Update app.py

app.py

@@ -894,79 +894,79 @@ with ui.navset_card_tab(id="tab"):
    # with ui.nav_panel("Viral Model"):
   #      gr.load("models/Hack90/virus_pythia_31_1024").launch()
 
-    with ui.nav_panel("Viral Microstructure"):          
-        ui.page_opts(fillable=True)
-        ui.panel_title("Kmer Distribution")
-        with ui.layout_columns():
-            with ui.card():
-                ui.input_slider("kmer", "kmer", 0, 10, 5)
-                ui.input_slider("top_k", "top:", 0, 1000, 15)
+    # with ui.nav_panel("Viral Microstructure"):          
+    #     ui.page_opts(fillable=True)
+    #     ui.panel_title("Kmer Distribution")
+    #     with ui.layout_columns():
+    #         with ui.card():
+    #             ui.input_slider("kmer", "kmer", 0, 10, 5)
+    #             ui.input_slider("top_k", "top:", 0, 1000, 15)
                 
-                ui.input_selectize(  
-            "plot_type",  
-            "Select metric:",
-            ["percentage", "count"],
-            multiple=False,  
-        )
+    #             ui.input_selectize(  
+    #         "plot_type",  
+    #         "Select metric:",
+    #         ["percentage", "count"],
+    #         multiple=False,  
+    #     )
         
         
-        @render.plot
-        def plot():  
-            df = pd.read_csv('kmers.csv')
-            k = input.kmer()
-            top_k = input.top_k()
-            fig = None
-            if input.plot_type() == "count":
-                df = df[df['k'] == k]
-                df = df.head(top_k)
-                fig, ax = plt.subplots()
-                ax.bar(df['kmer'], df['count'])
-                ax.set_title(f"Most common {k}-mers")
-                ax.set_xlabel("K-mer")
-                ax.set_ylabel("Count")
-                ax.set_xticklabels(df['kmer'], rotation=90)
-            if input.plot_type() == "percentage":
-                df = df[df['k'] == k]
-                df = df.head(top_k)
-                fig, ax = plt.subplots()
-                ax.bar(df['kmer'], df['percent']*100)
-                ax.set_title(f"Most common {k}-mers")
-                ax.set_xlabel("K-mer")
-                ax.set_ylabel("Percentage")
-                ax.set_xticklabels(df['kmer'], rotation=90)
-            return fig
-
-    with ui.nav_panel("Viral Model Training"):    
-        ui.page_opts(fillable=True)
-        ui.panel_title("Does context size matter for a nucleotide model?")
+    #     @render.plot
+    #     def plot():  
+    #         df = pd.read_csv('kmers.csv')
+    #         k = input.kmer()
+    #         top_k = input.top_k()
+    #         fig = None
+    #         if input.plot_type() == "count":
+    #             df = df[df['k'] == k]
+    #             df = df.head(top_k)
+    #             fig, ax = plt.subplots()
+    #             ax.bar(df['kmer'], df['count'])
+    #             ax.set_title(f"Most common {k}-mers")
+    #             ax.set_xlabel("K-mer")
+    #             ax.set_ylabel("Count")
+    #             ax.set_xticklabels(df['kmer'], rotation=90)
+    #         if input.plot_type() == "percentage":
+    #             df = df[df['k'] == k]
+    #             df = df.head(top_k)
+    #             fig, ax = plt.subplots()
+    #             ax.bar(df['kmer'], df['percent']*100)
+    #             ax.set_title(f"Most common {k}-mers")
+    #             ax.set_xlabel("K-mer")
+    #             ax.set_ylabel("Percentage")
+    #             ax.set_xticklabels(df['kmer'], rotation=90)
+    #         return fig
+
+    # with ui.nav_panel("Viral Model Training"):    
+    #     ui.page_opts(fillable=True)
+    #     ui.panel_title("Does context size matter for a nucleotide model?")
         
-        def plot_loss_rates(df, type): 
-            # interplot each column to be same number of points
-            x = np.linspace(0, 1, 1000)
-            loss_rates = []
-            labels = ['32', '64', '128', '256', '512', '1024']
-            #drop the column step
-            df = df.drop(columns=['Step'])
-            for col in df.columns:
-                y = df[col].dropna().astype('float', errors = 'ignore').dropna().values
-                f = interp1d(np.linspace(0, 1, len(y)), y)
-                loss_rates.append(f(x))
-            fig, ax = plt.subplots()
-            for i, loss_rate in enumerate(loss_rates):
-                ax.plot(x, loss_rate, label=labels[i])
-            ax.legend()
-            ax.set_title(f'Loss rates for a {type} parameter model')
-            ax.set_xlabel('Training steps')
-            ax.set_ylabel('Loss rate')
-            return fig
+    #     def plot_loss_rates(df, type): 
+    #         # interplot each column to be same number of points
+    #         x = np.linspace(0, 1, 1000)
+    #         loss_rates = []
+    #         labels = ['32', '64', '128', '256', '512', '1024']
+    #         #drop the column step
+    #         df = df.drop(columns=['Step'])
+    #         for col in df.columns:
+    #             y = df[col].dropna().astype('float', errors = 'ignore').dropna().values
+    #             f = interp1d(np.linspace(0, 1, len(y)), y)
+    #             loss_rates.append(f(x))
+    #         fig, ax = plt.subplots()
+    #         for i, loss_rate in enumerate(loss_rates):
+    #             ax.plot(x, loss_rate, label=labels[i])
+    #         ax.legend()
+    #         ax.set_title(f'Loss rates for a {type} parameter model')
+    #         ax.set_xlabel('Training steps')
+    #         ax.set_ylabel('Loss rate')
+    #         return fig
             
-        @render.plot
-        def plot():
-            fig = None
-            df = pd.read_csv('14m.csv')
-            mpl.rcParams.update(mpl.rcParamsDefault)
-            fig = plot_loss_rates(df, '14M')
-            return fig
+    #     @render.plot
+    #     def plot():
+    #         fig = None
+    #         df = pd.read_csv('14m.csv')
+    #         mpl.rcParams.update(mpl.rcParamsDefault)
+    #         fig = plot_loss_rates(df, '14M')
+    #         return fig
 
         
 # @render.image