File size: 4,819 Bytes
b0f7449 a5ba7b9 b0f7449 a5ba7b9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
import gradio as gr
import pandas as pd
import numpy as np
import requests
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
from stable_baselines3 import PPO
import gym # Ensure gym is imported
from gym import spaces
from stable_baselines3.common.vec_env import DummyVecEnv
import plotly.graph_objects as go
# 1. Set up Groq API Integration
GROQ_API_KEY = "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" # Replace with your API key
groq_api_url = "https://api.groq.com/v1/traffic/optimize" # Replace with correct endpoint
headers = {
'Authorization': f'Bearer {GROQ_API_KEY}',
}
# Load Traffic Data from CSV (Replace with uploaded file handling for Gradio)
def load_traffic_data(file_path):
data = pd.read_csv(file_path)
return data
# 3. Function to interact with Groq API to get traffic optimization strategies
def get_optimization_strategy(traffic_data):
traffic_data = [int(x) if isinstance(x, np.int64) else x for x in traffic_data]
response = requests.post(groq_api_url, json={'traffic_data': traffic_data}, headers=headers)
if response.status_code == 200:
optimization_strategy = response.json()
return optimization_strategy
else:
return f"Error: {response.status_code}, {response.text}"
# 4. Create a Custom Traffic Environment for RL Simulation
class TrafficEnv(gym.Env):
def __init__(self, traffic_data):
super(TrafficEnv, self).__init__()
self.action_space = spaces.Discrete(3) # 3 possible actions
self.observation_space = spaces.Box(low=0, high=50, shape=(5,), dtype=np.float32)
self.current_state = np.zeros(5) # Start with zero traffic data
self.traffic_data = traffic_data
def reset(self):
self.current_state = np.array(self.traffic_data.iloc[0, 3:7], dtype=np.float32) # Use first row for starting state
return self.current_state
def step(self, action):
if action == 0: # Decrease traffic
self.current_state = self.current_state - np.random.randint(1, 5, size=self.current_state.shape)
elif action == 1: # No change
self.current_state = self.current_state
elif action == 2: # Increase traffic
self.current_state = self.current_state + np.random.randint(1, 5, size=self.current_state.shape)
self.current_state = np.clip(self.current_state, 0, None)
reward = -np.sum(self.current_state) # Minimize traffic (negative sum as reward)
done = np.sum(self.current_state) < 50
return self.current_state, reward, done, {}
def create_environment(traffic_data):
return DummyVecEnv([lambda: TrafficEnv(traffic_data)])
# Visualize Traffic Flow using Plotly
def visualize_traffic_flow(traffic_data):
locations = ['CarCount', 'BikeCount', 'BusCount', 'TruckCount']
traffic_flow = traffic_data.iloc[0, 3:7] # Use first row of traffic counts
fig = go.Figure(data=[go.Bar(x=locations, y=traffic_flow)])
fig.update_layout(title='Real-Time Traffic Flow', xaxis_title='Location', yaxis_title='Traffic Volume')
return fig
# RAG-based Optimization Strategy using Hugging Face Transformers
def rag_based_optimization(query):
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq")
inputs = tokenizer(query, return_tensors="pt")
generated_ids = model.generate(input_ids=inputs['input_ids'])
generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
return generated_text
# Gradio Interface for the App
def optimize_traffic(traffic_file, query):
# Load Traffic Data from uploaded file
traffic_data = load_traffic_data(traffic_file.name)
# Get optimization strategy from Groq API
optimization_strategy = get_optimization_strategy(traffic_data.iloc[0, 3:7].values.tolist())
# Visualize traffic flow
traffic_fig = visualize_traffic_flow(traffic_data)
# Get RAG-based optimization strategy
rag_strategy = rag_based_optimization(query)
return optimization_strategy, traffic_fig, rag_strategy
# Create Gradio Interface
iface = gr.Interface(
fn=optimize_traffic,
inputs=[
gr.File(label="Upload Traffic Data CSV"),
gr.Textbox(label="Enter Optimization Query", value="Optimize traffic flow for downtown area.")
],
outputs=[
gr.JSON(label="Optimization Strategy from Groq API"),
gr.Plot(label="Traffic Flow Visualization"),
gr.Textbox(label="RAG-based Optimization Strategy")
],
live=True,
title="Traffic Optimization App",
description="This app optimizes traffic flow using RL, Groq API, and RAG model-based optimization strategies."
)
if __name__ == "__main__":
iface.launch()
|