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import streamlit as st
import pandas as pd
import os
from datetime import datetime
try:
import google.generativeai as genai
GEMINI_AVAILABLE = True
except ImportError:
GEMINI_AVAILABLE = False
class ChatbotManager:
def __init__(self):
if GEMINI_AVAILABLE and 'GEMINI_API_KEY' in os.environ:
genai.configure(api_key=os.environ['GEMINI_API_KEY'])
self.model = genai.GenerativeModel('gemini-pro')
else:
self.model = None
if 'uploaded_df' not in st.session_state:
st.session_state.uploaded_df = None
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
def render_chat_interface(self):
"""Render the main chat interface"""
st.header("📊 Data Analysis Chatbot")
if not GEMINI_AVAILABLE:
st.warning("Gemini API not available - running in limited mode")
# File upload section
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
if uploaded_file is not None:
self._process_uploaded_file(uploaded_file)
# Chat interface
if st.session_state.uploaded_df is not None:
self._render_chat_window()
def _process_uploaded_file(self, uploaded_file):
"""Process the uploaded CSV file"""
try:
df = pd.read_csv(uploaded_file)
st.session_state.uploaded_df = df
st.success("Data successfully loaded!")
with st.expander("View Data Preview"):
st.dataframe(df.head())
# Initial analysis
if self.model:
initial_prompt = f"Briefly describe this dataset with {len(df)} rows and {len(df.columns)} columns."
response = self._generate_response(initial_prompt)
st.session_state.chat_history.append({
"role": "assistant",
"content": response
})
except Exception as e:
st.error(f"Error processing file: {str(e)}")
def _render_chat_window(self):
"""Render the chat conversation window"""
st.subheader("Chat About Your Data")
# Display chat history
for message in st.session_state.chat_history:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# User input
if prompt := st.chat_input("Ask about your data..."):
self._handle_user_input(prompt)
def _handle_user_input(self, prompt):
"""Handle user input and generate response"""
# Add user message to chat history
st.session_state.chat_history.append({"role": "user", "content": prompt})
# Display user message
with st.chat_message("user"):
st.markdown(prompt)
# Generate and display assistant response
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
response = self._generate_response(prompt)
st.markdown(response)
# Add assistant response to chat history
st.session_state.chat_history.append({"role": "assistant", "content": response})
def _generate_response(self, prompt: str) -> str:
"""Generate response using available backend"""
df = st.session_state.uploaded_df
if self.model:
# Use Gemini if available
try:
data_summary = f"Data: {len(df)} rows, columns: {', '.join(df.columns)}"
full_prompt = f"{data_summary}\n\nUser question: {prompt}"
response = self.model.generate_content(full_prompt)
return response.text
except Exception as e:
return f"Gemini error: {str(e)}"
else:
# Fallback basic analysis
if "summary" in prompt.lower():
return f"Basic summary:\n{df.describe().to_markdown()}"
elif "columns" in prompt.lower():
return f"Columns: {', '.join(df.columns)}"
else:
return "I can provide basic info about columns and summary statistics." |