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import streamlit as st
import pandas as pd
import google.generativeai as genai
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
# Set up page layout
st.set_page_config(page_title="AI CSV Data Analyst", layout="wide")
# Initialize Gemini API (Replace with your API Key)
GEMINI_API_KEY = "AIzaSyDs-vtZBkyLmEUH5NgkfUNkMJ8kxg_pR3Y"
genai.configure(api_key=GEMINI_API_KEY)
# Create two columns (70:30 split)
left_col, right_col = st.columns([7, 3])
with left_col:
st.title("📊 AI-Powered Data Analyst")
# File Upload
uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])
if uploaded_file is not None:
# Read File
file_ext = uploaded_file.name.split(".")[-1]
if file_ext == "csv":
df = pd.read_csv(uploaded_file)
elif file_ext == "xlsx":
df = pd.read_excel(uploaded_file, engine="openpyxl")
# Display the DataFrame
st.subheader("📂 Uploaded Data")
st.dataframe(df)
# Data Insights
st.subheader("📈 Data Insights")
# Dataset Summary
st.write(f"**Rows:** {df.shape[0]}, **Columns:** {df.shape[1]}")
st.write(f"**Missing Values:**\n{df.isnull().sum()}")
# Basic Statistics
st.subheader("📊 Statistical Summary")
st.write(df.describe())
# Visualizations
st.subheader("📉 Data Visualizations")
# Select Column for Histogram
numeric_columns = df.select_dtypes(include=["number"]).columns
if len(numeric_columns) > 0:
col = st.selectbox("Select a column for histogram:", numeric_columns)
fig = px.histogram(df, x=col, title=f"Histogram of {col}")
st.plotly_chart(fig)
# Correlation Heatmap
if len(numeric_columns) > 1:
st.subheader("🔍 Correlation Heatmap")
fig, ax = plt.subplots(figsize=(6, 4))
sns.heatmap(df[numeric_columns].corr(), annot=True, cmap="coolwarm", ax=ax)
st.pyplot(fig)
with right_col:
st.subheader("💬 Chat with Your Data")
user_query = st.text_input("Ask a question about the data...")
if user_query and uploaded_file is not None:
# Prepare prompt for AI
prompt = f"""
You are a data analyst. The user has uploaded a dataset.
Answer the query based on the dataset provided.
Dataset Overview:
{df.describe().to_string()}
User Question:
{user_query}
"""
# Get AI response
model = genai.GenerativeModel("gemini-pro")
response = model.generate_content(prompt)
# Display AI Response
if response.text:
st.write("🤖 AI Response:")
st.write(response.text)
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