Update app.py

app.py

@@ -1,18 +1,17 @@
-from fastapi import FastAPI, Request, File, UploadFile, Form
-from fastapi.responses import StreamingResponse, JSONResponse, HTMLResponse
-from fastapi.staticfiles import StaticFiles
-from fastapi.templating import Jinja2Templates
+from fastapi import FastAPI, File, UploadFile, Form, JSONResponse, StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
 import pandas as pd
 from google import genai
 from google.genai import types
 import os
 import json
 import asyncio
+import numpy as np
 
 # -------------------------------
 # 🔑 Configuration
 # -------------------------------
-API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyB1jgGCuzg7ELPwNEEwaluQZoZhxhgLmAs")
+API_KEY = os.getenv("GEMINI_API_KEY", "YOUR_GEMINI_KEY")
 MODEL = "gemini-2.5-flash-lite"
 client = genai.Client(api_key=API_KEY)
 
@@ -20,33 +19,56 @@ client = genai.Client(api_key=API_KEY)
 # ⚡ FastAPI Setup
 # -------------------------------
 app = FastAPI()
-app.mount("/static", StaticFiles(directory="static"), name="static") 
-templates = Jinja2Templates(directory="templates")
+
+# Enable CORS for all origins
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # or specify your frontend URLs
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
 # -------------------------------
 # 🛠️ Helper Functions
 # -------------------------------
 def get_metadata(df: pd.DataFrame):
-    return {
+    """Extract JSON-serializable metadata."""
+    def serialize_value(x):
+        if isinstance(x, pd.Timestamp):
+            return x.isoformat()
+        elif isinstance(x, (np.integer, np.int64, np.int32)):
+            return int(x)
+        elif isinstance(x, (np.floating, np.float64, np.float32)):
+            return float(x)
+        elif pd.isna(x):
+            return None
+        else:
+            return x
+
+    metadata = {
         "columns": list(df.columns),
         "dtypes": df.dtypes.apply(lambda x: str(x)).to_dict(),
         "num_rows": df.shape[0],
         "num_cols": df.shape[1],
-        "null_counts": df.isnull().sum().to_dict(),
-        "unique_counts": df.nunique().to_dict(),
-        "sample_rows": df.head(3).to_dict(orient="records"),
+        "null_counts": {k: int(v) for k, v in df.isnull().sum().to_dict().items()},
+        "unique_counts": {k: int(v) for k, v in df.nunique().to_dict().items()},
+        "sample_rows": [
+            {col: serialize_value(val) for col, val in row.items()}
+            for row in df.head(3).to_dict(orient="records")
+        ],
     }
+    return metadata
 
 async def stream_insights(user_query, metadata):
-    """Generator that yields insights step by step as text/json strings."""
-
-    # Step 1: Start message
+    """Generator that yields insights step by step as JSON strings."""
+    # Step 1: Start
     yield json.dumps({"status": "started", "message": "File received. Extracting metadata..."}) + "\n"
-    await asyncio.sleep(0.5)
+    await asyncio.sleep(0.2)
 
     # Step 2: Metadata
     yield json.dumps({"status": "metadata", "metadata": metadata}) + "\n"
-    await asyncio.sleep(0.5)
+    await asyncio.sleep(0.2)
 
     # Step 3: Call Gemini for structured insights
     system_prompt = """
@@ -89,16 +111,16 @@ async def stream_insights(user_query, metadata):
 
     # Step 4: Excel info
     yield json.dumps({"status": "excel_info", "excel_info": parsed.get("excel_info", {})}) + "\n"
-    await asyncio.sleep(0.5)
+    await asyncio.sleep(0.2)
 
     # Step 5: Data type context
     yield json.dumps({"status": "context", "data_type_context": parsed.get("data_type_context", "")}) + "\n"
-    await asyncio.sleep(0.5)
+    await asyncio.sleep(0.2)
 
-    # Step 6: Stream each insight one by one
+    # Step 6: Stream each insight
     for insight in parsed.get("auto_insights", {}).get("insights", []):
         yield json.dumps({"status": "insight", "insight": insight}) + "\n"
-        await asyncio.sleep(0.5)
+        await asyncio.sleep(0.2)
 
     # Step 7: Query insights
     yield json.dumps({"status": "query", "query_insights": parsed.get("query_insights", {})}) + "\n"
@@ -106,14 +128,9 @@ async def stream_insights(user_query, metadata):
     # Step 8: Completed
     yield json.dumps({"status": "completed", "message": "All insights generated"}) + "\n"
 
-
 # -------------------------------
-# 🌐 Routes
+# 🌐 API Routes
 # -------------------------------
-@app.get("/", response_class=HTMLResponse)
-async def home(request: Request):
-    return templates.TemplateResponse("index.html", {"request": request})
-
 @app.post("/stream_insights")
 async def stream_insight_file(file: UploadFile = File(...), query: str = Form("Analyze the dataset")):
     """Stream structured JSON insights step by step."""