app.py

import os
os.system('pip install matplotlib')
import pandas as pd
import matplotlib.pyplot as plt
import gradio as gr
from groq import Groq

GROQ_API_KEY = "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"
client = Groq(api_key=GROQ_API_KEY)

def analyze_traffic(file):
    # Load CSV file
    traffic_data = pd.read_csv(file.name)

    # Add a 'TotalVehicles' column to sum up the total number of vehicles per row
    traffic_data['TotalVehicles'] = traffic_data['CarCount'] + traffic_data['BikeCount'] + traffic_data['BusCount'] + traffic_data['TruckCount']

    # Group by 'Day of the week' and calculate the total number of vehicles for each day
    daywise_traffic = traffic_data.groupby('Day of the week')['TotalVehicles'].sum().reset_index()

    # Sort by 'Day of the week' to display in order (assuming day names are in order)
    day_of_week_order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
    daywise_traffic['Day of the week'] = pd.Categorical(daywise_traffic['Day of the week'], categories=day_of_week_order, ordered=True)
    daywise_traffic = daywise_traffic.sort_values('Day of the week')

    # Plot the total number of vehicles for each day of the week
    plt.figure(figsize=(10, 6))
    plt.bar(daywise_traffic['Day of the week'], daywise_traffic['TotalVehicles'], color='skyblue', alpha=0.7)
    plt.title("Total Vehicle Count by Day of the Week")
    plt.xlabel("Day of the Week")
    plt.ylabel("Total Number of Vehicles")
    plt.xticks(rotation=45)
    plt.grid(True)
    plt.tight_layout()

    # Save the plot to an image file
    plot_path = "traffic_plot.png"
    plt.savefig(plot_path)
    plt.close()

    # Generate summary statistics
    summary = traffic_data.describe()

    # Prepare RAG prompt with detailed questions for improvement suggestions
    prompt = f"""
    Based on the following traffic data and the summary statistics, provide realistic suggestions for improving traffic flow and reducing congestion:

    1. What strategies can be implemented to reduce peak-hour traffic congestion, especially on days like Friday and Monday, based on the 'TotalVehicles' counts for those days?
    2. How can the number of vehicles for each type (Car, Bike, Bus, Truck) be optimized to create smoother traffic flow and better distribution across different types of vehicles?
    3. Suggest traffic management techniques, such as signal timings, road expansions, or public transport incentives, that could be employed based on traffic volume trends.
    4. What other factors (e.g., weather, special events, public holidays) should be considered to improve the flow, and how can this data be integrated into the model?

    Summary statistics of the traffic data:
    {summary.to_string()}

    Sample Data:
    {traffic_data.head().to_string()}
    """

    # Use Groq API to generate strategies
    chat_completion = client.chat.completions.create(
        messages=[{"role": "user", "content": prompt}],
        model="llama3-8b-8192"
    )

    # Extract and return Groq's response and the plot image path
    optimization_suggestions = chat_completion.choices[0].message.content
    return optimization_suggestions, plot_path


# Gradio interface
iface = gr.Interface(
    fn=analyze_traffic,
    inputs=gr.inputs.File(label="Upload Traffic Data CSV"),
    outputs=[gr.outputs.Textbox(label="Optimization Suggestions"), gr.outputs.Image(label="Traffic Data Plot")],
    title="Traffic Flow Optimization",
    description="Upload your traffic data (CSV file) to analyze and get traffic optimization suggestions based on real-time data. The analysis includes traffic flow optimization recommendations and a plot of vehicle counts by day of the week."
)

# Launch Gradio interface
iface.launch()