Create app.py

app.py

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+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from groq import Groq
+
+# Set up the API key (replace with your actual Groq API key)
+os.environ["GROQ_API_KEY"] = "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"
+
+# Groq client setup
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Function to generate traffic optimization strategies from Groq
+def generate_traffic_optimization(data: str):
+    response = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": f"Generate a traffic flow optimization strategy for the following data: {data}",
+            }
+        ],
+        model="llama3-8b-8192",  # You can choose a different model if necessary
+    )
+    return response.choices[0].message.content
+
+# Visualization function to generate the traffic flow chart
+def generate_traffic_chart(df_filtered):
+    # Summarize traffic data by aggregating vehicle counts for each record
+    df_filtered['Total'] = df_filtered['CarCount'] + df_filtered['BikeCount'] + df_filtered['BusCount'] + df_filtered['TruckCount']
+
+    # Select relevant columns for the optimization request (you can adjust this as needed)
+    traffic_data_summary = df_filtered[['Time', 'Total', 'Traffic Situation', 'CarCount', 'BikeCount', 'BusCount', 'TruckCount']]
+
+    # Convert the summary to a string format for input to the Groq API
+    summary_str = traffic_data_summary.head(10).to_string(index=False)
+
+    # Get the optimization strategy from Groq
+    optimization_strategy = generate_traffic_optimization(summary_str)
+
+    # Visualization of traffic flow data
+    time_labels = df_filtered['Time'].head(10)
+    car_counts = df_filtered['CarCount'].head(10)
+    bike_counts = df_filtered['BikeCount'].head(10)
+    bus_counts = df_filtered['BusCount'].head(10)
+    truck_counts = df_filtered['TruckCount'].head(10)
+
+    # Create the stacked bar chart for vehicle counts
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.bar(time_labels, car_counts, label='Cars', color='blue')
+    ax.bar(time_labels, bike_counts, bottom=car_counts, label='Bikes', color='green')
+    ax.bar(time_labels, bus_counts, bottom=np.array(car_counts) + np.array(bike_counts), label='Buses', color='red')
+    ax.bar(time_labels, truck_counts, bottom=np.array(car_counts) + np.array(bike_counts) + np.array(bus_counts), label='Trucks', color='yellow')
+
+    # Customize chart
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Vehicle Count')
+    ax.set_title('Traffic Flow by Vehicle Type')
+    ax.legend()
+
+    # Save the plot as a file
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    chart_path = "/tmp/traffic_chart.png"
+    plt.savefig(chart_path)
+    plt.close()
+
+    return optimization_strategy, chart_path
+
+# Function to process the uploaded file and run traffic optimization
+def process_traffic_file(file):
+    # Load the dataset
+    df = pd.read_csv(file.name)
+    
+    # Optionally, you may filter data for specific days or time intervals
+    # For example, let's filter the data for a specific day:
+    df_filtered = df[df['Day of the week'] == 'Monday']
+
+    # Generate traffic chart and optimization strategy
+    optimization_strategy, chart_path = generate_traffic_chart(df_filtered)
+
+    return optimization_strategy, chart_path
+
+# Gradio interface
+iface = gr.Interface(
+    fn=process_traffic_file,
+    inputs=gr.File(label="Upload CSV with Traffic Data"),
+    outputs=[gr.Textbox(label="Optimization Strategy"), gr.Image(label="Traffic Flow Chart")],
+    title="Traffic Flow Optimization",
+    description="Upload a CSV file with traffic data, and the app will generate traffic optimization strategies and visualize the traffic flow."
+)
+
+# Launch the app
+iface.launch()