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models/gemini_client.py
-----------------------
Gemini API client for Notiflow.
Serves two distinct roles:
1. FALLBACK REASONER — used by ModelRouter when Nova is unavailable.
generate(prompt) returns a plain-text response that must match
Nova's JSON output schema exactly (enforced by the prompt).
2. NOTIFICATION GENERATOR — used by notification_generator.py to
produce realistic Hinglish business notifications for demo mode.
generate_notifications(n) returns a list of notification dicts.
Configuration
-------------
GEMINI_API_KEY — from .env / environment variable
Dependencies
------------
pip install google-generativeai
"""
from __future__ import annotations
import json
import logging
import re
from typing import Any
from app.config import GEMINI_API_KEY
logger = logging.getLogger(__name__)
# Gemini model to use — flash is fast and sufficient for this use case
_GEMINI_MODEL = "gemini-2.5-flash"
_client = None # lazy singleton
def _get_client():
"""Return a cached Gemini GenerativeModel instance."""
global _client
if _client is not None:
return _client
if not GEMINI_API_KEY:
raise RuntimeError(
"GEMINI_API_KEY is not set. "
"Add it to your .env file: GEMINI_API_KEY=your_key_here"
)
try:
import google.generativeai as genai
genai.configure(api_key=GEMINI_API_KEY)
_client = genai.GenerativeModel(_GEMINI_MODEL)
logger.info("Gemini client initialised (model=%s)", _GEMINI_MODEL)
return _client
except ImportError:
raise RuntimeError(
"google-generativeai is not installed. "
"Run: pip install google-generativeai"
)
except Exception as exc:
raise RuntimeError(f"Failed to initialise Gemini client: {exc}") from exc
def _strip_fences(text: str) -> str:
"""Strip markdown code fences from model output."""
return re.sub(r"```(?:json)?|```", "", text).strip()
# ---------------------------------------------------------------------------
# Public API — Role 1: Fallback reasoner for ModelRouter
# ---------------------------------------------------------------------------
def generate(prompt: str) -> str:
"""
Send a prompt to Gemini and return the raw text response.
Used by ModelRouter as a Nova fallback. The prompt already instructs
the model to return JSON — this function returns the raw string and
lets the caller parse it.
Args:
prompt: Fully rendered prompt (same prompt sent to Nova).
Returns:
Raw text response from Gemini (may contain JSON).
Raises:
RuntimeError: If the API call fails or client cannot be initialised.
"""
client = _get_client()
try:
response = client.generate_content(prompt)
raw = response.text or ""
logger.debug("Gemini raw response: %r", raw[:200])
return _strip_fences(raw)
except Exception as exc:
logger.error("Gemini generate() failed: %s", exc)
raise RuntimeError(f"Gemini API error: {exc}") from exc
# ---------------------------------------------------------------------------
# Public API — Role 2: Notification generator
# ---------------------------------------------------------------------------
_NOTIFICATION_PROMPT = """
You are simulating incoming business notifications for a small business in India.
Generate {n} realistic business notifications in Hinglish (Hindi + English mix).
Each notification must come from one of these sources:
- whatsapp (informal text from customers or suppliers)
- payment (UPI payment confirmation message)
- amazon (marketplace order or return notification)
- return (customer return or exchange request)
Each notification must represent ONE of these business events:
- An order for a product (item name + quantity)
- A payment received (person name + amount)
- A credit/udhar request
- A return or exchange request
- A preparation/packing request
Rules:
- Use natural Hinglish phrasing. Not too formal.
- Vary sources and event types.
- Include real-sounding names (Rahul, Priya, Suresh, Amit, etc.)
- Include real product names (kurti, aata, daal, saree, etc.)
- Do NOT include any explanation or preamble.
- Return ONLY a valid JSON array, nothing else.
Output format (JSON array only, no markdown):
[
{{"source": "whatsapp", "message": "bhaiya 3 kurti bhej dena"}},
{{"source": "payment", "message": "Rahul ne 15000 bheja UPI se"}}
]
"""
def generate_notifications(n: int = 5) -> list[dict[str, str]]:
"""
Generate n realistic Hinglish business notifications via Gemini.
Args:
n: Number of notifications to generate (default 5).
Returns:
List of dicts with keys "source" and "message".
Returns an empty list if generation fails (non-fatal).
Example:
>>> generate_notifications(3)
[
{"source": "whatsapp", "message": "bhaiya 3 kurti bhej dena"},
{"source": "payment", "message": "Rahul ne 15000 bheja"},
{"source": "return", "message": "size chota hai exchange karna hai"},
]
"""
prompt = _NOTIFICATION_PROMPT.format(n=n)
try:
raw = generate(prompt)
data = json.loads(raw)
if not isinstance(data, list):
logger.warning("Gemini notification response was not a list: %s", type(data))
return []
# Validate each entry has required keys
valid = [
item for item in data
if isinstance(item, dict)
and "source" in item
and "message" in item
]
logger.info("Generated %d notifications via Gemini", len(valid))
return valid
except json.JSONDecodeError as exc:
logger.warning("Could not parse Gemini notification output as JSON: %s", exc)
return []
except Exception as exc:
logger.warning("generate_notifications failed: %s", exc)
return [] |