ci/cd

.github/workflows/sync2hf.yaml

@@ -0,0 +1,25 @@
+name: Sync to Hugging Face hub
+
+on:
+  push:
+    branches: [main]
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Push to Hugging Face hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_SECRET }}
+        run: |
+          git remote remove origin || true
+          git remote add origin https://Ashad001:${{ secrets.HF_SECRET }}@huggingface.co/spaces/Ashad001/roomaligner
+          git add .
+          git commit -m "Sync code to Hugging Face Hub" || true
+          git push --force origin main

DOCKERFILE

@@ -0,0 +1,13 @@
+FROM python:3.12
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,70 @@
+import os
+import base64
+import json
+from io import BytesIO
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from dotenv import load_dotenv
+from plan.agents import GeneratePlan, GenerateSuggestions
+from plan.image_analyzer import ImageAnalyzer
+from raster.raster import InferenceClient
+from utils.preprocess_data import preprocess_data
+from PIL import Image
+
+load_dotenv()
+
+app = FastAPI(title="Interior Designer API")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  
+    allow_credentials=True,
+    allow_methods=["*"],  
+    allow_headers=["*"],  
+)
+
+# required classes
+inference_client = InferenceClient()
+plan_generator = GeneratePlan()
+image_analyzer = ImageAnalyzer()
+suggestions_generator = GenerateSuggestions()
+
+@app.post("/upload-room-image/")
+async def upload_image(file: UploadFile = File(...)):
+    """
+    Upload room image and get natural language description and suggested room layout.
+    """
+    try:
+        contents = await file.read()
+        img = Image.open(BytesIO(contents))
+
+        # Convert image to base64
+        buffered = BytesIO()
+        img.save(buffered, format="JPEG")
+        image_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+        # Step 1: Classify objects in the image
+        result = inference_client.infer_image(image_base64)
+        data = preprocess_data(result)
+
+        # Step 2: Generate a natural language description
+        nl_description = plan_generator.forward(data)
+
+        # Step 3: Analyze room structure and generate suggestions
+        room_structure = image_analyzer.generate_floor_plan_details(image_base64, nl_description)
+        suggested_structure = suggestions_generator.forward(room_structure.content)
+
+        # Return response as JSON
+        return JSONResponse(content={
+            "natural_language_description": nl_description,
+            "room_structure": room_structure.content,
+            "suggested_structure": suggested_structure
+        })
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")
+
+@app.get("/")
+def read_root():
+    return {"message": "Welcome to the Interior Designer API!"}

plan/agents.py

@@ -5,7 +5,6 @@
 from typing import List, Dict, Any
 
 load_dotenv(".env")
-print(os.getenv("OPENAI_API_KEY"))
 lm = dspy.OpenAI(model="gpt-4o-mini", api_key=os.getenv("OPENAI_API_KEY"), max_tokens=4096)
 dspy.settings.configure(lm=lm)
 

requirements.txt

@@ -24,6 +24,7 @@ dill==0.3.8
 diskcache==5.6.3
 distro==1.9.0
 dspy==2.5.3
+fastapi==0.115.2
 filelock==3.16.1
 fonttools==4.54.1
 frozenlist==1.4.1
@@ -87,6 +88,7 @@ six==1.16.0
 smmap==5.0.1
 sniffio==1.3.1
 SQLAlchemy==2.0.35
+starlette==0.40.0
 streamlit==1.39.0
 structlog==24.4.0
 supervision==0.23.0