added training and new endpoint

matchCollection.py

@@ -0,0 +1,32 @@
+import json
+import numpy as np
+
+def load_palettes(filepath):
+    with open(filepath, 'r') as file:
+        return json.load(file)
+
+def hex_to_rgb(hex_color):
+    hex_color = hex_color.lstrip('#')
+    return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
+
+def rgb_distance(color1, color2):
+    return np.sqrt(sum((a - b) ** 2 for a, b in zip(color1, color2)))
+
+def predict_palette(collection_name, hex_color, filepath='output.json'):
+    palettes_data = load_palettes(filepath)
+    
+    target_rgb = hex_to_rgb(hex_color)
+    
+    min_distance = float('inf')
+    best_palette = None
+    
+    for collection in palettes_data['collections']:
+        if collection['collection'] == collection_name:
+            for palette in collection['colors']:
+                for color in palette:
+                    distance = rgb_distance(target_rgb, hex_to_rgb(color))
+                    if distance < min_distance:
+                        min_distance = distance
+                        best_palette = palette
+                
+    return best_palette

server.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, File, UploadFile, Form
+from fastapi import FastAPI, File, UploadFile, Form, Body
 from fastapi.responses import StreamingResponse
 from fastapi.middleware.cors import CORSMiddleware
 from typing import List
@@ -14,6 +14,7 @@ import recolorLumaConverterAlgo
 import recolorPaletteBasedTransfer
 import recolorReinhardV2Algo
 import recolorLinearColorTransfer
+import matchCollection
 from typing import Optional
 
 app = FastAPI()
@@ -131,12 +132,32 @@ async def recolor(file: UploadFile = File(...), colors: str = Form(...),  model:
         
     img_file = open("./result.jpg", "rb")
     return StreamingResponse(img_file, media_type="image/jpeg")
+
+
+# @app.post("/collection/")
+# async def create_collection(collection: str = Body(...), colors: List[str] = Body(...)):
+#     """
+#     Endpoint to create a collection with items.
+#     """
+#     result = matchCollection.predict_palette(collection, colors[0])
+#     print(result)
+#     #preparar o dado pra ser respondido
+#     return {"collection": result}
+
+@app.post("/collection/")
+async def create_collection(collection: str = Body(...), colors: List[str] = Body(...)):
+    """
+    Endpoint to create a collection with items.
+    """
+    palettes = [matchCollection.predict_palette(collection, color) for color in colors]
+    return {"collection": collection, "palettes": palettes}
+
 @app.get("/test/")
 async def test():
     """
     Test endpoint to check if the server is running.
     """
-    return {"message": "Server is running"}
+    return {"message": "Server is running!"}
 
 
 
@@ -152,4 +173,6 @@ if __name__ == "__main__":
 
 #how to run:
 #source env/bin/activate     
-#uvicorn server:app --reload
+#uvicorn server:app --reload
+
+#curl -X POST http://0.0.0.0:4201/collection/ \ -H "Content-Type: application/json" \ -d '{"collection": "FLORAL", "colors": ["#1f3b4a", "#597375", "#7f623e", "#5c453c"]}'

todo.md

@@ -11,4 +11,6 @@ source ./env/bin/activate
 #source env/bin/activate     
 #uvicorn server:app --reload
 #docker run -p 4201:7860 farm-recolor    
+#docker build -t farm-recolor .
+
 `