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import streamlit as st
import pandas as pd
import os
from datetime import datetime
import google.generativeai as genai
# Configure Gemini
GEMINI_API_KEY = os.environ.get('GEMINI_API_KEY')
genai.configure(api_key=GEMINI_API_KEY)
model = genai.GenerativeModel('gemini-pro')
class GeminiDataChatbot:
def __init__(self):
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_interface(self):
st.title("📊 Data Analysis Chatbot")
st.write("Upload your CSV file and ask questions about your data")
# 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):
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 prompt
initial_prompt = f"""
I have uploaded a dataset with {len(df)} rows and {len(df.columns)} columns.
Columns: {', '.join(df.columns)}.
First give a very brief (2-3 sentence) overview of what this data might contain.
Then suggest 3 specific questions I could ask about this data.
"""
with st.spinner("Analyzing your data..."):
response = self._generate_gemini_response(initial_prompt, df)
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):
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..."):
# 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_gemini_response(prompt, st.session_state.uploaded_df)
st.markdown(response)
# Add assistant response to chat history
st.session_state.chat_history.append({"role": "assistant", "content": response})
def _generate_gemini_response(self, prompt: str, df: pd.DataFrame) -> str:
"""Generate response using Gemini API with data context"""
try:
# Create data summary for context
data_summary = f"""
Data Summary:
- Shape: {df.shape}
- Columns: {', '.join(df.columns)}
- First 5 rows:
{df.head().to_markdown()}
"""
# Create prompt with context
full_prompt = f"""
You are a data analysis assistant. The user has uploaded a dataset with the following characteristics:
{data_summary}
User Question: {prompt}
Provide a detailed response answering their question about the data. If appropriate, include:
- Relevant statistics
- Potential visualizations that would help
- Any data quality issues to consider
- Business insights if applicable
"""
response = model.generate_content(full_prompt)
return response.text
except Exception as e:
return f"Sorry, I encountered an error processing your request: {str(e)}"
# Initialize and run the chatbot
if __name__ == "__main__":
chatbot = GeminiDataChatbot()
chatbot.render_interface() |