app.py

import streamlit as st
import pandas as pd
import numpy as np
import faiss
import os
from groq import Groq
import matplotlib.pyplot as plt

# Initialize Groq API
GROQ_API_KEY = "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"
client = Groq(api_key=GROQ_API_KEY)

# Load the dataset
@st.cache
def load_data():
    data = pd.read_csv("traffic.csv")  # Replace with your dataset file name
    return data

# Function to create FAISS index
def create_faiss_index(documents):
    embeddings = np.random.rand(len(documents), 128).astype("float32")  # Placeholder embeddings
    index = faiss.IndexFlatL2(128)  # FAISS index for 128-dimensional embeddings
    index.add(embeddings)
    return index, embeddings

# Load traffic data
traffic_data = load_data()
st.title("Dynamic Traffic Flow Optimization")
st.write("Optimize urban traffic flow using AI strategies.")

# Display dataset
st.write("### Traffic Dataset")
st.dataframe(traffic_data)

# Generate documents from dataset
documents = [
    f"Junction: {row['Junction']}, Vehicles: {row['Vehicles']}, "
    f"DateTime: {row['DateTime']}, ID: {row['ID']}"
    for _, row in traffic_data.iterrows()
]

# Create FAISS index
index, embeddings = create_faiss_index(documents)

# User Query
query = st.text_input("Enter a traffic optimization query:")

if query:
    # Generate fake embedding for query (replace with a real embedding model)
    query_embedding = np.random.rand(1, 128).astype("float32")
    
    # Retrieve relevant documents
    distances, indices = index.search(query_embedding, 3)
    retrieved_context = " ".join([documents[i] for i in indices[0]])
    
    # Generate optimization strategy using Groq
    response = client.chat.completions.create(
        messages=[
            {"role": "system", "content": "You are an expert in traffic optimization."},
            {"role": "user", "content": query},
            {"role": "assistant", "content": f"Relevant context: {retrieved_context}"}
        ],
        model="llama3-8b-8192",
    )
    generated_strategy = response.choices[0].message.content

    # Display Results
    st.write("### Relevant Context:")
    st.write(retrieved_context)
    st.write("### Optimization Strategy:")
    st.write(generated_strategy)

    # Visualize Vehicle Count
    st.write("### Traffic Data Overview")
    fig, ax = plt.subplots()
    traffic_data.groupby('Junction')['Vehicles'].sum().plot(kind='bar', ax=ax, color='skyblue')
    ax.set_title("Total Vehicle Count by Junction")
    ax.set_ylabel("Number of Vehicles")
    st.pyplot(fig)