Update app.py

app.py

@@ -1,66 +1,55 @@
-'''PyTorch Food Classifier - FastAI 2022- Mostly Good For Pastries
-and trained on ResNet 34'''
-
-import streamlit as st
-import os
-from PIL import Image
-import time
-from fastai.vision.all import *
-from fastai.learner import load_learner
-
-def is_penguin(img):
-    return img.split('-')[0]
-    
-
-#Load the Learner (Exported from ipnyb file with learn.export() )
-learn = load_learner('model.pkl')
-
-
-#Classify image
-def classify_image(cl_img):
-    img = Image.open(cl_img)
-    st.image(img)
-    pred, _ , _ = learn.predict(img)
-    return pred
+# '''PyTorch Food Classifier - FastAI 2022- Mostly Good For Pastries
+# and trained on ResNet 34'''
 
+# import streamlit as st
+# import os
+# from PIL import Image
+# import time
+# from fastai.vision.all import *
+# from fastai.learner import load_learner
 
+# def is_penguin(img):
+#     return img.split('-')[0]
     
-    
-st.set_page_config(page_title="PyTorch Food Classifier - FastAI 2022", page_icon=":robot:")
-st.header("PyTorch Food Classifier")
+# #Load the Learner (Exported from ipnyb file with learn.export() )
+# learn = load_learner('model.pkl')
 
-file_up = st.file_uploader("Upload Your Food Image Below", type=["jpg","png"])
+# categories = ('Penguin', 'Puffin')
 
-if st.button('Run Model'):
-    st.write("Button Pressed")
-    cl_done = classify_image(file_up)
+# #Classify image
+# def classify_image(cl_img):
+#     img = Image.open(cl_img)
+#     st.image(img)
+#     pred, _ , prob = learn.predict(img)
+#     return dict(zip(categories, map(float, prob)))
     
-    st.write(f"Your food is:  {cl_done}")
-
-st.write('This classifier is trained on Resnet-34 and works primarily for three classes. Donuts, CheeseCake and Panna Cotta (HOTDOGNOTHOTDOG).\n\n Thankyou to FastAI for the exercise.')
-
+# st.set_page_config(page_title="Penguin vs Puffin Classifier - FastAI 2025", page_icon=":robot:")
+# st.header("Penguin vs Puffin Classifier")
 
+# file_up = st.file_uploader("Upload Your Image Below", type=["jpg","png"])
 
+# if st.button('Run Model'):
+#     st.write("Button Pressed")
+#     cl_done = classify_image(file_up)
+#     st.write(f"Your food is:  {cl_done}")
 
+# st.write('This classifier is trained on Resnet-34 and specializes in differentiating penguins from puffins).')
 
-# from fastai.vision.all import *
-# from fastai.learner import load_learner
-# import gradio as gr
-
-# import sys
-# print(f"Python version: {sys.version}")
+from fastai.vision.all import *
+from fastai.learner import load_learner
+import gradio as gr
 
-# learn = load_learner('model.pkl')
+learn = load_learner('model.pkl')
 
-# categories = ('Penguin', 'Puffin')
+categories = ('Penguin', 'Puffin')
 
-# def classifyImage(img):
-#     pred, idx, prob = learn.predict(img)
-#     return dict(zip(categories, map(float, prob)))
+def classifyImage(img):
+    pred, idx, prob = learn.predict(img)
+    return dict(zip(categories, map(float, prob)))
 
-# image = gr.Image(shape=(192, 192))
-# label = gr.Label()
-# examples = ['penguin.jpg', 'puffin.png', 'razorbill.jpg']
+image = gr.Image(shape=(192, 192))
+label = gr.Label()
+examples = ['penguin.jpg', 'puffin.png', 'razorbill.jpg']
 
-# intf = gr.Interface(fn=classifyImage, inputs=image, outputs=label, examples=examples)
-# intf.launch(inline=False)
+intf = gr.Interface(fn=classifyImage, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)