app.py

import gradio as gr
import pandas as pd
import random
from datetime import datetime
from transformers import pipeline

# Load a Hugging Face model for sentiment analysis (can be adapted for other tasks)
sentiment_analyzer = pipeline("sentiment-analysis")

# Simulated functions for generating data
def get_sensor_data():
    return {
        "Temperature (°C)": round(random.uniform(20, 35), 2),
        "Humidity (%)": round(random.uniform(30, 70), 2),
        "Motion Detected": random.choice(["Yes", "No"]),
    }

def get_geo_tag_data():
    return {
        "Device ID": f"Geo-{random.randint(1000, 9999)}",
        "Latitude": round(random.uniform(-90.0, 90.0), 6),
        "Longitude": round(random.uniform(-180.0, 180.0), 6),
        "Last Updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
    }

def get_rfid_data():
    return {
        "Tag ID": f"RFID-{random.randint(1000, 9999)}",
        "Location": random.choice(["Entry Gate", "Exit Gate", "Warehouse"]),
        "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
    }

# Sentiment analysis for logging data
def analyze_data(data):
    text_input = f"Sensor Data: {data['Temperature (°C)']}°C, {data['Humidity (%)']}% Humidity. Motion Detected: {data['Motion Detected']}."
    sentiment = sentiment_analyzer(text_input)
    return sentiment[0]['label'], sentiment[0]['score']

# Combine all data sources
def get_all_data():
    sensor_data = get_sensor_data()
    geo_tag_data = get_geo_tag_data()
    rfid_data = get_rfid_data()
    
    sentiment_label, sentiment_score = analyze_data(sensor_data)
    
    data = {
        "Temperature (°C)": sensor_data["Temperature (°C)"],
        "Humidity (%)": sensor_data["Humidity (%)"],
        "Motion Detected": sensor_data["Motion Detected"],
        "Geo Device ID": geo_tag_data["Device ID"],
        "Latitude": geo_tag_data["Latitude"],
        "Longitude": geo_tag_data["Longitude"],
        "Geo Last Updated": geo_tag_data["Last Updated"],
        "RFID Tag ID": rfid_data["Tag ID"],
        "RFID Location": rfid_data["Location"],
        "RFID Timestamp": rfid_data["Timestamp"],
        "Sentiment": sentiment_label,
        "Confidence": round(sentiment_score, 2),
    }
    return pd.DataFrame([data])

# Function to refresh the dashboard
def update_dashboard():
    return get_all_data()

# Gradio interface
with gr.Blocks() as dashboard:
    gr.Markdown("# 📊 Real-Time Monitoring Dashboard with Hugging Face Integration")
    gr.Markdown("This dashboard tracks sensor data, geo-tag locations, and RFID activities with real-time sentiment analysis of logs.")

    data_view = gr.Dataframe(
        headers=[
            "Temperature (°C)", "Humidity (%)", "Motion Detected",
            "Geo Device ID", "Latitude", "Longitude", "Geo Last Updated",
            "RFID Tag ID", "RFID Location", "RFID Timestamp", "Sentiment", "Confidence"
        ],
        row_count=5,
        interactive=False
    )

    refresh_button = gr.Button("Refresh Data")
    refresh_button.click(update_dashboard, outputs=data_view)

dashboard.launch()