main.py

from fastapi import FastAPI

import pandas as pd
from fastapi import UploadFile, File
from io import BytesIO
import uvicorn
import seaborn as sns
import matplotlib.pyplot as plt
from fastapi.responses import StreamingResponse
import io

app = FastAPI()

@app.post("/figure")
async def create_figure(file: UploadFile = File(...)):
    # Read the uploaded Excel file
    contents = await file.read()
    excel_data = pd.read_excel(BytesIO(contents))
    
    print(excel_data.head())  # Print the first few rows of the DataFrame for debugging
    return {"message": "File received", "filename": file.filename}
    # Create a plot (example: correlation heatmap)
    # plt.figure(figsize=(10, 8))
    # sns.heatmap(excel_data.corr(), annot=True, cmap="coolwarm")


    # plt.title("Correlation Heatmap")
    
    # # Save the plot to a BytesIO buffer
    # buf = io.BytesIO()
    # plt.savefig(buf, format="png")
    # buf.seek(0)
    # plt.close()
    
    # # Return the plot as a StreamingResponse
    # return StreamingResponse(buf, media_type="image/png")

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)