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app.py

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+import os
+import pandas as pd
+import plotly.express as px
+from groq import Groq
+import gradio as gr
+
+
+GROQ_API_KEY ="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"
+client = Groq(api_key=GROQ_API_KEY)
+
+# Function to process the traffic dataset
+def process_traffic_data(csv_file):
+    data = pd.read_csv(csv_file)
+    peak_hours = data.groupby("Time")["Total"].sum().idxmax()
+    worst_situation = data.groupby("Traffic Situation")["Total"].sum().idxmax()
+    vehicle_contribution = (
+        data[["CarCount", "BikeCount", "BusCount", "TruckCount"]]
+        .sum()
+        .apply(lambda x: round((x / data["Total"].sum()) * 100, 2))
+    )
+    summary = (
+        f"Peak traffic hour: {peak_hours}. Worst traffic situation: {worst_situation}.\n"
+        f"Vehicle Contribution to Traffic:\n"
+        f"- Cars: {vehicle_contribution['CarCount']}%\n"
+        f"- Bikes: {vehicle_contribution['BikeCount']}%\n"
+        f"- Buses: {vehicle_contribution['BusCount']}%\n"
+        f"- Trucks: {vehicle_contribution['TruckCount']}%\n"
+    )
+    return data, summary
+
+# Function to generate suggestions using Groq API
+def generate_suggestions(summary):
+    input_content = (
+        f"Here is the detailed traffic data summary:\n{summary}"
+        f"Suggest realistic traffic optimization strategies with supporting stats."
+    )
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": input_content}],
+        model="llama3-8b-8192",
+    )
+    return chat_completion.choices[0].message.content
+
+# Function to create traffic visualization
+def create_visualization(data):
+    fig = px.bar(
+        data,
+        x="Day of the week",
+        y="Total",
+        color="Traffic Situation",
+        title="Total Traffic by Day and Traffic Situation",
+        labels={"Total": "Total Vehicles", "Day of the week": "Day"},
+    )
+    return fig.to_html()
+
+# Gradio interface function
+def analyze_traffic(file):
+    data, summary = process_traffic_data(file.name)
+    suggestions = generate_suggestions(summary)
+    visualization_html = create_visualization(data)
+    return summary, suggestions, visualization_html
+
+# Gradio interface definition
+with gr.Blocks() as demo:
+    gr.Markdown("# Traffic Optimization Application")
+    gr.Markdown(
+        "Upload a CSV file with traffic data to generate traffic flow optimization suggestions and visualize traffic patterns."
+    )
+
+    with gr.Row():
+        file_input = gr.File(label="Upload Traffic Data CSV")
+        analyze_button = gr.Button("Analyze Traffic Data")
+
+    with gr.Row():
+        summary_output = gr.Textbox(label="Traffic Data Summary", lines=8)
+        suggestions_output = gr.Textbox(label="Optimization Suggestions", lines=8)
+
+    visualization_output = gr.HTML(label="Traffic Visualization")
+
+    analyze_button.click(
+        analyze_traffic, inputs=file_input, outputs=[summary_output, suggestions_output, visualization_output]
+    )
+
+# Launch Gradio app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+pandas
+plotly
+gradio
+groq