| import shiny |
| from shiny import App, ui, reactive |
| import pandas as pd |
| import matplotlib.pyplot as plt |
| import ollama |
| import io |
| from shinywidgets import render_plotly |
| import plotly.express as px |
| import os |
|
|
| |
| os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib" |
|
|
| |
| app_ui = ui.page_fluid( |
| ui.panel_title("LLM-Powered Data Analytics and Visualization"), |
| ui.layout_sidebar( |
| ui.sidebar( |
| ui.input_file("file", "Upload CSV/Parquet", accept=[".csv", ".parquet"]), |
| ui.input_text("question", "Ask about the data:"), |
| ui.input_action_button("analyze", "Analyze & Visualize"), |
| ), |
| ui.layout_columns( |
| ui.card( |
| ui.output_text("llm_response") |
| ), |
| ui.card( |
| ui.output_plot("plot") |
| ) |
| ) |
| ) |
| ) |
|
|
| |
| def server(input, output, session): |
| data = reactive.Value(None) |
| |
| @reactive.effect |
| @reactive.event(input.file) |
| def load_data(): |
| file_info = input.file() |
| if file_info is not None: |
| ext = file_info["name"].split(".")[-1] |
| if ext == "csv": |
| df = pd.read_csv(file_info["datapath"]) |
| elif ext == "parquet": |
| df = pd.read_parquet(file_info["datapath"]) |
| data.set(df) |
| |
| @output |
| @render.text |
| @reactive.event(input.analyze) |
| def llm_response(): |
| df = data.get() |
| if df is None: |
| return "Please upload a dataset." |
| question = input.question() |
| prompt = f"Dataset: {df.head().to_string()}\nUser Question: {question}\nProvide insights based on the data." |
| response = ollama.chat(model="mistral", messages=[{"role": "user", "content": prompt}]) |
| return response["message"]["content"] |
| |
| @output |
| @render_plotly |
| @reactive.event(input.analyze) |
| def plot(): |
| df = data.get() |
| if df is None: |
| return None |
| if len(df.columns) < 2: |
| return None |
| fig = px.scatter(df, x=df.columns[0], y=df.columns[1]) |
| return fig |
|
|
| app = App(app_ui, server) |
|
|
