Update app.py

app.py

@@ -1,5 +1,51 @@
+# This Python 3 environment comes with many helpful analytics libraries installed
+# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
+# For example, here's several helpful packages to load
+
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+
+# Input data files are available in the read-only "../input/" directory
+# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))
+
+# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
+# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
+
+!pip install -Uqq fastai
+!pip install gradio
+
+import gradio as gr
+
 import gradio as gr
 
+from fastai.vision.all import*
+
+def is_healthy(x): return x[0].isupper()
+
+im = PILImage.create('/kaggle/input/healthy-cells/istockphoto-1376243518-612x612.jpg')
+im.thumbnail((192,192))
+im
+
+learn = load_learner('/kaggle/input/011pdk/ML HT6 Model 011 perc.pkl')
+
+learn.predict(im)
+
+categories = ('Healthy Cell', 'Leukemia', 'Sickle Cell', 'Thalassemia')
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+classify_image(im)
+
+import gradio as gr
+
+
+
 # Assuming no shape needs to be specified directly in the constructor
 image = gr.Image()
 label = gr.Label()