Added classification and recipe generator

app.py

@@ -5,12 +5,20 @@ import uvicorn
 
 from PIL import Image
 
+import requests
+
 import torch
 import torch.nn.functional as F
 from torchvision import transforms 
 
 app = FastAPI()
 
+def grab_recipes(food: str):
+    result = requests.get(f'https://api.spoonacular.com/recipes/complexSearch?apiKey={}&query={food}&addRecipeInformation=true&number=2')
+    recipes = result.json()
+    
+    return recipes
+    
 def transform_img(img: Image.Image) -> torch.tensor:
     # Transformations that will be applied
     the_transform = transforms.Compose([
@@ -36,16 +44,14 @@ def classify_img(img: Image.Image) -> dict:
     
     # Converting values to softmax values 
     result = F.softmax(result,dim=1)
-    # Grabbing top 3 indices and probabilities for each index
-    top3_prob, top3_catid = torch.topk(result,3)
+    # Grabbing top 1 index and probability
+    top1_prob, top1_catid = torch.topk(result,1)
     
     # Dictionary I will display
     model_output = {}
-    for i in range(top3_prob.size(1)):
-        fast_food_name = class_names[top3_catid[0][i].item()]
-        probability = round(top3_prob[0][i].item() * 100, 2)
-        
-        model_output[f"top{i+1}"] = {"name": fast_food_name, "probability": probability}
+    fast_food_name = class_names[top1_catid[0][0].item()]
+    probability = round(top1_prob[0][0].item() * 100, 2)
+    model_output[f"winner"] = {"name": fast_food_name, "probability": probability}
     
     return model_output
 
@@ -53,9 +59,12 @@ def classify_img(img: Image.Image) -> dict:
 async def upload(file: UploadFile = File(...)) -> JSONResponse:
 
     pil_img = Image.open(file.file)
-    result = classify_img(pil_img)
+    classifier_result = classify_img(pil_img)
     
-    return JSONResponse(content=result, status_code=201)
+    recipes = grab_recipes(classifier_result['winner']['name'])
+    recipes['fastfoodwinner'] = classifier_result
+
+    return JSONResponse(content=recipes, status_code=201)
 
 @app.get("/")
 def api_home():