Update src/streamlit_app.py

src/streamlit_app.py

@@ -169,11 +169,12 @@ def fetch_image():
 
 # ---------------------------
 
-placeholder = st.empty()
+
 
  
 
 st.title("Neural Network Classification Demo")
+placeholder = st.empty()
 
  
 
@@ -199,102 +200,102 @@ if 'model' not in st.session_state:
 
     st.session_state.calculating=False
 
-
-with placeholder.container():
-
-    # Button to fetch a new image
-    col1, col2, col3 = st.columns([1,1,1])
-    
-    if st.session_state.current_image is not None and st.session_state.started is False:
-        st.session_state.started=True
-    
-    with col1: 
-        if st.button("Start",disabled=st.session_state.started):
-            if not st.session_state.started:
-                fetch_image()
-    
+while True:
+    with placeholder.container():
     
-    # Display the current image
-    
-    if st.session_state.current_image is not None:
-        with col2:
-            if st.button("cat"):#,disabled=st.session_state.calculating):
-                print("cat pressed")
-                st.session_state.label_input="cat"
-        with col3:
-            if st.button("dog"):#,disabled=st.session_state.calculating):
-                print("dog pressed")
-                st.session_state.label_input="dog"
-        print(f"SHAPE:{st.session_state.current_image.shape}")
-        prediction = st.session_state.model.predict(np.array([st.session_state.current_image]))[0][0]
-    
-        st.session_state.current_prediction = 'dog' if prediction > 0.5 else 'cat'
-        st.success(f"**Model Predicts:** {st.session_state.current_prediction} --- (cat-confidence {(1-prediction)*100:.2f}%; dog-confidence {(prediction)*100:.2f}%)")
-    
-        st.image(st.session_state.unprocessed_image)
+        # Button to fetch a new image
+        col1, col2, col3 = st.columns([1,1,1])
         
-    
-     
-    
-        # User input for label
-     
-    
-        if st.session_state.label_input in ['cat', 'dog']:
-            label_input=st.session_state.label_input
-            st.session_state.label_input="None"
-    
-            # Convert user input to 0 (cat) or 1 (dog)
-            print(f"LABEL CLICKED IS: {label_input.lower()}")
-            label = 0 if label_input.lower() == 'cat' else 1
-    
-            st.session_state.current_label = label
-    
-     
-    
-            # Add the labeled image and label to training data
-    
-            st.session_state.training_data.append((st.session_state.current_image, label))
-    
-     
-    
-            # Retrain the model
-    
-            image = np.array([img for img, _ in st.session_state.training_data])
-    
-            label = np.array([lab for _, lab in st.session_state.training_data])
-    
+        if st.session_state.current_image is not None and st.session_state.started is False:
+            st.session_state.started=True
+        
+        with col1: 
+            if st.button("Start",disabled=st.session_state.started):
+                if not st.session_state.started:
+                    fetch_image()
+        
+        
+        # Display the current image
+        
+        if st.session_state.current_image is not None:
+            with col2:
+                if st.button("cat"):#,disabled=st.session_state.calculating):
+                    print("cat pressed")
+                    st.session_state.label_input="cat"
+            with col3:
+                if st.button("dog"):#,disabled=st.session_state.calculating):
+                    print("dog pressed")
+                    st.session_state.label_input="dog"
+            print(f"SHAPE:{st.session_state.current_image.shape}")
+            prediction = st.session_state.model.predict(np.array([st.session_state.current_image]))[0][0]
+        
+            st.session_state.current_prediction = 'dog' if prediction > 0.5 else 'cat'
+            st.success(f"**Model Predicts:** {st.session_state.current_prediction} --- (cat-confidence {(1-prediction)*100:.2f}%; dog-confidence {(prediction)*100:.2f}%)")
+        
+            st.image(st.session_state.unprocessed_image)
             
-    
-     
-    
-            # Predict the current image
-    
-            st.session_state.current_image=None
-    
-            print("before model fit")
-            def model_fit():
-                print("Entering model fit function")
-                st.session_state.model.fit(image, label, epochs=1)
-                st.write(st.session_state.model.evaluate(image,  label, verbose=2))
-                st.session_state.calculating=True
-                st.write("hi")
-                return
-            model_fit()
-    
-    if st.session_state.calculating:
         
-        print("after model fit")
-        #st.session_state.unprocessed_image=None
-        print("before fetch_image")
-    
-        fetch_image()
-        print("after fetch_image")
-        st.info(f"You clicked on last picture (picture {st.session_state.which_pic}): {label_input}")
-        st.session_state.which_pic=st.session_state.which_pic+1
-        st.session_state.calculating=False
-    
+         
+        
+            # User input for label
+         
+        
+            if st.session_state.label_input in ['cat', 'dog']:
+                label_input=st.session_state.label_input
+                st.session_state.label_input="None"
+        
+                # Convert user input to 0 (cat) or 1 (dog)
+                print(f"LABEL CLICKED IS: {label_input.lower()}")
+                label = 0 if label_input.lower() == 'cat' else 1
+        
+                st.session_state.current_label = label
+        
+         
+        
+                # Add the labeled image and label to training data
+        
+                st.session_state.training_data.append((st.session_state.current_image, label))
+        
+         
+        
+                # Retrain the model
+        
+                image = np.array([img for img, _ in st.session_state.training_data])
+        
+                label = np.array([lab for _, lab in st.session_state.training_data])
+        
+                
+        
+         
+        
+                # Predict the current image
+        
+                st.session_state.current_image=None
+        
+                print("before model fit")
+                def model_fit():
+                    print("Entering model fit function")
+                    st.session_state.model.fit(image, label, epochs=1)
+                    st.write(st.session_state.model.evaluate(image,  label, verbose=2))
+                    st.session_state.calculating=True
+                    st.write("hi")
+                    return
+                model_fit()
+        
+        if st.session_state.calculating:
             
-
+            print("after model fit")
+            #st.session_state.unprocessed_image=None
+            print("before fetch_image")
+        
+            fetch_image()
+            print("after fetch_image")
+            st.info(f"You clicked on last picture (picture {st.session_state.which_pic}): {label_input}")
+            st.session_state.which_pic=st.session_state.which_pic+1
+            st.session_state.calculating=False
+        
+                
+        time.sleep(1)