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tools.py

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+from smolagents import CodeAgent, HfApiModel
+from sql_data import sql_query, get_schema
+from sqlalchemy import create_engine, inspect, text
+import os
+from dotenv import load_dotenv
+from typing import Dict, List, Any
+import json
+
+# Load environment variables
+load_dotenv()
+
+# Example queries that the agent can handle
+EXAMPLE_QUERIES = [
+    "Quels sont les tarifs moyens des conteneurs 20ft et 40ft entre tous les ports ?",
+    "Quels sont les ports d'origine les plus fréquents ?",
+    "Montre-moi les routes avec des tarifs élevés pour les conteneurs 40ft",
+    "Quelle est l'évolution des prix au fil du temps pour la route Surabaya vers Nansha ?",
+    "Quelles sont les destinations disponibles depuis Shanghai ?",
+]
+
+
+class FreightAgent:
+    def __init__(self):
+        self.setup_agent()
+
+    def setup_agent(self) -> None:
+        """
+        Initialize the CodeAgent with SQL tools.
+        Create a CodeAgent with two tools: `sql_query` and `get_schema`.
+        `sql_query` allows to perform SQL queries on the freights table.
+        `get_schema` returns the schema of the freights table.
+        """
+        self.agent = CodeAgent(
+            tools=[sql_query, get_schema],
+            model=HfApiModel("meta-llama/Llama-3.1-8B-Instruct"),
+        )
+
+    def query(self, question: str) -> str:
+        """
+        Ask a question about the freight data in natural language
+        """
+        return self.agent.run(question)

utils.py

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+import pandas as pd
+import sqlite3
+import requests
+from typing import List, Dict, Any
+import os
+from sqlalchemy import create_engine, Column, Float, String, Integer, DateTime
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+
+# Create base class for declarative models
+Base = declarative_base()
+
+
+class Freight(Base):
+    """SQLAlchemy model for freight data"""
+
+    __tablename__ = "freights"
+
+    id = Column(Integer, primary_key=True)
+    departure = Column(DateTime)
+    origin_port_locode = Column(String)
+    origin_port_name = Column(String)
+    destination_port = Column(String)
+    destination_port_name = Column(String)
+    dv20rate = Column(Float)
+    dv40rate = Column(Float)
+    currency = Column(String)
+    inserted_on = Column(DateTime)
+
+
+def download_csv(url: str, local_path: str = "freights.csv") -> str:
+    """
+    Download CSV file from Hugging Face and save it locally
+    """
+    response = requests.get(url)
+    with open(local_path, "wb") as f:
+        f.write(response.content)
+    return local_path
+
+
+def create_database(db_name: str = "freights.db") -> None:
+    """
+    Create SQLite database and necessary tables
+    """
+    engine = create_engine(f"sqlite:///{db_name}")
+    Base.metadata.create_all(engine)
+
+
+def load_csv_to_db(csv_path: str, db_name: str = "freights.db") -> None:
+    """
+    Load CSV data into SQLite database
+    """
+    # Read CSV
+    df = pd.read_csv(csv_path, parse_dates=["departure", "inserted_on"])
+
+    # Connect to database
+    engine = create_engine(f"sqlite:///{db_name}")
+
+    # Save to database
+    df.to_sql("freights", engine, if_exists="replace", index=False)
+
+
+def initialize_database(csv_url: str) -> None:
+    """
+    Initialize the database by downloading CSV and loading data.
+    Args:
+        csv_url: URL of the CSV file to download and load.
+    """
+    # Download CSV
+    csv_path = download_csv(csv_url)
+
+    # Create and load database
+    create_database()
+    load_csv_to_db(csv_path)
+    print("Database initialized.")
+
+    # Clean up CSV file
+    if os.path.exists(csv_path):
+        os.remove(csv_path)