import gradio as gr
import pandas as pd
from datetime import datetime
import json
from transformers import pipeline
import logging
import os
import plotly.express as px
# Configure logging for debugging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# Load Hugging Face summarization model
try:
logging.info("Attempting to load Hugging Face model...")
summarizer = pipeline("text2text-generation", model="google/flan-t5-base")
logging.info("Hugging Face model loaded successfully")
except Exception as e:
logging.error(f"Failed to load model: {str(e)}")
raise e
# Format summary prompt and generate report
def summarize_logs(df, lab_name, start_date, end_date):
try:
total_devices = df["device_id"].nunique()
avg_uptime = "97%" # Placeholder
most_used = df.groupby("device_id")["usage_hours"].sum().idxmax() if not df.empty else "N/A"
downtime_events = 3 # Placeholder
prompt = (
f"Summarize maintenance and usage logs for lab {lab_name} "
f"from {start_date} to {end_date}. "
f"There were {total_devices} devices. "
f"The most used device was {most_used}."
)
summary = summarizer(prompt, max_length=200, do_sample=False)[0]["generated_text"]
logging.info("Summary generated successfully")
return summary
except Exception as e:
logging.error(f"Summary generation failed: {str(e)}")
return "Failed to generate summary."
# Create a bar chart for usage hours per device
def create_usage_chart(df):
try:
usage_data = df.groupby("device_id")["usage_hours"].sum().reset_index()
fig = px.bar(
usage_data,
x="device_id",
y="usage_hours",
title="Usage Hours per Device",
labels={"device_id": "Device ID", "usage_hours": "Usage Hours"},
color="usage_hours",
color_continuous_scale="Blues"
)
fig.update_layout(
title_font_size=16,
margin=dict(l=20, r=20, t=40, b=20),
plot_bgcolor="white",
paper_bgcolor="white",
font=dict(size=12)
)
return fig
except Exception as e:
logging.error(f"Failed to create usage chart: {str(e)}")
return None
# Main Gradio function
def process_logs(file_obj, lab_site, start_date, end_date):
try:
if file_obj is None:
logging.warning("No file uploaded, returning empty results")
return "No file uploaded.", "No data to preview.", None
# Read file based on extension
file_name = file_obj.name if hasattr(file_obj, 'name') else file_obj
logging.info(f"Processing file: {file_name}")
if file_name.endswith(".json"):
df = pd.read_json(file_name)
elif file_name.endswith(".csv"):
df = pd.read_csv(file_name)
else:
logging.error("Unsupported file format")
return "Unsupported file format. Please upload a CSV or JSON file.", None, None
logging.info(f"File loaded successfully with {len(df)} rows")
# Convert timestamp to datetime and filter by date range
try:
df["timestamp"] = pd.to_datetime(df["timestamp"])
start_date = pd.to_datetime(start_date)
end_date = pd.to_datetime(end_date)
df = df[(df["timestamp"] >= start_date) & (df["timestamp"] <= end_date)]
logging.info(f"Filtered to {len(df)} rows within date range {start_date} to {end_date}")
except Exception as e:
logging.error(f"Date filtering failed: {str(e)}")
return f"Failed to filter data by date: {str(e)}", None, None
if df.empty:
logging.warning("No data within the specified date range")
return "No data available for the specified date range.", "No data to preview.", None
summary = summarize_logs(df, lab_site, start_date, end_date)
preview = df.head().to_markdown() if not df.empty else "No data available."
chart = create_usage_chart(df)
return summary, preview, chart
except Exception as e:
logging.error(f"Failed to process file: {str(e)}")
return f"Failed to process file: {str(e)}", None, None
# Gradio Interface with Dashboard Layout
try:
logging.info("Initializing Gradio Blocks interface...")
with gr.Blocks(css="""
.dashboard-container {border: 1px solid #e0e0e0; padding: 10px; border-radius: 5px; background-color: #f9f9f9;}
.dashboard-title {font-size: 24px; font-weight: bold; margin-bottom: 10px;}
.dashboard-section {margin-bottom: 15px;}
.dashboard-section h3 {font-size: 18px; margin-bottom: 5px;}
""") as iface:
gr.Markdown("LabOps Log Analyzer Dashboard (Hugging Face AI)
")
gr.Markdown("Upload a CSV or JSON file containing lab equipment logs to analyze usage.")
with gr.Row():
with gr.Column(scale=1):
file_input = gr.File(label="Upload Logs (CSV or JSON)", file_types=[".csv", ".json"])
lab_site_input = gr.Textbox(label="Lab Site", placeholder="e.g., Lab A")
start_date_input = gr.Textbox(label="Start Date (YYYY-MM-DD)", placeholder="e.g., 2025-01-01")
end_date_input = gr.Textbox(label="End Date (YYYY-MM-DD)", placeholder="e.g., 2025-01-31")
submit_button = gr.Button("Submit", variant="primary")
with gr.Column(scale=2):
with gr.Group(elem_classes="dashboard-container"):
gr.Markdown("Analysis Dashboard
")
with gr.Row():
with gr.Column(scale=1):
with gr.Group(elem_classes="dashboard-section"):
gr.Markdown("### Summary Report")
summary_output = gr.Textbox(lines=5)
with gr.Row():
with gr.Column(scale=1):
with gr.Group(elem_classes="dashboard-section"):
gr.Markdown("### Usage Chart")
chart_output = gr.Plot()
with gr.Column(scale=1):
with gr.Group(elem_classes="dashboard-section"):
gr.Markdown("### Log Preview")
preview_output = gr.Markdown()
submit_button.click(
fn=process_logs,
inputs=[file_input, lab_site_input, start_date_input, end_date_input],
outputs=[summary_output, preview_output, chart_output]
)
logging.info("Gradio interface initialized successfully")
except Exception as e:
logging.error(f"Failed to initialize Gradio interface: {str(e)}")
raise e
if __name__ == "__main__":
try:
logging.info("Launching Gradio interface...")
iface.launch(server_name="0.0.0.0", server_port=7860, debug=True, share=False)
logging.info("Gradio interface launched successfully")
except Exception as e:
logging.error(f"Failed to launch Gradio interface: {str(e)}")
print(f"Error launching app: {str(e)}")
raise e