added autoplay

app.py

@@ -84,44 +84,7 @@ def clear_df():
 
 #@st.cache(show_spinner=False)
 def plot_fig(df,sent_id,xlims,ylims,color_list):
-    x_tsne, y_tsne = df.x_tsne, df.y_tsne
-    fig = plt.figure(figsize=(5,5),dpi=200)
-    ax = fig.add_subplot(1,1,1)
-    ax.plot(x_tsne[:sent_id+1],y_tsne[:sent_id+1],linewidth=0.2,color='gray',zorder=1)
-    ax.scatter(x_tsne[:sent_id+1],y_tsne[:sent_id+1],s=5,color=color_list[:sent_id+1],zorder=2)
-    ax.scatter(x_tsne[sent_id:sent_id+1],y_tsne[sent_id:sent_id+1],s=50,marker='*',color='blue',zorder=3)
-    ax.set_xlim(*xlims)
-    ax.set_ylim(*ylims)
-    ax.axis('off')
-    ax.set_title(df.cleaned_sentence.to_list()[sent_id])
-    #fig.savefig(f'figures/{sent_id}.png')
-    buf = io.BytesIO()
-    fig.savefig(buf, format="png", dpi=200)
-    buf.seek(0)
-    img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
-    buf.close()
-    img = cv2.imdecode(img_arr, 1)
-    plt.clf()
-    plt.close()
-    return img
 
-def pre_render_images(df,input_sent_id):
-    sent_id_options = [min(len(df)-1,max(0,input_sent_id+increment)) for increment in [-500,-100,-10,-1,0,1,10,100,500]]
-    x_tsne, y_tsne = df.x_tsne, df.y_tsne
-    xscale_unit = (max(x_tsne)-min(x_tsne))/10
-    yscale_unit = (max(y_tsne)-min(y_tsne))/10
-    xmax,xmin = (max(x_tsne)//xscale_unit+1)*xscale_unit,(min(x_tsne)//xscale_unit-1)*xscale_unit
-    ymax,ymin = (max(y_tsne)//yscale_unit+1)*yscale_unit,(min(y_tsne)//yscale_unit-1)*yscale_unit
-    color_list = sns.color_palette('flare',n_colors=int(len(df)*1.2))
-    sent_list = []
-    fig_list = []
-    fig_production = st.progress(0)
-    for fig_id,sent_id in enumerate(sent_id_options):
-        fig_production.progress(fig_id+1)
-        img = plot_fig(df,sent_id,[xmin,xmax],[ymin,ymax],color_list)
-        sent_list.append(df.cleaned_sentence.to_list()[sent_id])
-        fig_list.append(img)
-    return sent_list,fig_list
 
 def update_sent_id(increment_value=0):
     sent_id = st.session_state.sent_id
@@ -195,6 +158,13 @@ if __name__=='__main__':
 
     if 'df' in st.session_state:
         df = st.session_state.df
+        x_tsne, y_tsne = df.x_tsne, df.y_tsne
+        xscale_unit = (max(x_tsne)-min(x_tsne))/10
+        yscale_unit = (max(y_tsne)-min(y_tsne))/10
+        xlims = [(min(x_tsne)//xscale_unit-1)*xscale_unit,(max(x_tsne)//xscale_unit+1)*xscale_unit]
+        ylims = [(min(y_tsne)//yscale_unit-1)*yscale_unit,(max(y_tsne)//yscale_unit+1)*yscale_unit]
+        color_list = sns.color_palette('flare',n_colors=int(len(df)*1.2))
+
         st.sidebar.slider(label='2. Select a position in the chain to start exploring',
                             min_value=0,max_value=len(df)-1,value=0,key='sent_id_from_slider',on_change=initialize_sent_id)
         if 'sent_id' not in st.session_state:
@@ -205,31 +175,40 @@ if __name__=='__main__':
         else:
             explore_type = st.sidebar.radio('3. Choose the way to explore',options=['In fixed increments','Click through each step'])
         if explore_type=='Autoplay':
-            #if st.button('Create the video (this may take a few minutes)'):
-                #st.write('Creating the video...')
-                #x_tsne, y_tsne = df.x_tsne, df.y_tsne
-                #xscale_unit = (max(x_tsne)-min(x_tsne))/10
-                #yscale_unit = (max(y_tsne)-min(y_tsne))/10
-                #xlims = [(min(x_tsne)//xscale_unit-1)*xscale_unit,(max(x_tsne)//xscale_unit+1)*xscale_unit]
-                #ylims = [(min(y_tsne)//yscale_unit-1)*yscale_unit,(max(y_tsne)//yscale_unit+1)*yscale_unit]
-                #color_list = sns.color_palette('flare',n_colors=1200)
-                #fig_production = st.progress(0)
-
-                #img = plot_fig(df,0,xlims,ylims,color_list)
-                #img = cv2.imread('figures/0.png')
-                #height, width, layers = img.shape
-                #size = (width,height)
-                #out = cv2.VideoWriter('sampling_video.mp4',cv2.VideoWriter_fourcc(*'H264'), 3, size)
-                #for sent_id in range(1000):
-                #    fig_production.progress((sent_id+1)/1000)
-                #    img = plot_fig(df,sent_id,xlims,ylims,color_list)
-                    #img = cv2.imread(f'figures/{sent_id}.png')
-                #    out.write(img)
-                #out.release()
-            cols = st.columns([1,2,1])
+            cols = st.columns(2)
+            with cols[0]:
+                container_0 = st.container()
             with cols[1]:
-                with open(f'sampling_video_{sentence_num}.mp4', 'rb') as f:
-                    st.video(f)
+                container_1 = st.container()
+            if container_0.button('Play',key='play'):
+                while not container_1.button('Stop',key='stop'):
+                    for sent_id in range(st.session_state.sent_id_from_slider,len(st.session_state)):
+                        sentence = df.cleaned_sentence.to_list()[sent_id]
+                        input_sent = tokenizer(sentence,return_tensors='pt')['input_ids']
+                        decoded_sent = [tokenizer.decode([token]) for token in input_sent[0]]
+                        disp_style = '"font-family:san serif; color:Black; font-size: 20px"'
+                        if explore_type=='Click through each step' and input_type=='Use your own initial sentence' and sent_id>0 and 'finished_sampling' in st.session_state:
+                            sampled_loc = df.next_sample_loc.to_list()[sent_id-1]
+                            disp_step = f'<p style={disp_style}>Step {st.session_state.sent_id}&colon;&nbsp;'
+                            disp_sent_before = f'{disp_step}<span style="font-weight:bold">'+' '.join(decoded_sent[1:sampled_loc])
+                            new_word = f'<span style="color:Red">{decoded_sent[sampled_loc]}</span>'
+                            disp_sent_after = ' '.join(decoded_sent[sampled_loc+1:-1])+'</span></p>'
+                            st.markdown(disp_sent_before+' '+new_word+' '+disp_sent_after,unsafe_allow_html=True)
+                        else:
+                            disp_step = f'<p style={disp_style}>Step {st.session_state.sent_id}&colon;&nbsp;'
+                            st.markdown(f'{disp_step}<span style="font-weight:bold">{sentence}</span></p>',unsafe_allow_html=True)
+
+                        fig = plt.figure(figsize=(5,5),dpi=200)
+                        ax = fig.add_subplot(1,1,1)
+                        ax.plot(x_tsne[:sent_id+1],y_tsne[:sent_id+1],linewidth=0.2,color='gray',zorder=1)
+                        ax.scatter(x_tsne[:sent_id+1],y_tsne[:sent_id+1],s=5,color=color_list[:sent_id+1],zorder=2)
+                        ax.scatter(x_tsne[sent_id:sent_id+1],y_tsne[sent_id:sent_id+1],s=50,marker='*',color='blue',zorder=3)
+                        ax.set_xlim(*xlims)
+                        ax.set_ylim(*ylims)
+                        ax.axis('off')
+                        cols = st.columns([1,2,1])
+                        with cols[1]:
+                            st.pyplot(fig)
         else:
             if explore_type=='In fixed increments':
                 button_labels = ['+1','+10','+100','+500']
@@ -247,30 +226,15 @@ if __name__=='__main__':
             elif explore_type=='Click through each step':
                 st.session_state.sent_id = st.sidebar.number_input(label='step number',value=st.session_state.sent_id_from_slider)
 
-            x_tsne, y_tsne = df.x_tsne, df.y_tsne
-            xscale_unit = (max(x_tsne)-min(x_tsne))/10
-            yscale_unit = (max(y_tsne)-min(y_tsne))/10
-            xlims = [(min(x_tsne)//xscale_unit-1)*xscale_unit,(max(x_tsne)//xscale_unit+1)*xscale_unit]
-            ylims = [(min(y_tsne)//yscale_unit-1)*yscale_unit,(max(y_tsne)//yscale_unit+1)*yscale_unit]
-            color_list = sns.color_palette('flare',n_colors=int(len(df)*1.2))
-
             sent_id = st.session_state.sent_id
-            fig = plt.figure(figsize=(5,5),dpi=200)
-            ax = fig.add_subplot(1,1,1)
-            ax.plot(x_tsne[:sent_id+1],y_tsne[:sent_id+1],linewidth=0.2,color='gray',zorder=1)
-            ax.scatter(x_tsne[:sent_id+1],y_tsne[:sent_id+1],s=5,color=color_list[:sent_id+1],zorder=2)
-            ax.scatter(x_tsne[sent_id:sent_id+1],y_tsne[sent_id:sent_id+1],s=50,marker='*',color='blue',zorder=3)
-            ax.set_xlim(*xlims)
-            ax.set_ylim(*ylims)
-            ax.axis('off')
-
             sentence = df.cleaned_sentence.to_list()[sent_id]
             input_sent = tokenizer(sentence,return_tensors='pt')['input_ids']
             decoded_sent = [tokenizer.decode([token]) for token in input_sent[0]]
+            char_nums = [len(word)+2 word for word in decoded_sent]
             show_candidates = st.checkbox('Show candidates')
             if show_candidates:
                 st.write('Click any word to see each candidate with its probability')
-                cols = st.columns(len(decoded_sent))
+                cols = st.columns(char_nums)
                 with cols[0]:
                     st.write(decoded_sent[0])
                 with cols[-1]:
@@ -293,6 +257,15 @@ if __name__=='__main__':
                 else:
                     disp_step = f'<p style={disp_style}>Step {st.session_state.sent_id}&colon;&nbsp;'
                     st.markdown(f'{disp_step}<span style="font-weight:bold">{sentence}</span></p>',unsafe_allow_html=True)
+
+            fig = plt.figure(figsize=(5,5),dpi=200)
+            ax = fig.add_subplot(1,1,1)
+            ax.plot(x_tsne[:sent_id+1],y_tsne[:sent_id+1],linewidth=0.2,color='gray',zorder=1)
+            ax.scatter(x_tsne[:sent_id+1],y_tsne[:sent_id+1],s=5,color=color_list[:sent_id+1],zorder=2)
+            ax.scatter(x_tsne[sent_id:sent_id+1],y_tsne[sent_id:sent_id+1],s=50,marker='*',color='blue',zorder=3)
+            ax.set_xlim(*xlims)
+            ax.set_ylim(*ylims)
+            ax.axis('off')
             cols = st.columns([1,2,1])
             with cols[1]:
                 st.pyplot(fig)