| 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) |
