finito

app.py

@@ -2,6 +2,8 @@ import numpy as np
 import gradio as gr
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
+import torch
+import pandas as pd
 
 
 
@@ -9,20 +11,27 @@ processor = AutoImageProcessor.from_pretrained("Moreza009/HF_CVcourse_FoodClassi
 model = AutoModelForImageClassification.from_pretrained("Moreza009/HF_CVcourse_FoodClassifier")
 
 def classifier(image):
-    image = Image.open(image)
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    #image = Image.open(image)
     inputs = processor(images=image, return_tensors="pt")
     outputs = model(**inputs)
     logits = outputs.logits
-    predicted_class_idx = logits.argmax(-1).item()
-    return model.config.id2label[predicted_class_idx]
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+    predicted_class_idxs = probabilities.topk(5, dim=-1)[1].tolist()[0]
+    probabilities = probabilities.tolist()[0][:5]
+    classes = [model.config.id2label[idx] for idx in predicted_class_idxs]
+    df = pd.DataFrame({'food':classes , 'posibility': probabilities})
+    return df.to_html(index=False)
 
 
 food = gr.Interface(
     fn=classifier,
-    inputs=gr.Image(),
-    outputs="text",
+    inputs=gr.Image(type="pil"),
+    outputs="html",
     title = "what's your eating?",
-    description = "A simple model for food classification"
+    description = " :)  "
 )
 
+
 food.launch()

requirements.txt

@@ -2,4 +2,6 @@ torch
 transformers
 gradio
 pillow
+numpy
+pandas
 numpy