Update app.py

app.py

@@ -15,6 +15,16 @@ import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.style as mplstyle
 from pathlib import Path
+from shiny import render
+from shiny.express import input, ui
+import pandas as pd
+from pathlib import Path
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd 
+from scipy.interpolate import interp1d
+import numpy as np
+
 
 # Mapping of nucleotides to float coordinates
 mapping_easy = {
@@ -881,8 +891,50 @@ with ui.navset_card_tab(id="tab"):
                 filtered_df = df.groupby('Organism_Name').apply(filter_and_select).reset_index(drop=True)
                 fig = plot_persistence_homology(filtered_df['Sequence'], filtered_df['Organism_Name'])
             return fig
-    with ui.nav_panel("Viral Model"):
-        gr.load("models/Hack90/virus_pythia_31_1024").launch()
+   # 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)
+                
+                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)
@@ -915,6 +967,7 @@ with ui.navset_card_tab(id="tab"):
             mpl.rcParams.update(mpl.rcParamsDefault)
             fig = plot_loss_rates(df, '14M')
             return fig
+
         
 # @render.image  
 # def image():