Update app.py

app.py

@@ -1,81 +1,86 @@
 import os
 import pandas as pd
-import gradio as gr
 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 read and process the dataset
+# Function to process the traffic dataset
 def process_traffic_data(csv_file):
-    # Load the CSV dataset
     data = pd.read_csv(csv_file)
-    # Example: Calculate peak hours and most congested traffic situations
-    peak_hours = data.groupby('Time')['Total'].sum().idxmax()
-    worst_situation = data.groupby('Traffic Situation')['Total'].sum().idxmax()
-    summary = f"Peak traffic hour: {peak_hours}. Worst traffic situation: {worst_situation}."
+    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 based on the dataset
+# Function to generate suggestions using Groq API
 def generate_suggestions(summary):
-    input_content = f"Here is the traffic data summary: {summary}. Provide strategies to optimize traffic flow."
+    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 a visual simulation of traffic flow
-def visualize_traffic_flow(data):
-    # Example: Line chart showing total traffic flow over time
-    fig = px.line(
+# Function to create traffic visualization
+def create_visualization(data):
+    fig = px.bar(
         data,
-        x="Time",
+        x="Day of the week",
         y="Total",
         color="Traffic Situation",
-        title="Traffic Flow Simulation",
-        labels={"Total": "Total Vehicles", "Time": "Time of Day"},
+        title="Total Traffic by Day and Traffic Situation",
+        labels={"Total": "Total Vehicles", "Day of the week": "Day"},
     )
     return fig.to_html()
 
-# Function to combine all functionalities
-def traffic_optimizer(csv_file):
-    # Process the dataset
-    data, summary = process_traffic_data(csv_file)
-    
-    # Generate suggestions
+# Gradio interface function
+def analyze_traffic(file):
+    data, summary = process_traffic_data(file.name)
     suggestions = generate_suggestions(summary)
-    
-    # Visual simulation
-    visualization = visualize_traffic_flow(data)
-    
-    return summary, suggestions, visualization
+    visualization_html = create_visualization(data)
+    return summary, suggestions, visualization_html
 
-# Gradio Interface
+# Gradio interface definition
 with gr.Blocks() as demo:
-    gr.Markdown("# Traffic Flow Optimization App")
-    gr.Markdown("Upload your traffic data CSV file to get optimization suggestions and a visual simulation.")
-    
-    with gr.Row():
-        csv_input = gr.File(label="Upload CSV File", type="file")
-    
+    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():
-        summary_output = gr.Textbox(label="Traffic Summary")
-        suggestions_output = gr.Textbox(label="Optimization Suggestions")
-    
+        file_input = gr.File(label="Upload Traffic Data CSV")
+        analyze_button = gr.Button("Analyze Traffic Data")
+
     with gr.Row():
-        visualization_output = gr.HTML(label="Traffic Flow Simulation")
-    
-    submit_btn = gr.Button("Generate Insights")
-    
-    submit_btn.click(
-        traffic_optimizer,
-        inputs=[csv_input],
-        outputs=[summary_output, suggestions_output, visualization_output],
+        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]
     )
 
-# Run the Gradio app
+# Launch Gradio app
 if __name__ == "__main__":
     demo.launch()