added process image files and functions

main.py

@@ -2,11 +2,13 @@ from fastapi import FastAPI
 from routers.auth import router as auth_router
 from routers.analysis import router as analysis_router
 from routers.history import router as history_router
+from routers.product import router as product_router
 
 app = FastAPI()
 
 app.include_router(analysis_router, prefix="/api/analyze")
 app.include_router(auth_router, prefix="/api/auth")
+app.include_router(product_router, prefix="/api/product")
 app.include_router(history_router, prefix="/api/history")
 
 # To run the FastAPI app, use the command: uvicorn main:app --reload

requirements.txt

@@ -140,3 +140,4 @@ xxhash==3.5.0
 yarl==1.19.0
 zope.interface==7.2
 zstandard==0.23.0
+pillow

routers/analysis.py

@@ -1,7 +1,8 @@
 import asyncio
 import os
 from datetime import datetime
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, UploadFile, File
+from fastapi.responses import JSONResponse
 import pytz
 from sqlalchemy.orm import Session
 from typing import List, Dict, Any
@@ -14,6 +15,10 @@ from db.database import get_db,SessionLocal
 from db.repositories import IngredientRepository
 from dotenv import load_dotenv
 from langsmith import traceable
+from PIL import Image
+import io
+import base64
+from fastapi.encoders import jsonable_encoder
 
 from services.ingredientFinderAgent import IngredientInfoAgentLangGraph
 from services.productAnalyzerAgent import analyze_product_ingredients
@@ -42,6 +47,26 @@ def ingredient_db_to_pydantic(db_ingredient):
         details_with_source=[source.data for source in db_ingredient.sources]
     )
 
+@router.post("/process_image")
+async def process_image(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents))
+
+        # Convert the image to JPEG format in memory
+        img_byte_arr = io.BytesIO()
+        image.save(img_byte_arr, format="JPEG")
+        img_byte_arr = img_byte_arr.getvalue()
+        # TODO add ML model to process the image and get the crop coordinates
+        # For testing frontend keeping same image and returning it
+
+        # Encode the JPEG image to base64
+        encoded_string = base64.b64encode(img_byte_arr).decode("utf-8")
+
+        return JSONResponse({"cropped_image": encoded_string})
+
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=500)
 
 # process single ingredient 
 @router.post("/process_ingredient", response_model=IngredientAnalysisResult)

routers/product.py

@@ -0,0 +1,50 @@
+import io
+from fastapi import APIRouter, File, Request, UploadFile
+from fastapi.responses import JSONResponse
+from typing import List, Dict
+from logger_manager import log_info, log_error
+from fastapi.encoders import jsonable_encoder
+from PIL import Image
+import os
+import uuid
+
+UPLOADED_IMAGES_DIR = "uploaded_images"
+if not os.path.exists(UPLOADED_IMAGES_DIR):
+    os.makedirs(UPLOADED_IMAGES_DIR)
+    
+router = APIRouter()
+
+@router.post("/add")
+async def create_product(request: Request, image: UploadFile = File(...)):
+    log_info("Create product endpoint called")
+    try:
+        data = await request.json()
+        print("Received data:", data)
+        
+        # dummy code below save image target to Vuforia and other details with id from vuforia
+        # TODO: Implement Vuforia integration to save image target and retrieve ID
+        # For now, we will just print the data and save the image locally
+        # Extract product details from the request body
+
+        name = data.get("name")
+        ingredients: List[str] = data.get("ingredients")
+
+        print("Product Name:", name)
+        print("Ingredients:", ingredients)
+
+        # Save the uploaded image
+        image_filename = f"{uuid.uuid4()}.jpg"  # Generate a unique filename
+        image_path = os.path.join(UPLOADED_IMAGES_DIR, image_filename)
+        
+
+        contents = await image.read()
+        img = Image.open(io.BytesIO(contents))
+        img.save(image_path, "JPEG")
+
+        print("Image saved to:", image_path)
+
+        return JSONResponse({"message": "Product data and image received and processed successfully"})
+
+    except Exception as e:
+        print("Error:", e)
+        return JSONResponse({"error": str(e)}, status_code=500)