Added 2 endpoints (modelfit_chat, modelforecast_chat) to use in custom chats

main.py

@@ -1,5 +1,7 @@
-import os, asyncio, httpx
-from fastapi import FastAPI, Request, HTTPException, UploadFile, File, Form
+import os, asyncio, io
+import httpx
+import pandas as pd
+from fastapi import FastAPI, Request, HTTPException, UploadFile, File, Form, Body
 from fastapi.responses import JSONResponse, Response, StreamingResponse
 from fastapi.middleware.cors import CORSMiddleware
 from slowapi import Limiter
@@ -41,8 +43,8 @@ async def ratelimit_handler(request: Request, exc: RateLimitExceeded):
 SDK_MAX_BODY_BYTES = int(os.getenv("SDK_MAX_BODY_BYTES", "25000000"))  # 25MB default for demo
 SDK_MAX_BODY_BYTES_extended = int(os.getenv("SDK_MAX_BODY_BYTES_extended", "125000000")) # 125MB default for prod 
 
-# How long to wait for upstream (API) response; long runs may need 30+ min (1800+)
-UPSTREAM_TIMEOUT = float(os.getenv("UPSTREAM_TIMEOUT", "900"))  # 15 minutes
+# How long to wait for upstream (API) response. modelforecast_ind on many individuals can take 60–90 min; set UPSTREAM_TIMEOUT high (e.g. 5400) or leave default.
+UPSTREAM_TIMEOUT = float(os.getenv("UPSTREAM_TIMEOUT", "900"))  # 15 min default so long forecast_ind runs don't hit ReadTimeout
 # While waiting for a response: every PING_INTERVAL seconds we ping the backend (secure_ping) and, if SELF_URL is set, we also ping ourselves (keep-alive) so this Space stays awake
 PING_INTERVAL = float(os.getenv("PING_INTERVAL", "270"))
 
@@ -127,6 +129,37 @@ def _extract_user_token(req: Request) -> str | None:
         return None
     return auth.split(" ", 1)[1].strip()
 
+# Chat endpoints: fetch data from URL, parse to table, forward to API
+DATA_URL_FETCH_TIMEOUT = 60.0
+DATA_URL_MAX_BYTES = 20 * 1024 * 1024  # 20MB
+
+async def _fetch_url_to_records(data_url: str) -> list:
+    """Fetch data_url (https only), parse CSV/Excel/JSON to list of dicts. Raises HTTPException on error."""
+    if not data_url.strip().lower().startswith("https://"):
+        raise HTTPException(status_code=400, detail="data_url must be an HTTPS URL.")
+    async with httpx.AsyncClient(timeout=DATA_URL_FETCH_TIMEOUT, follow_redirects=True) as client:
+        r = await client.get(data_url)
+        r.raise_for_status()
+        raw = r.content
+        content_type = (r.headers.get("content-type") or "").lower()
+    if len(raw) > DATA_URL_MAX_BYTES:
+        raise HTTPException(status_code=413, detail=f"Data at URL exceeds {DATA_URL_MAX_BYTES // (1024*1024)}MB limit.")
+    path_lower = data_url.split("?")[0].lower()
+    try:
+        if "json" in content_type or path_lower.endswith(".json"):
+            df = pd.read_json(io.BytesIO(raw))
+        elif "spreadsheet" in content_type or "excel" in content_type or path_lower.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(io.BytesIO(raw))
+        else:
+            # CSV or default
+            df = pd.read_csv(io.BytesIO(raw))
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Could not parse data from URL: {str(e)[:200]}")
+    for col in df.columns:
+        if pd.api.types.is_datetime64_any_dtype(df[col]):
+            df[col] = df[col].astype(str)
+    return df.to_dict(orient="records")
+
 async def _forward(path: str, method: str = "GET", json_body=None, user_token: str | None = None):
     """
     Forward request to the PRIVATE Space:
@@ -516,6 +549,55 @@ async def modelforecast(req: Request):
     body = await req.json()
     return await _forward("/modelforecast/", "POST", json_body=body, user_token=user_token)
 
+
+@app.post("/modelfit_chat/")
+async def modelfit_chat(
+    req: Request,
+    data_url: str = Body(..., embed=True),
+    id_col: str = Body(..., embed=True),
+    time_col: str = Body(..., embed=True),
+    y: str = Body(..., embed=True),
+    lag_y: object = Body(None, embed=True),
+    lagged_features: object = Body({}, embed=True),
+    current_features: object = Body([], embed=True),
+    filter_by_significance: bool = Body(False, embed=True),
+    meanvar_test: bool = Body(False, embed=True),
+    signif: object = Body(0.05, embed=True),
+):
+    """Fetch training data from data_url (HTTPS), then call /modelfit/ on the API. Returns JSON fit result."""
+    user_token = _extract_user_token(req)
+    if not user_token:
+        raise HTTPException(status_code=401, detail="Missing Authorization Bearer token (dt+...)")
+    df_records = await _fetch_url_to_records(data_url)
+    payload = {
+        "df": df_records,
+        "id_col": id_col,
+        "time_col": time_col,
+        "y": y,
+        "lag_y": lag_y,
+        "lagged_features": lagged_features,
+        "current_features": current_features,
+        "filter_by_significance": filter_by_significance,
+        "meanvar_test": meanvar_test,
+        "signif": signif,
+    }
+    return await _forward("/modelfit/", "POST", json_body=payload, user_token=user_token)
+
+
+@app.post("/modelforecast_chat/")
+async def modelforecast_chat(
+    req: Request,
+    data_url: str = Body(..., embed=True),
+):
+    """Fetch forecast input data from data_url (HTTPS), then call /modelforecast/ on the API. Returns JSON forecast list."""
+    user_token = _extract_user_token(req)
+    if not user_token:
+        raise HTTPException(status_code=401, detail="Missing Authorization Bearer token (dt+...)")
+    df_records = await _fetch_url_to_records(data_url)
+    payload = {"df_forecast": df_records}
+    return await _forward("/modelforecast/", "POST", json_body=payload, user_token=user_token)
+
+
 @app.post("/modelfit-file/")
 async def modelfit_file(
     req: Request,

requirements.txt

@@ -3,4 +3,6 @@ uvicorn
 httpx
 python-multipart
 slowapi
-limits
+limits
+pandas
+openpyxl