Update app.py

app.py

@@ -11,6 +11,18 @@ import json
 import os
 import sqlite3
 from datetime import datetime
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+import io
+from datetime import datetime
+import base64
+from PIL import Image
+
+# Import the DataAnalysisChatbot class
+from paste import DataAnalysisChatbot
 
 class DataAnalysisChatbot:
     def __init__(self):
@@ -1275,6 +1287,291 @@ class DataAnalysisChatbot:
         help_text += "  correlate temperature humidity pressure\n"
         help_text += "  visualize scatter temperature humidity\n"
         help_text += "  trend sales date\n"
+
+
+# Page configuration
+st.set_page_config(
+    page_title="Data Analysis Assistant",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Initialize session state variables if they don't exist
+if 'chatbot' not in st.session_state:
+    st.session_state.chatbot = DataAnalysisChatbot()
+if 'conversation' not in st.session_state:
+    st.session_state.conversation = []
+if 'data_loaded' not in st.session_state:
+    st.session_state.data_loaded = False
+if 'current_file' not in st.session_state:
+    st.session_state.current_file = None
+if 'data_preview' not in st.session_state:
+    st.session_state.data_preview = None
+
+# Function to get a download link for a file
+def get_download_link(file_path, link_text):
+    with open(file_path, 'rb') as f:
+        data = f.read()
+    b64 = base64.b64encode(data).decode()
+    href = f'<a href="data:file/txt;base64,{b64}" download="{os.path.basename(file_path)}">{link_text}</a>'
+    return href
+
+# Function to convert matplotlib figure to Streamlit-compatible format
+def plt_to_streamlit():
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    return buf
+
+# Custom CSS
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        font-weight: 700;
+        color: #1E88E5;
+        margin-bottom: 1rem;
+    }
+    .sub-header {
+        font-size: 1.5rem;
+        font-weight: 600;
+        color: #333;
+        margin-bottom: 1rem;
+    }
+    .chat-user {
+        background-color: #E3F2FD;
+        padding: 10px 15px;
+        border-radius: 15px;
+        margin-bottom: 10px;
+        font-size: 1rem;
+    }
+    .chat-bot {
+        background-color: #F5F5F5;
+        padding: 10px 15px;
+        border-radius: 15px;
+        margin-bottom: 10px;
+        font-size: 1rem;
+    }
+    .file-info {
+        padding: 10px;
+        background-color: #E8F5E9;
+        border-radius: 5px;
+        margin-bottom: 10px;
+    }
+    .sidebar-content {
+        padding: 10px;
+    }
+    .highlight-text {
+        color: #1E88E5;
+        font-weight: bold;
+    }
+    .stButton>button {
+        width: 100%;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Sidebar for data loading and information
+with st.sidebar:
+    st.markdown('<div class="sidebar-content">', unsafe_allow_html=True)
+    st.markdown('<p class="sub-header">📁 Data Loading</p>', unsafe_allow_html=True)
+    
+    # File uploader
+    uploaded_file = st.file_uploader("Upload your data file", type=['csv', 'xlsx', 'json', 'db', 'sqlite'])
+    
+    # Load data button (only show if file is uploaded)
+    if uploaded_file is not None:
+        file_type = uploaded_file.name.split('.')[-1].lower()
+        
+        # Save the uploaded file to a temporary location
+        temp_file_path = f"temp_upload_{datetime.now().strftime('%Y%m%d%H%M%S')}.{file_type}"
+        with open(temp_file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        
+        # Load data based on file type
+        if st.button("Load Data"):
+            try:
+                if file_type == 'csv':
+                    response = st.session_state.chatbot.process_query(f"load csv {temp_file_path}")
+                elif file_type in ['xlsx', 'xls']:
+                    response = st.session_state.chatbot.process_query(f"load excel {temp_file_path}")
+                elif file_type == 'json':
+                    response = st.session_state.chatbot.process_query(f"load json {temp_file_path}")
+                elif file_type in ['db', 'sqlite']:
+                    # For SQL databases, we need to prompt for a query
+                    st.session_state.current_file = temp_file_path
+                    st.session_state.data_loaded = False
+                    response = "SQL database loaded. Please enter a query in the main chat."
+                else:
+                    response = "Unsupported file format. Please upload CSV, Excel, JSON, or SQLite files."
+                
+                st.session_state.conversation.append({"role": "user", "message": f"Loading {uploaded_file.name}"})
+                st.session_state.conversation.append({"role": "bot", "message": response})
+                
+                if "Successfully loaded data" in response:
+                    st.session_state.data_loaded = True
+                    st.session_state.current_file = temp_file_path
+                    
+                    # Get data preview
+                    if st.session_state.chatbot.data is not None:
+                        st.session_state.data_preview = st.session_state.chatbot.data.head()
+            except Exception as e:
+                st.error(f"Error loading data: {str(e)}")
+    
+    # Display data information if data is loaded
+    if st.session_state.data_loaded and st.session_state.chatbot.data is not None:
+        st.markdown('<p class="sub-header">📊 Data Information</p>', unsafe_allow_html=True)
+        
+        # Display basic info
+        st.markdown('<div class="file-info">', unsafe_allow_html=True)
+        st.write(f"**Rows:** {len(st.session_state.chatbot.data)}")
+        st.write(f"**Columns:** {len(st.session_state.chatbot.data.columns)}")
+        st.write(f"**Data Source:** {st.session_state.chatbot.data_source}")
+        st.markdown('</div>', unsafe_allow_html=True)
+        
+        # Quick actions
+        st.markdown('<p class="sub-header">⚡ Quick Actions</p>', unsafe_allow_html=True)
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button("Describe Data"):
+                response = st.session_state.chatbot.process_query("describe")
+                st.session_state.conversation.append({"role": "user", "message": "Describe data"})
+                st.session_state.conversation.append({"role": "bot", "message": response})
+        
+        with col2:
+            if st.button("Check Missing"):
+                response = st.session_state.chatbot.process_query("missing")
+                st.session_state.conversation.append({"role": "user", "message": "Check missing values"})
+                st.session_state.conversation.append({"role": "bot", "message": response})
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button("Correlations"):
+                response = st.session_state.chatbot.process_query("correlate")
+                st.session_state.conversation.append({"role": "user", "message": "Show correlations"})
+                st.session_state.conversation.append({"role": "bot", "message": response})
+        
+        with col2:
+            if st.button("Generate Report"):
+                response = st.session_state.chatbot.process_query("report")
+                st.session_state.conversation.append({"role": "user", "message": "Generate report"})
+                st.session_state.conversation.append({"role": "bot", "message": response})
+                
+                # If report was generated, provide download link
+                if "Report generated and saved as" in response:
+                    report_filename = response.split("Report generated and saved as ")[-1].strip()
+                    st.markdown(
+                        get_download_link(report_filename, "📥 Download Report"),
+                        unsafe_allow_html=True
+                    )
+    
+    # Help section
+    st.markdown('<p class="sub-header">❓ Help</p>', unsafe_allow_html=True)
+    if st.button("Show Commands"):
+        response = st.session_state.chatbot.process_query("help")
+        st.session_state.conversation.append({"role": "user", "message": "Show available commands"})
+        st.session_state.conversation.append({"role": "bot", "message": response})
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# Main area
+st.markdown('<h1 class="main-header">📊 Data Analysis Assistant</h1>', unsafe_allow_html=True)
+
+# Show data preview if data is loaded
+if st.session_state.data_loaded and st.session_state.data_preview is not None:
+    st.markdown('<p class="sub-header">Data Preview</p>', unsafe_allow_html=True)
+    st.dataframe(st.session_state.data_preview, use_container_width=True)
+
+# Display conversation history
+st.markdown('<p class="sub-header">Chat History</p>', unsafe_allow_html=True)
+chat_container = st.container()
+
+with chat_container:
+    for message in st.session_state.conversation:
+        if message["role"] == "user":
+            st.markdown(f'<div class="chat-user">👤 <b>You:</b> {message["message"]}</div>', unsafe_allow_html=True)
+        else:
+            # Process bot messages for special content
+            bot_message = message["message"]
+            
+            # Check if it's a visualization result
+            if "Visualization created and saved as" in bot_message:
+                # Extract the filename and load the image
+                img_file = bot_message.split("Visualization created and saved as ")[-1].strip()
+                if os.path.exists(img_file):
+                    st.markdown(f'<div class="chat-bot">🤖 <b>Assistant:</b></div>', unsafe_allow_html=True)
+                    try:
+                        img = Image.open(img_file)
+                        st.image(img, caption="Generated Visualization", use_column_width=True)
+                    except Exception as e:
+                        st.error(f"Error displaying visualization: {str(e)}")
+                        st.markdown(f'<div class="chat-bot">🤖 <b>Assistant:</b> {bot_message}</div>', unsafe_allow_html=True)
+                else:
+                    st.markdown(f'<div class="chat-bot">🤖 <b>Assistant:</b> {bot_message}</div>', unsafe_allow_html=True)
+            
+            # Check if it's a report result
+            elif "Report generated and saved as" in bot_message:
+                report_filename = bot_message.split("Report generated and saved as ")[-1].strip()
+                st.markdown(
+                    f'<div class="chat-bot">🤖 <b>Assistant:</b> {bot_message}<br/>{get_download_link(report_filename, "📥 Download Report")}</div>',
+                    unsafe_allow_html=True
+                )
+            
+            # Regular message
+            else:
+                # Format code blocks
+                if "```" in bot_message:
+                    parts = bot_message.split("```")
+                    formatted_message = ""
+                    for i, part in enumerate(parts):
+                        if i % 2 == 0:  # Outside code block
+                            formatted_message += part
+                        else:  # Inside code block
+                            formatted_message += f"<pre style='background-color: #f0f0f0; padding: 10px; border-radius: 5px; overflow-x: auto;'>{part}</pre>"
+                    st.markdown(f'<div class="chat-bot">🤖 <b>Assistant:</b> {formatted_message}</div>', unsafe_allow_html=True)
+                else:
+                    st.markdown(f'<div class="chat-bot">🤖 <b>Assistant:</b> {bot_message}</div>', unsafe_allow_html=True)
+
+# User input
+st.markdown('<p class="sub-header">Ask a Question</p>', unsafe_allow_html=True)
+user_input = st.text_area("Enter your query", height=100, key="user_query")
+
+# Handle SQL query case
+if st.session_state.current_file is not None and not st.session_state.data_loaded and st.session_state.current_file.endswith(('db', 'sqlite')):
+    sql_query = st.text_area("Enter SQL query", height=100, key="sql_query")
+    if st.button("Run SQL Query") and sql_query:
+        response = st.session_state.chatbot.process_query(f"load sql {st.session_state.current_file} query {sql_query}")
+        st.session_state.conversation.append({"role": "user", "message": f"SQL query: {sql_query}"})
+        st.session_state.conversation.append({"role": "bot", "message": response})
+        
+        if "Successfully loaded data" in response:
+            st.session_state.data_loaded = True
+            if st.session_state.chatbot.data is not None:
+                st.session_state.data_preview = st.session_state.chatbot.data.head()
+
+# Submit button for regular queries
+if st.button("Submit") and user_input:
+    # Add user message to conversation
+    st.session_state.conversation.append({"role": "user", "message": user_input})
+    
+    # Process query
+    response = st.session_state.chatbot.process_query(user_input)
+    
+    # Add bot response to conversation
+    st.session_state.conversation.append({"role": "bot", "message": response})
+    
+    # Clear input
+    st.session_state.user_query = ""
+
+# Add warning for demo mode
+st.markdown("---")
+st.markdown("**Note:** File uploads and data processing are handled locally. Make sure you have the necessary dependencies installed.", unsafe_allow_html=True)
+
+# Footer
+st.markdown("---")
+st.markdown("© 2025 Data Analysis Assistant | Built with Streamlit")
         
         return help_text