Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import openpyxl
+
+#Function to predict the food from the image using the pre-trained model "nateraw/food"
+def predict(image):
+    extractor = AutoFeatureExtractor.from_pretrained("nateraw/food")
+    model = AutoModelForImageClassification.from_pretrained("nateraw/food")
+    
+    input = extractor(images=image, return_tensors='pt')
+    output = model(**input)
+    logits = output.logits
+    
+    pred_class = logits.argmax(-1).item()
+    return(model.config.id2label[pred_class])
+
+#Function to retrieve the Nutritional Value from database.xlsx which is downloaded from USDA
+def check_food(food, counter):
+    path = './database.xlsx'
+    wb_obj = openpyxl.load_workbook(path)
+    sheet_obj = wb_obj.active
+    
+    foodPred, cal, carb, prot, fat = None, None, None, None, None
+    
+    #Filter to prioritize the most probable match between the prediction and the entries in the database
+    for i in range(3, sheet_obj.max_row+1):
+        cell_obj = sheet_obj.cell(row = i, column = 2)
+        if counter == 0:
+            if len(food) >= 3:
+                foodName = food[0].capitalize() + " " + food[1] + " " + food[2] + ","
+            elif len(food) == 2:
+                foodName = food[0].capitalize() + " " + food[1] + ","
+            elif len(food) == 1:
+                foodName = food[0].capitalize() + ","
+            condition = foodName == cell_obj.value[0:len(foodName):]
+        elif counter == 1:
+            if len(food) >= 3:
+                foodName = food[0].capitalize() + " " + food[1] + " " + food[2]
+            elif len(food) == 2:
+                foodName = food[0].capitalize() + " " + food[1]
+            elif len(food) == 1:
+                foodName = food[0].capitalize()
+            condition = foodName == cell_obj.value[0:len(foodName):]
+        elif counter == 2:
+            if len(food) >= 3:
+                foodName = food[0] + " " + food[1] + " " + food[2]
+            elif len(food) == 2:
+                foodName = food[0] + " " + food[1]
+            elif len(food) == 1:
+                foodName = food[0]
+            condition = foodName in cell_obj.value
+        elif (counter == 3) & (len(food) > 1):
+            condition = food[0] in cell_obj.value
+        else:
+            break
+        
+        #Update values if conditions are met
+        if condition:
+            foodPred = cell_obj.value
+            cal = sheet_obj.cell(row = i, column = 5).value
+            carb = sheet_obj.cell(row = i, column = 7).value
+            prot = sheet_obj.cell(row = i, column = 6).value
+            fat = sheet_obj.cell(row = i, column = 10).value
+            break
+    
+    return foodPred, cal, carb, prot, fat
+
+#Function to prepare the output
+def get_cc(food, weight):
+
+    #Configure the food string to match the entries in the database
+    food = food.split("_")
+    if food[-1][-1] == "s":
+        food[-1] = food[-1][:-1]
+    
+    foodPred, cal, carb, prot, fat = None, None, None, None, None
+    counter = 0
+    
+    #Try for the most probable match between the prediction and the entries in the database
+    while (not foodPred) &  (counter <= 3):
+        foodPred, cal, carb, prot, fat = check_food(food,counter)
+        counter += 1
+
+    #Check if there is a match
+    if food:        
+        output = foodPred + "\nCalories: " + str(round(cal * weight)/100) + " kJ\nCarbohydrate: " + str(round(carb * weight)/100) + " g\nProtein: " + str(round(prot * weight)/100) + " g\nTotal Fat: " + str(round(fat * weight)/100) + " g"      
+    elif not food:
+        output = "No data for food"
+
+    return(output)
+
+#Main function
+def CC(image, weight):
+    pred = predict(image)
+    cc = get_cc(pred, weight)
+    return(pred, cc)
+
+interface = gr.Interface(
+    fn = CC,
+    inputs = [gr.inputs.Image(shape=(224,224)), gr.inputs.Number(default = 100, label = "Weight in grams (g):")],
+    outputs = [gr.outputs.Textbox(label='Food Prediction:'), gr.outputs.Textbox(label='Nutritional Value:')],
+    examples = [["pizza.jpg", 107], ["spaghetti.jpg",205]])
+
+interface.launch()