add figures and change app.py

app.py

@@ -0,0 +1,67 @@
+# Bismillahir Rahmaanir Raheem 
+# Almadadh Ya Gause Radi Allahu Ta'alah Anh - Ameen 
+
+
+from fastai.vision.all import *
+import gradio as gr
+
+
+def is_pneumonia(x): 
+    return (x.find('virus')!=-1 or x.find('bacteria')!=-1)
+	
+
+
+# load the trained fast ai model for predictions
+learn = load_learner('model.pkl')
+
+
+# define the function to call
+categories = ('Pneumonia', 'Normal') 
+
+def predict(img):
+	pred, idx, probs = learn.predict(img)
+	return dict(zip(categories, map(float, probs)))
+	
+	
+title = "Pneumonia or Normal X-Ray Predictor"
+
+description = "A pneumonia or normal x-ray predictor model trained on the chest-xray-pneumonia dataset with fastai. </br> Dataset taken from: <a href='https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia' target='_blank'>Chest X-Ray Images (Pneumonia)</a> and the associated scientific journal paper is <a href='https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5' target='_blank'>Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning</a>"
+
+
+article = "<p style='text-align: center'><span style='font-size: 15pt;'>Pneumonia or Normal X-Ray Predictor. Zakia Salod. 2022. </span></p>"
+
+
+image = gr.inputs.Image(shape=(512, 512))
+label = gr.outputs.Label()
+examples = [
+             ['person1_virus_6.jpeg'],
+			 ['NORMAL2-IM-0285-0001.jpeg'],
+			 ['person82_bacteria_404.jpeg'],
+			 ['NORMAL2-IM-0373-0001.jpeg'],
+			 ['person1618_virus_2805.jpeg'],
+			 ['NORMAL2-IM-0381-0001.jpeg'],
+			 ['person159_bacteria_747.jpeg'],
+			 ['NORMAL2-IM-0222-0001.jpeg'],
+		   ]
+interpretation = 'default'
+enable_queue = True 
+
+
+
+
+iface = gr.Interface(
+		fn=predict, 
+		title=title, 
+		description=description, 
+		article=article,
+		inputs=image, 
+		outputs=label,
+		theme="huggingface",
+		examples=examples,
+		interpretation=interpretation,
+		enable_queue=enable_queue
+)
+
+
+
+iface.launch(inline=False)