Update app.py

app.py

@@ -12,91 +12,108 @@ from IPython.display import HTML, display
 import uuid
 import base64
 import json
+import tkinter
+import matplotlib
+matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
 
-st.title("Ask Questions to Data")
-st.markdown("##### Demo Application powered by sketch package")
-st.sidebar.image("https://avatars.githubusercontent.com/u/106505054?s=200&v=4", width=100)
-st.sidebar.title("About the Package used")
-st.sidebar.markdown("##### Sketch is an AI code-writing assistant for pandas users that understands the context of the data, greatly improving the relevance of suggestions. Sketch is usable in seconds and doesn't require adding a plugin to IDE.")
+col1, mid, col2= st.columns([1,1,20])
+with col1:
+    st.image("https://avatars.githubusercontent.com/u/106505054?s=200&v=4", width=70)
+with col2:
+    st.markdown("# Ask Questions to Data")
+st.markdown("###### Streamlit implementation of sketch package in pandas ")
 
-st.sidebar.title("How it works:")
-st.sidebar.markdown("##### Sketch uses efficient approximation algorithms (data sketches) to quickly summarize the data, and feed that information into language models. Right now, it does this by summarizing the columns and writing these summary statistics as additional context to be used by the code-writing prompt. In the future, the dev team hopes to feed these sketches directly into custom made data + language foundation models to get more accurate results.")
 
-st.sidebar.title("Usecases:")
-st.sidebar.markdown("##### Data Catalogging: General tagging (eg. PII identification), Metadata generation (names and descriptions)")
-st.sidebar.markdown("##### Data Engineering: Data cleaning and masking (compliance), Derived feature creation and extraction")
-st.sidebar.markdown("##### Data Analysis: Data questions, Data Visualizations")
+tab1, tab2 = st.tabs(["Ask Questions to Data", "About the Application"])
 
-st.sidebar.caption("Github Repository: https://github.com/approximatelabs/sketch")
+with tab1:
 
+    def upload_data_file():
+        st.session_state.file = None
+        st.session_state.df = None
+        file = st.file_uploader(
+            label='Upload Data File',
+            type=["csv","xlsx","xls"]
+        )
+        if file is not None:
+            load_data(file)
+           
+        
+    def load_data(file):
+        st.session_state.file = file
+        df = pd.read_csv(file)
+        st.session_state.df = df
+        
+            
+    # Configure session state
+    if 'file' not in st.session_state:
+        st.session_state.file = None
+    if 'df' not in st.session_state:
+        st.session_state.df = None
 
+        
+    if st.session_state.file is None:
+        upload_data_file()
 
 
+    def to_b64(data):
+        return base64.b64encode(json.dumps(data).encode("utf-8")).decode("utf-8")
 
-def upload_data_file():
-    st.session_state.file = None
-    st.session_state.df = None
-    file = st.file_uploader(
-        label='Upload Data File',
-        type=["csv","xlsx","xls"]
-    )
-    if file is not None:
-        load_data(file)
-       
-    
-def load_data(file):
-    st.session_state.file = file
-    df = pd.read_csv(file)
-    st.session_state.df = df
-    
-        
-# Configure session state
-if 'file' not in st.session_state:
-    st.session_state.file = None
-if 'df' not in st.session_state:
-    st.session_state.df = None
+    if st.session_state.file is not None:
+        st.session_state.file.seek(0)
 
-    
-if st.session_state.file is None:
-    upload_data_file()
+        df = pd.read_csv(st.session_state.file)
 
+        st.header("Uploaded Data:")
+        st.dataframe(df)
 
-def to_b64(data):
-    return base64.b64encode(json.dumps(data).encode("utf-8")).decode("utf-8")
+        with st.form("my_form"):
+            request_type = st.radio(
+                label="Selection Panel",
+                options=['Ask question about the data', 'Generate codes for new analysis'],
+                index=0
+            )
 
-if st.session_state.file is not None:
-    st.session_state.file.seek(0)
+            request = st.text_area(
+                label="Input your request",
+                value="",
+                height=50,
+                max_chars=500
+            )
 
-    df = pd.read_csv(st.session_state.file)
+            submitted = st.form_submit_button("Submit")
 
-    st.header("Uploaded Data:")
-    st.dataframe(df)
+        if submitted:
+            if request_type== 'Ask question about the data':
+                if request != "":
+                    answer = df.sketch.ask(request, call_display=False)
+                    st.code(answer)
+            else:
+                if request != "":
+                    answer1 = df.sketch.howto(request, call_display=False)
+                    st.code(answer1)
 
-    with st.form("my_form"):
-        request_type = st.radio(
-            label="Selection Panel",
-            options=['Ask question about the data', 'Generate codes for new analysis'],
-            index=0
-        )
+                    # Plot the bar chart
 
-        request = st.text_area(
-            label="Input your request",
-            value="",
-            height=50,
-            max_chars=500
-        )
+                    exec(answer1)
+        
+    else:
+        st.write('Please upload data file in order to ask questions to it.')
 
-        submitted = st.form_submit_button("Submit")
-
-    if submitted:
-        if request_type== 'Ask question about the data':
-            if request != "":
-                answer = df.sketch.ask(request, call_display=False)
-                st.code(answer)
-        else:
-            if request != "":
-                answer1 = df.sketch.howto(request, call_display=False)
-                st.code(answer1)
+with tab2:
     
-else:
-    st.write('Please upload data file in order to ask questions to it.')
+    st.title("Demo video")
+    st.video('https://youtu.be/hDbTjWqmrJY')
+    st.title("About the Package used")
+    st.markdown("###### Sketch is an AI code-writing assistant for pandas users that understands the context of the data, greatly improving the relevance of suggestions. Sketch is usable in seconds and doesn't require adding a plugin to IDE.")
+
+    st.title("How it works:")
+    st.markdown("###### Sketch uses efficient approximation algorithms (data sketches) to quickly summarize the data, and feed that information into language models. Right now, it does this by summarizing the columns and writing these summary statistics as additional context to be used by the code-writing prompt. In the future, the dev team hopes to feed these sketches directly into custom made data + language foundation models to get more accurate results.")
+
+    st.title("Usecases:")
+    st.markdown("###### --- Data Catalogging: General tagging (eg. PII identification), Metadata generation (names and descriptions)")
+    st.markdown("###### --- Data Engineering: Data cleaning and masking (compliance), Derived feature creation and extraction")
+    st.markdown("###### --- Data Analysis: Data questions, Data Visualizations")
+
+    st.caption("More details available in Github Repository: https://github.com/approximatelabs/sketch")