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import json
from pathlib import Path
import pandas as pd
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
app = FastAPI()
origins = [
"https://pro.openbb.dev",
"https://pro.openbb.co",
"https://excel.openbb.co",
"https://excel.openbb.dev",
]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
ROOT_PATH = Path(__file__).parent.resolve()
@app.get("/")
def read_root():
return {"Info": "Full example for OpenBB Custom Backend"}
@app.get("/csv-data")
def csv_data():
"""Read mock csv data and return it as a table to your widget"""
# Specify the path to your CSV file
csv_file_path = "mock_data.csv"
try:
# Convert the DataFrame to a dictionary and return the data
return pd.read_csv((ROOT_PATH / csv_file_path).open()).to_dict(orient="records")
except Exception as e:
# Handle error cases here
error_message = f"Error reading the CSV file: {str(e)}"
return JSONResponse(content={"error": error_message}, status_code=500)
from pydantic import BaseModel
import random
import time
# Define a Pydantic model for stock data
class StockData(BaseModel):
ticker: str
price: float
volume: int
timestamp: float
# Create a mock function to simulate fetching stock data
def generate_mock_stock_data(ticker: str):
return {
"ticker": ticker,
"price": round(random.uniform(100, 500), 2), # Generate a random stock price
"volume": random.randint(1000, 10000), # Random trading volume
"timestamp": time.time() # Current timestamp
}
# Define the /api/stocks endpoint
@app.get("/api/stocks", response_model=StockData)
async def get_stock_data(ticker: str):
"""
Fetches stock data for a given ticker symbol.
The data includes price, volume, and timestamp.
"""
# Generate mock data for the given ticker
stock_data = generate_mock_stock_data(ticker)
return stock_data
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