Update streamlit_app.py

streamlit_app.py

@@ -1,204 +1,212 @@
-# import streamlit as st
-# import requests
-# import json
-
-# st.set_page_config(page_title="QueryMate: Text to SQL & CSV")
-
-# st.markdown("# QueryMate: Text to SQL & CSV 💬🌐🗄️")
-# st.description('''Welcome to QueryMate, your friendly assistant for converting natural language queries into SQL statements and CSV outputs!
-#                Let's get started with your data queries!''')
-# # Load chat history
-# def load_chat_history():
-#     try:
-#         with open('chat_history.json', 'r') as f:
-#             return json.load(f)
-#     except FileNotFoundError:
-#         return []
-
-# def save_chat_history(history):
-#     with open('chat_history.json', 'w') as f:
-#         json.dump(history, f)
-
-# chat_history = load_chat_history()
-
-# # Data source selection
-# data_source = st.radio("Select Data Source:", ('SQL Database', 'Employee CSV'))
-
-# # Predefined queries
-# predefined_queries = {
-#     'SQL Database': [
-#         'Print all students',
-#         'Count total number of students',
-#         'List students in Data Science class'
-#     ],
-#     'Employee CSV': [
-#         'Print employees having the department id equal to 100',
-#         'Count total number of employees',
-#         'List Top 5 employees according to salary in descending order'
-#     ]
-# }
-
-# st.markdown(f"### Predefined Queries for {data_source}")
-
-# # Create buttons for predefined queries
-# for query in predefined_queries[data_source]:
-#     if st.button(query):
-#         st.session_state.predefined_query = query
-
-# st.markdown("### Enter Your Question")
-# question = st.text_input("Input: ", key="input", value=st.session_state.get('predefined_query', ''))
-
-# # Submit button
-# submit = st.button("Submit")
-
-# if submit:
-#     # Send request to FastAPI backend
-#     response = requests.post("http://localhost:8000/query", 
-#                              json={"question": question, "data_source": data_source})
-#     if response.status_code == 200:
-#         data = response.json()
-#         st.markdown(f"## Generated {'SQL' if data_source == 'SQL Database' else 'Pandas'} Query")
-#         st.code(data['query'])
-        
-#         st.markdown("## Query Results")
-#         st.write(data['result'])
-
-#         if data_source == 'Employee CSV':
-#             st.markdown("## Available CSV Columns")
-#             st.write(data['columns'])
-
-#         # Update chat history
-#         chat_history.append(f"User ({data_source}): {question}")
-#         chat_history.append(f"AI: {data['query']}")
-#         save_chat_history(chat_history)
-#     else:
-#         st.error(f"Error processing your request: {response.text}")
-
-#     # Clear the predefined query from session state
-#     st.session_state.pop('predefined_query', None)
-
-# # Display chat history
-# st.markdown("## Chat History")
-# for message in chat_history:
-#     st.text(message)
-
-# # Option to clear chat history
-# if st.button("Clear Chat History"):
-#     chat_history.clear()
-#     save_chat_history(chat_history)
-#     st.success("Chat history cleared!")
-
-
-
-
-
-import streamlit as st
-import requests
-import json
-import pandas as pd
-
-st.set_page_config(page_title="QueryMate: Text to SQL & CSV")
-
-st.markdown("# QueryMate: Text to SQL & CSV 💬🗄️")
-st.markdown('''Welcome to QueryMate, your friendly assistant for converting natural language queries into SQL statements and CSV outputs!
-               Let's get started with your data queries!''')
-
-# Load chat history
-def load_chat_history():
-    try:
-        with open('chat_history.json', 'r') as f:
-            return json.load(f)
-    except FileNotFoundError:
-        return []
-
-def save_chat_history(history):
-    with open('chat_history.json', 'w') as f:
-        json.dump(history, f)
-
-chat_history = load_chat_history()
-
-# Data source selection
-data_source = st.radio("Select Data Source:", ('SQL Database', 'Employee CSV'))
-
-# Predefined queries
-predefined_queries = {
-    'SQL Database': [
-        'Print all students',
-        'Count total number of students',
-        'List students in Data Science class'
-    ],
-    'Employee CSV': [
-        'Print employees having the department id equal to 100',
-        'Count total number of employees',
-        'List Top 5 employees according to salary in descending order'
-    ]
-}
-
-st.markdown(f"### Predefined Queries for {data_source}")
-
-# Create buttons for predefined queries
-for query in predefined_queries[data_source]:
-    if st.button(query):
-        st.session_state.predefined_query = query
-
-st.markdown("### Enter Your Question")
-question = st.text_input("Input: ", key="input", value=st.session_state.get('predefined_query', ''))
-
-# Submit button
-submit = st.button("Submit")
-
-if submit:
-    # Send request to FastAPI backend
-    response = requests.post("http://localhost:8000/query", 
-                             json={"question": question, "data_source": data_source})
-    if response.status_code == 200:
-        data = response.json()
-        st.markdown(f"## Generated {'SQL' if data_source == 'SQL Database' else 'Pandas'} Query")
-        st.code(data['query'])
-        
-        st.markdown("## Query Results")
-        result = data['result']
-        
-        if isinstance(result, list) and len(result) > 0:
-            if isinstance(result[0], dict):
-                # For CSV queries that return a list of dictionaries
-                df = pd.DataFrame(result)
-                st.dataframe(df)
-            elif isinstance(result[0], list):
-                # For SQL queries that return a list of lists
-                df = pd.DataFrame(result)
-                st.dataframe(df)
-            else:
-                # For single column results
-                st.dataframe(pd.DataFrame(result, columns=['Result']))
-        elif isinstance(result, dict):
-            # For single row results
-            st.table(result)
-        else:
-            # For scalar results or empty results
-            st.write(result)
-
-        if data_source == 'Employee CSV':
-            st.markdown("## Available CSV Columns")
-            st.write(data['columns'])
-
-        # Update chat history
-        chat_history.append(f"👨‍💻({data_source}): {question}")
-        chat_history.append(f"🤖: {data['query']}")
-        save_chat_history(chat_history)
-    else:
-        st.error(f"Error processing your request: {response.text}")
-
-    # Clear the predefined query from session state
-    st.session_state.pop('predefined_query', None)
-
-# Display chat history
-st.markdown("## Chat History")
-for message in chat_history:
-    st.text(message)
-
-# Option to clear chat history
-if st.button("Clear Chat History"):
-    chat_history.clear()
-    save_chat_history(chat_history)
+# import streamlit as st
+# import requests
+# import json
+# import pandas as pd
+
+# st.set_page_config(page_title="QueryMate: Text to SQL & CSV")
+
+# st.markdown("# QueryMate: Text to SQL & CSV 💬🗄️")
+# st.markdown('''Welcome to QueryMate, your friendly assistant for converting natural language queries into SQL statements and CSV outputs!
+#                Let's get started with your data queries!''')
+
+# # Load chat history
+# def load_chat_history():
+#     try:
+#         with open('chat_history.json', 'r') as f:
+#             return json.load(f)
+#     except FileNotFoundError:
+#         return []
+
+# def save_chat_history(history):
+#     with open('chat_history.json', 'w') as f:
+#         json.dump(history, f)
+
+# chat_history = load_chat_history()
+
+# # Data source selection
+# data_source = st.radio("Select Data Source:", ('SQL Database', 'Employee CSV'))
+
+# # Predefined queries
+# predefined_queries = {
+#     'SQL Database': [
+#         'Print all students',
+#         'Count total number of students',
+#         'List students in Data Science class'
+#     ],
+#     'Employee CSV': [
+#         'Print employees having the department id equal to 100',
+#         'Count total number of employees',
+#         'List Top 5 employees according to salary in descending order'
+#     ]
+# }
+
+# st.markdown(f"### Predefined Queries for {data_source}")
+
+# # Create buttons for predefined queries
+# for query in predefined_queries[data_source]:
+#     if st.button(query):
+#         st.session_state.predefined_query = query
+
+# st.markdown("### Enter Your Question")
+# question = st.text_input("Input: ", key="input", value=st.session_state.get('predefined_query', ''))
+
+# # Submit button
+# submit = st.button("Submit")
+
+# if submit:
+#     # Send request to FastAPI backend
+#     response = requests.post("http://localhost:8000/query", 
+#                              json={"question": question, "data_source": data_source})
+#     if response.status_code == 200:
+#         data = response.json()
+#         st.markdown(f"## Generated {'SQL' if data_source == 'SQL Database' else 'Pandas'} Query")
+#         st.code(data['query'])
+        
+#         st.markdown("## Query Results")
+#         result = data['result']
+        
+#         if isinstance(result, list) and len(result) > 0:
+#             if isinstance(result[0], dict):
+#                 # For CSV queries that return a list of dictionaries
+#                 df = pd.DataFrame(result)
+#                 st.dataframe(df)
+#             elif isinstance(result[0], list):
+#                 # For SQL queries that return a list of lists
+#                 df = pd.DataFrame(result)
+#                 st.dataframe(df)
+#             else:
+#                 # For single column results
+#                 st.dataframe(pd.DataFrame(result, columns=['Result']))
+#         elif isinstance(result, dict):
+#             # For single row results
+#             st.table(result)
+#         else:
+#             # For scalar results or empty results
+#             st.write(result)
+
+#         if data_source == 'Employee CSV':
+#             st.markdown("## Available CSV Columns")
+#             st.write(data['columns'])
+
+#         # Update chat history
+#         chat_history.append(f"👨‍💻({data_source}): {question}")
+#         chat_history.append(f"🤖: {data['query']}")
+#         save_chat_history(chat_history)
+#     else:
+#         st.error(f"Error processing your request: {response.text}")
+
+#     # Clear the predefined query from session state
+#     st.session_state.pop('predefined_query', None)
+
+# # Display chat history
+# st.markdown("## Chat History")
+# for message in chat_history:
+#     st.text(message)
+
+# # Option to clear chat history
+# if st.button("Clear Chat History"):
+#     chat_history.clear()
+#     save_chat_history(chat_history)
+#     st.success("Chat history cleared!")
+
+
+
+
+
+import streamlit as st
+import requests
+import pandas as pd
+
+st.set_page_config(page_title="QueryMate: Text to SQL & CSV")
+
+st.markdown("# QueryMate: Text to SQL & CSV 💬🗄️")
+st.markdown('''Welcome to QueryMate, your friendly assistant for converting natural language queries into SQL statements and CSV outputs!
+               Let's get started with your data queries!''')
+
+# Initialize chat history in session state if it doesn't exist
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+
+# Data source selection
+data_source = st.radio("Select Data Source:", ('SQL Database', 'Employee CSV'))
+
+# Predefined queries
+predefined_queries = {
+    'SQL Database': [
+        'Print all students',
+        'Count total number of students',
+        'List students in Data Science class'
+    ],
+    'Employee CSV': [
+        'Print employees having the department id equal to 100',
+        'Count total number of employees',
+        'List Top 5 employees according to salary in descending order'
+    ]
+}
+
+st.markdown(f"### Predefined Queries for {data_source}")
+
+# Create buttons for predefined queries
+for query in predefined_queries[data_source]:
+    if st.button(query):
+        st.session_state.predefined_query = query
+
+st.markdown("### Enter Your Question")
+question = st.text_input("Input: ", key="input", value=st.session_state.get('predefined_query', ''))
+
+# Submit button
+submit = st.button("Submit")
+
+if submit:
+    # Send request to FastAPI backend
+    response = requests.post("http://localhost:8000/query", 
+                             json={"question": question, "data_source": data_source})
+    if response.status_code == 200:
+        data = response.json()
+        st.markdown(f"## Generated {'SQL' if data_source == 'SQL Database' else 'Pandas'} Query")
+        st.code(data['query'])
+        
+        st.markdown("## Query Results")
+        result = data['result']
+        
+        if isinstance(result, list) and len(result) > 0:
+            if isinstance(result[0], dict):
+                # For CSV queries that return a list of dictionaries
+                df = pd.DataFrame(result)
+                st.dataframe(df)
+            elif isinstance(result[0], list):
+                # For SQL queries that return a list of lists
+                df = pd.DataFrame(result)
+                st.dataframe(df)
+            else:
+                # For single column results
+                st.dataframe(pd.DataFrame(result, columns=['Result']))
+        elif isinstance(result, dict):
+            # For single row results
+            st.table(result)
+        else:
+            # For scalar results or empty results
+            st.write(result)
+
+        if data_source == 'Employee CSV':
+            st.markdown("## Available CSV Columns")
+            st.write(data['columns'])
+
+        # Update chat history in session state
+        st.session_state.chat_history.append(f"👨‍💻({data_source}): {question}")
+        st.session_state.chat_history.append(f"🤖: {data['query']}")
+    else:
+        st.error(f"Error processing your request: {response.text}")
+
+    # Clear the predefined query from session state
+    st.session_state.pop('predefined_query', None)
+
+# Display chat history
+st.markdown("## Chat History")
+for message in st.session_state.chat_history:
+    st.text(message)
+
+# Option to clear chat history
+if st.button("Clear Chat History"):
+    st.session_state.chat_history = []
     st.success("Chat history cleared!")