File size: 2,305 Bytes
cfd55bb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import streamlit as st
import pandas as pd
import openai
import matplotlib.pyplot as plt
import seaborn as sns
import os
openai.api_key = os.getenv("openapikey")
def generate_insight(df, question):
"""Generates insights using an LLM."""
prompt = f"Given the following dataset (first 5 rows):\n{df.head().to_string()}\n\nQuestion: {question}\n\nAnswer:"
try:
response = openai.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}],
max_tokens=300,
)
return response.choices[0].message.content.strip()
except Exception as e:
return f"Error: {e}"
def generate_visualization(df, column_x, column_y=None, vis_type="hist"):
"""Generates a visualization."""
try:
plt.figure(figsize=(10, 6))
if vis_type == "hist":
sns.histplot(df[column_x])
elif vis_type == "scatter" and column_y:
sns.scatterplot(x=column_x, y=column_y, data=df)
else:
return "Invalid visualization request."
st.pyplot(plt)
return "Visualization generated."
except Exception as e:
return f"Error generating visualization: {e}"
st.title("Data Exploration and Insight Generation")
uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
if uploaded_file:
df = pd.read_csv(uploaded_file)
st.write("### Dataset Preview")
st.dataframe(df.head())
st.write("### Dataset Information")
st.write(f"Number of rows: {df.shape[0]}")
st.write(f"Number of columns: {df.shape[1]}")
st.write(f"Column names: {', '.join(df.columns)}")
question = st.text_input("Ask a question about the data:")
if question:
insight = generate_insight(df, question)
st.write("### Generated Insight")
st.write(insight)
st.write("### Data Visualization")
col_x = st.selectbox("Select X-axis column", df.columns)
col_y = st.selectbox("Select Y-axis column (for scatter plot)", [None] + list(df.columns))
vis_type = st.selectbox("Select visualization type", ["hist", "scatter"])
if st.button("Generate Visualization"):
vis_result = generate_visualization(df, col_x, col_y if vis_type == "scatter" else None, vis_type)
st.write(vis_result) |