app.py

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()