Update app.py

app.py

@@ -1,5 +1,5 @@
-from fastapi import FastAPI, File, UploadFile, Form, StreamingResponse
-from fastapi.responses import JSONResponse  # <- correct import
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import JSONResponse, StreamingResponse
 from fastapi.middleware.cors import CORSMiddleware
 import pandas as pd
 from google import genai
@@ -7,13 +7,11 @@ from google.genai import types
 import os
 import json
 import asyncio
-import numpy as np
-
 
 # -------------------------------
 # 🔑 Configuration
 # -------------------------------
-API_KEY = os.getenv("GEMINI_API_KEY", "YOUR_GEMINI_KEY")
+API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyB1jgGCuzg7ELPwNEEwaluQZoZhxhgLmAs")
 MODEL = "gemini-2.5-flash-lite"
 client = genai.Client(api_key=API_KEY)
 
@@ -22,11 +20,10 @@ client = genai.Client(api_key=API_KEY)
 # -------------------------------
 app = FastAPI()
 
-# Enable CORS for all origins
+# Enable CORS
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # or specify your frontend URLs
-    allow_credentials=True,
+    allow_origins=["*"],
     allow_methods=["*"],
     allow_headers=["*"],
 )
@@ -35,44 +32,29 @@ app.add_middleware(
 # 🛠️ Helper Functions
 # -------------------------------
 def get_metadata(df: pd.DataFrame):
-    """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": {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")
-        ],
+    # Convert all timestamps to string to avoid JSON serialization issues
+    df_serializable = df.copy()
+    for col in df_serializable.select_dtypes(include=['datetime64[ns]']).columns:
+        df_serializable[col] = df_serializable[col].astype(str)
+    return {
+        "columns": list(df_serializable.columns),
+        "dtypes": df_serializable.dtypes.apply(lambda x: str(x)).to_dict(),
+        "num_rows": df_serializable.shape[0],
+        "num_cols": df_serializable.shape[1],
+        "null_counts": df_serializable.isnull().sum().to_dict(),
+        "unique_counts": df_serializable.nunique().to_dict(),
+        "sample_rows": df_serializable.head(3).to_dict(orient="records"),
     }
-    return metadata
 
 async def stream_insights(user_query, metadata):
-    """Generator that yields insights step by step as JSON strings."""
-    # Step 1: Start
+    """Stream insights step by step."""
     yield json.dumps({"status": "started", "message": "File received. Extracting metadata..."}) + "\n"
-    await asyncio.sleep(0.2)
+    await asyncio.sleep(0.5)
 
-    # Step 2: Metadata
     yield json.dumps({"status": "metadata", "metadata": metadata}) + "\n"
-    await asyncio.sleep(0.2)
+    await asyncio.sleep(0.5)
 
-    # Step 3: Call Gemini for structured insights
+    # Gemini system prompt
     system_prompt = """
     You are a data analysis assistant.
     Always return JSON with this schema:
@@ -111,31 +93,24 @@ async def stream_insights(user_query, metadata):
         yield json.dumps({"status": "error", "raw_output": result}) + "\n"
         return
 
-    # Step 4: Excel info
     yield json.dumps({"status": "excel_info", "excel_info": parsed.get("excel_info", {})}) + "\n"
-    await asyncio.sleep(0.2)
+    await asyncio.sleep(0.5)
 
-    # Step 5: Data type context
     yield json.dumps({"status": "context", "data_type_context": parsed.get("data_type_context", "")}) + "\n"
-    await asyncio.sleep(0.2)
+    await asyncio.sleep(0.5)
 
-    # 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(0.2)
+        await asyncio.sleep(0.5)
 
-    # Step 7: Query insights
     yield json.dumps({"status": "query", "query_insights": parsed.get("query_insights", {})}) + "\n"
-
-    # Step 8: Completed
     yield json.dumps({"status": "completed", "message": "All insights generated"}) + "\n"
 
 # -------------------------------
-# 🌐 API Routes
+# 🌐 Routes
 # -------------------------------
 @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."""
     try:
         df = pd.read_excel(file.file)
     except Exception as e: