Update app.py

app.py

@@ -1,7 +1,7 @@
 import subprocess
 
 #port =  $PORT
-subprocess.run("uvicorn application.server.main:app --host 0.0.0.0 --port 7860", shell=True)
+#subprocess.run("uvicorn application.server.main:app --host 0.0.0.0 --port 7860", shell=True)
 
 
 import streamlit as st
@@ -10,6 +10,11 @@ import requests
 from PIL import Image
 import os
 
+#option 2
+import numpy as np
+from tensorflow.keras.models import load_model
+import cv2
+
 
 def load_image(image):
 	img = Image.open(image)
@@ -22,9 +27,33 @@ def save_uploadedfile(uploadedfile):
      return uploaded_location#st.success("Saved File:{} to {}".format(uploadedfile.name, uploaded_location))
      
 
+def image_predict (image_file):
+    model_path = 'application/models/resnet_ct.h5'  
+    h5_model = load_model(model_path)
+    image = cv2.imread(image_file)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = cv2.resize(image, (224, 224)) 
+    image = np.array(image) / 255
+    image = np.expand_dims(image, axis=0)
+    h5_prediction = h5_model.predict(image)  
+    print('Prediction from h5 model: {}'.format(h5_prediction))
+    print(h5_prediction)
+    probability = h5_prediction[0]
+    print("H5 Predictions:")
+    print (probability)
+    if probability[0] > 0.5:
+        covid_chest_pred = str('%.2f' % (probability[0] * 100) + '% COVID-Positive')
+        probability = (probability[0] * 100)
+    else:
+        covid_chest_pred = str('%.2f' % ((1 - probability[0]) * 100) + '% COVID-Negative')
+        probability = ((1 - probability[0]) * 100)
+    return  covid_chest_pred
+         
+
 st.title("Covid Prediction App from CT Images")
 
 
+
 #taking user inputs
 
 st.write("")
@@ -47,12 +76,22 @@ if image is not None:
 	  #save image to disk
 	  saved = save_uploadedfile(image)
 
-	  if st.button ('Analyze'):
-	  	  test_file = open(os.path.join("images/img", image.name), "rb")
-	  	  response = requests.post('http://127.0.0.1:8000/predict/image', files={'file': test_file })
-	  	  prediction = response.json()##json_object["prediction"]
-	  	  st.write(prediction)
-	  	  st. subheader (f"Response from Covid Analyzer API = {prediction}")
+	  #if st.button ('Analyze'):
+	  	  #test_file = open(os.path.join("images/img", image.name), "rb")
+	  	  #response = requests.post('http://127.0.0.1:8000/predict/image', files={'file': test_file })
+	  	  #prediction = response.json()##json_object["prediction"]
+	  	  #st.write(prediction)
+	  	  #st. subheader (f"Response from Covid Analyzer API = {prediction}")
+
+         #OPTION 2 - NON API..
+            if st.button ('Analyze'):
+                with st.spinner('Analyzing...'):
+                    prediction = image_predict(saved)
+                    #st.write(prediction)
+                    st. subheader (f"Image Prediction = {prediction}")
+                    st.success(f"Image Prediction = {prediction}", icon="✅")
+
+    
 
 #RUN BOTH...
 #streamlit run app.py