Update app.py

app.py

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+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
+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()