_classes
+
+import streamlit as st
+import cv2
+import numpy as np
+from io import BytesIO
+from PIL import Image
+from transparent_background import Remover
+
+def process_image(image):
+    try:
+        remover = Remover()
+        img = Image.open(image).convert("RGB")
+
+        with st.spinner("Processing image..."):
+            out = remover.process(img, type="rgba")
+
+        return out  # `out` is already a PIL Image
+
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
+        return None
+
+def main():
+    st.title("Image Upload and Processing App")
+
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png", "tif", "tiff"])
+
+    if uploaded_file is not None:
+        try:
+            image = Image.open(uploaded_file)
+
+            # Convert unsupported image modes
+            if image.mode == 'I;16':
+                image = image.point(lambda i: i * (1.0 / 256)).convert('RGB')
+
+            st.image(image, caption="Uploaded Image", use_container_width=True)
+
+            processed_pil = process_image(uploaded_file)
+
+            if processed_pil:
+                st.image(processed_pil, caption="Processed Image", use_container_width=True)
+
+                buf = BytesIO()
+                processed_pil.save(buf, format="PNG")
+                byte_im = buf.getvalue()
+
+                st.download_button(
+                    label="Download Processed Image",
+                    data=byte_im,
+                    file_name="processed_image.png",
+                    mime="image/png"
+                )
+
+        except Exception as e:
+            st.error(f"Error loading image: {e}")
+
+if __name__ == "__main__":
+    main()

bg_rm_with_fastapi.py

@@ -0,0 +1,193 @@
+from fastapi import FastAPI, File, UploadFile, Form, HTTPException, Request
+from fastapi.responses import JSONResponse, StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+import requests
+from PIL import Image
+import io
+import warnings
+from transparent_background import Remover
+import ssl
+import torch
+import re
+import json
+import numpy as np
+from torch.quantization import quantize_dynamic
+from transformers import CLIPProcessor, CLIPModel
+from langchain_ollama import OllamaLLM
+
+# Disable SSL verification and warnings
+ssl._create_default_https_context = ssl._create_unverified_context
+warnings.filterwarnings('ignore', category=UserWarning)
+warnings.filterwarnings('ignore', category=FutureWarning)
+
+llm = OllamaLLM(model="llama2",base_url  = "http://localhost:11434",system="you are an jewellery expert",temperature=0.0)  
+model=CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+
+quantized_model = quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8) #8-bit quantized model
+
+j_type=["necklace", "finger ring","single earring","earrings","necklace without chain","bangels","pendant without chain"]
+p_gem=["diamond center stone", "ruby center stone", "emerald center stone", "sapphire center stone", "amethyst center stone", "pearl center stone", "topaz center stone", "opal center stone", "garnet center stone", "aquamarine center stone"]
+s_gem=["surrounded by small diamond","surounded by nothing or no secondary stone"]
+design=[ "modern design", "classic design", "minimalist design", "flower design","round shaped", "oval shaped", "square shaped", "cushion shaped", "pear shaped"]
+size=["small size", "medium size", "large size"]
+metal=["gold", "silver"]
+# occasion=["wedding occasion", "casual occasion", "formal occasion", "party occasion", "gifting ", "travel"]
+# t_audience=["women", "men", "teen", "fashionista", "casual"]
+t_audience=["women", "men"]
+visual_desc=["dazzling", "radiant", "glittering", "shimmering", "captivating", "bold", "playful", "charming"]
+
+t=[j_type,p_gem,s_gem,design,size,metal,t_audience,visual_desc]
+
+app = FastAPI()
+def generating_prompt(image):
+  lst1=[]
+  image=image
+   #add the path of image to generate description
+  for items in t:
+    inputs = processor(text=items, images=image, return_tensors="pt", padding=True)
+    # with torch.cuda.amp.autocast():
+    outputs = quantized_model(**inputs)
+    # print(outputs)
+    logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
+    probs = logits_per_image.softmax(dim=1).detach().numpy()
+    probs=np.array(probs)
+  # print(probs)
+    indes=np.argmax(probs)
+    lst1.append(items[indes])
+  res = llm.invoke(f"generate the description(2 to 4 lines) and title(3 to 5 words) of a object from the given features :{str(lst1)}")
+  text = res
+  substring = "Title:"
+  desc="Description:"
+  match0 = re.search(substring, text)
+  match1 = re.search(desc,text)
+  if match0 and match1:
+    title=text[match0.start():match1.start()]
+    description = text[match1.start():]
+    X = title.split(":")
+    y = description.split(":")
+    di = {X[0]:X[1],y[0]:y[1]}
+    json_object = json.dumps(di)
+    return json_object
+  else:
+    return f"The substring '{substring}' is not found."
+# Enable CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Change this to specific domains if needed
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/")
+def home():
+    return {"status": "running", "message": "Background removal service is operational", "version": "1.0"}
+
+@app.post("/remove-background")
+async def remove_background(
+    request: Request,
+    image: UploadFile = File(None), 
+    imageUrl: str = Form(None), 
+    backgroundColor: str = Form(None)):
+    try:
+        input_image = None
+        
+        # Handle JSON request
+        if request.headers.get("content-type") == "application/json":
+            data = await request.json()
+            imageUrl = data.get("imageUrl")
+            backgroundColor = data.get("backgroundColor")
+        
+        if image:
+            # Handle direct image upload
+            input_image = Image.open(io.BytesIO(await image.read()))
+        elif imageUrl:
+            # Handle image URL
+            response = requests.get(imageUrl)
+            if response.status_code != 200:
+                raise HTTPException(status_code=400, detail="Failed to fetch image from URL")
+            input_image = Image.open(io.BytesIO(response.content))
+        else:
+            raise HTTPException(status_code=400, detail="No image or image URL provided")
+        
+        # Initialize remover
+        remover = Remover()
+        
+        # Convert input_image to RGB mode
+        input_image = input_image.convert('RGB')
+        
+        # Remove background using new method
+        output_image = remover.process(input_image, type='rgba'
+                                       
+                                       )
+        
+        # If background color is specified, apply it
+        if backgroundColor:
+            # Convert hex to RGB
+            bg_color = tuple(int(backgroundColor.lstrip('#')[i:i+2], 16) for i in (0, 2, 4))
+            
+            # Create new image with background color
+            background = Image.new('RGBA', output_image.size, bg_color + (255,))
+            # Use alpha channel as mask
+            background.paste(output_image, (0, 0), output_image)
+            output_image = background
+
+        # Save to buffer
+        output_buffer = io.BytesIO()
+        output_image.save(output_buffer, format='PNG')
+        output_buffer.seek(0)
+        
+        return StreamingResponse(output_buffer, media_type="image/png", headers={"Content-Disposition": "attachment; filename=removed_bg.png"})
+    
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+
+@app.post("/description_gen")
+async def description_gen(
+    request: Request,
+    image: UploadFile = File(None), 
+    imageUrl: str = Form(None) ):
+    try:
+        input_image = None
+        
+        # Handle JSON request
+        if request.headers.get("content-type") == "application/json":
+            data = await request.json()
+            imageUrl = data.get("imageUrl")
+        
+        if image:
+            # Handle direct image upload
+            input_image = Image.open(io.BytesIO(await image.read()))
+        elif imageUrl:
+            # Handle image URL
+            response = requests.get(imageUrl)
+            if response.status_code != 200:
+                raise HTTPException(status_code=400, detail="Failed to fetch image from URL")
+            input_image = Image.open(io.BytesIO(response.content))
+        else:
+            raise HTTPException(status_code=400, detail="No image or image URL provided")
+        
+    
+        
+        # Convert input_image to RGB mode
+        input_image = input_image.convert('RGB')
+        output = generating_prompt(input_image)
+
+        return StreamingResponse(output, media_type="text/json", headers={"Content-Disposition": "attachment; filename=discription.json"})
+    
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+        
+
+
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="127.0.0.1", port=8000)