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import gradio as gr
import pandas as pd
import plotly.express as px
from groq import Groq
import os
# ---------------- CONFIG ----------------
client = Groq(api_key=os.getenv("GROQ_API_KEY"))
# ---------------- CORE FUNCTION ----------------
def analyze_health_data(file):
if file is None:
return "Please upload a CSV file.", None, None
# Read CSV file
try:
df = pd.read_csv(file.name)
except Exception as e:
return f"Error reading CSV: {e}", None, None
# Drop completely empty columns
df = df.dropna(axis=1, how="all")
# Detect numeric columns for analysis
numeric_cols = df.select_dtypes(include=["number"]).columns.tolist()
if len(numeric_cols) < 2:
return "Please upload a dataset with at least two numeric columns.", None, None
# Try to find a date/time column
date_cols = [c for c in df.columns if "date" in c.lower() or "time" in c.lower()]
x_col = date_cols[0] if date_cols else df.columns[0]
# Convert potential date column
try:
df[x_col] = pd.to_datetime(df[x_col], errors="ignore")
except Exception:
pass
# ---------------- VISUALIZATIONS ----------------
# First chart — all numeric columns line plot
fig1 = px.line(
df,
x=x_col,
y=numeric_cols,
title="Health Metrics Over Time",
markers=True,
labels={"value": "Metric Value", "variable": "Health Metric"},
)
# Second chart — correlation heatmap (if >2 numeric cols)
if len(numeric_cols) >= 2:
corr = df[numeric_cols].corr()
fig2 = px.imshow(
corr,
text_auto=True,
title="Correlation Between Health Metrics",
color_continuous_scale="Blues",
)
else:
fig2 = None
# ---------------- SUMMARY STATS ----------------
summary = df.describe(include="all").to_string()
# ---------------- GENERATE AI INSIGHTS ----------------
prompt = f"""
You are an expert digital health assistant.
The user uploaded the following dataset summary:
{summary}
Identify 3 interesting insights or patterns (e.g., trends, correlations),
and 2 practical recommendations that could help improve their health behavior,
based solely on the numerical data provided.
"""
try:
response = client.chat.completions.create(
model="deepseek-r1-distill-llama-70b",
messages=[{"role": "user", "content": prompt}],
temperature=0.7,
)
insights = response.choices[0].message.content.strip()
except Exception as e:
insights = f"Error generating AI insights: {e}"
return insights, fig1, fig2
# ---------------- GRADIO INTERFACE ----------------
title = "🏥 HealthMind AI — Universal Health Data Insight Dashboard"
desc = (
"Upload any health-related CSV file. "
"The app will automatically detect numeric columns, visualize trends, "
"and generate AI-driven insights about your data."
)
demo = gr.Interface(
fn=analyze_health_data,
inputs=[gr.File(label="Upload Health Data (CSV)", file_types=[".csv"])],
outputs=[
gr.Textbox(label="AI Health Insights", lines=10),
gr.Plot(label="Metric Trends"),
gr.Plot(label="Correlation Heatmap"),
],
title=title,
description=desc
)
if __name__ == "__main__":
demo.launch() |