Upload app2.py

app2.py

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+import streamlit as st
+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+import io
+import pandas as pd
+import threading
+
+# Create FastAPI app
+app = FastAPI()
+
+# Allow public Space to access this private Space API
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # You can restrict this to your public Space URL if needed
+    allow_methods=["POST"],
+    allow_headers=["*"],
+)
+
+# Define API endpoint to process files
+@app.post("/run")
+async def process_file(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        df = pd.read_excel(io.BytesIO(contents))
+
+        # Process the file (example: return first few rows)
+        result = df.head().to_dict()
+
+        return {"status": "success", "preview": result}
+
+    except Exception as e:
+        return {"status": "error", "message": str(e)}
+
+# Function to run FastAPI on a separate thread
+def run_fastapi():
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+# Start FastAPI in a separate thread
+threading.Thread(target=run_fastapi, daemon=True).start()
+
+# Streamlit UI (For Debugging in Private Space)
+st.title("📐 Math Assignment Optimizer (Private API)")
+st.write("This private Space runs a FastAPI backend.")
+
+st.warning("⚠️ This app is designed to be accessed via API, not directly.")