Added new endpoints for the gemini live feature
This commit is contained in:
@@ -0,0 +1,85 @@
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from fastapi import APIRouter, Depends, Request, HTTPException
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from app.api.deps import get_current_module
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from app.models.module import Module
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from sqlalchemy.orm import Session
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from app.core.database import get_db
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from app.core.limiter import limiter
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from app.core.config import settings
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from pydantic import BaseModel
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import httpx
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router = APIRouter()
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GEMINI_API_BASE = "https://generativelanguage.googleapis.com/v1beta"
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GEMINI_LIVE_WSS_URI = (
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"wss://generativelanguage.googleapis.com/ws/"
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"google.ai.generativelanguage.v1beta.GenerativeService.BidiGenerateContent"
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)
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class LiveTokenRequest(BaseModel):
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model: str = "gemini-2.0-flash-live-001"
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system_instruction: str
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voice_name: str | None = "Puck"
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@router.post("/live-token")
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@limiter.limit(settings.RATE_LIMIT)
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async def generate_live_token(
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request: Request,
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body: LiveTokenRequest,
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module: Module = Depends(get_current_module),
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db: Session = Depends(get_db),
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):
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if not settings.GOOGLE_API_KEY or settings.GOOGLE_API_KEY == "your-google-api-key":
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return {
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"token": "mock-ephemeral-token",
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"expires_at": "2026-04-02T02:00:00Z",
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"websocket_uri": GEMINI_LIVE_WSS_URI,
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}
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payload = {
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"systemInstruction": {
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"parts": [{"text": body.system_instruction}]
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},
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"generationConfig": {
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"responseModalities": ["AUDIO", "TEXT"],
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},
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}
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if body.voice_name:
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payload["generationConfig"]["speechConfig"] = {
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"voiceConfig": {
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"prebuiltVoiceConfig": {"voiceName": body.voice_name}
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}
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}
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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.post(
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f"{GEMINI_API_BASE}/models/{body.model}:generateEphemeralToken",
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json=payload,
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params={"key": settings.GOOGLE_API_KEY},
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)
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if response.status_code != 200:
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raise HTTPException(
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status_code=502,
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detail=f"Gemini token API error {response.status_code}: {response.text}",
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)
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data = response.json()
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# Response name is "ephemeralTokens/{token_value}"
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token_value = data.get("name", "").split("/")[-1]
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expires_at = data.get("expireTime", "")
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if module:
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module.ingress_tokens += 1
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module.total_tokens += 1
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db.commit()
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return {
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"token": token_value,
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"expires_at": expires_at,
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"websocket_uri": GEMINI_LIVE_WSS_URI,
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}
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@@ -0,0 +1,167 @@
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from fastapi import APIRouter, Depends, Request, HTTPException
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from app.api.deps import get_current_module
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from app.models.module import Module
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from sqlalchemy.orm import Session
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from app.core.database import get_db
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from app.core.limiter import limiter
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from app.core.config import settings
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from pydantic import BaseModel
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from typing import List
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from google import genai
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from google.genai import types
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import json
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router = APIRouter()
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_client = None
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def get_gemini_client():
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global _client
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if _client is None and settings.GOOGLE_API_KEY and settings.GOOGLE_API_KEY != "your-google-api-key":
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_client = genai.Client(
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api_key=settings.GOOGLE_API_KEY,
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http_options={"api_version": "v1alpha"},
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)
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return _client
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class TranscriptEntry(BaseModel):
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role: str # "user" or "assistant"
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text: str
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class ScorecardCriterion(BaseModel):
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name: str
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weight: float
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description: str
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good_example: str | None = None
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poor_example: str | None = None
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class Scorecard(BaseModel):
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criteria: List[ScorecardCriterion]
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class ScoreRequest(BaseModel):
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transcript: List[TranscriptEntry]
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scorecard: Scorecard
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pass_threshold: int = 70
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SCORE_RESPONSE_SCHEMA = {
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"type": "OBJECT",
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"properties": {
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"overall_score": {"type": "NUMBER"},
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"passed": {"type": "BOOLEAN"},
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"criteria_scores": {
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"type": "ARRAY",
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"items": {
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"type": "OBJECT",
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"properties": {
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"name": {"type": "STRING"},
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"score": {"type": "NUMBER"},
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"feedback": {"type": "STRING"},
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},
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},
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},
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"positives": {"type": "ARRAY", "items": {"type": "STRING"}},
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"improvements": {"type": "ARRAY", "items": {"type": "STRING"}},
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},
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"required": ["overall_score", "passed", "criteria_scores", "positives", "improvements"],
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}
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def _build_prompt(body: ScoreRequest) -> str:
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transcript_text = "\n".join(
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f"{e.role.upper()}: {e.text}" for e in body.transcript
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)
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criteria_text = "\n".join(
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f"- {c.name} (weight: {c.weight}%): {c.description}"
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+ (f"\n Good example: {c.good_example}" if c.good_example else "")
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+ (f"\n Poor example: {c.poor_example}" if c.poor_example else "")
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for c in body.scorecard.criteria
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)
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return f"""You are an expert conversation evaluator for workplace learning simulations.
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Score the following conversation transcript against the provided scorecard criteria.
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TRANSCRIPT:
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{transcript_text}
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SCORING CRITERIA (weights must sum to 100%):
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{criteria_text}
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Instructions:
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- Score each criterion from 0 to 100 based on evidence in the transcript.
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- Calculate overall_score as the weighted average of all criteria scores.
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- Set passed to true if overall_score >= {body.pass_threshold}.
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- Write specific, evidence-based feedback for each criterion (1-2 sentences).
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- List exactly 2-3 positives (specific things done well with transcript evidence).
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- List exactly 2-3 improvements (specific, actionable suggestions).
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Return a single JSON object following the response schema."""
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@router.post("/score")
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@limiter.limit(settings.RATE_LIMIT)
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async def score_conversation(
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request: Request,
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body: ScoreRequest,
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module: Module = Depends(get_current_module),
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db: Session = Depends(get_db),
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):
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client = get_gemini_client()
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if not client:
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mock_result = {
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"overall_score": 75.0,
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"passed": True,
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"criteria_scores": [
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{
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"name": c.name,
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"score": 75.0,
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"feedback": f"Mock feedback for {c.name}.",
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}
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for c in body.scorecard.criteria
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],
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"positives": [
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"Maintained a professional tone throughout.",
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"Responded clearly to the main questions.",
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],
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"improvements": [
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"Could provide more specific examples.",
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"Consider addressing the customer's emotional state earlier.",
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],
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}
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return mock_result
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prompt = _build_prompt(body)
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try:
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response = await client.aio.models.generate_content(
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model="gemini-2.5-flash",
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contents=prompt,
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config=types.GenerateContentConfig(
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response_mime_type="application/json",
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response_schema=SCORE_RESPONSE_SCHEMA,
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temperature=0.2,
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),
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)
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result = json.loads(response.text)
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if module:
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usage = response.usage_metadata
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prompt_tokens = usage.prompt_token_count if usage else len(prompt) // 4
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completion_tokens = usage.candidates_token_count if usage else len(response.text) // 4
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module.ingress_tokens += prompt_tokens
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module.egress_tokens += completion_tokens
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module.total_tokens += prompt_tokens + completion_tokens
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db.commit()
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return result
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except json.JSONDecodeError as e:
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raise HTTPException(status_code=502, detail=f"Gemini returned invalid JSON: {str(e)}")
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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+3
-1
@@ -1,7 +1,9 @@
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from fastapi import APIRouter
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from app.api.endpoints import storyline, gemini, openai
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from app.api.endpoints import storyline, gemini, openai, gemini_live, gemini_score
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api_router = APIRouter()
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api_router.include_router(storyline.router, prefix="/storyline", tags=["storyline"])
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api_router.include_router(gemini.router, prefix="/gemini", tags=["gemini"])
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api_router.include_router(openai.router, prefix="/openai", tags=["openai"])
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api_router.include_router(gemini_live.router, prefix="/gemini", tags=["gemini-live"])
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api_router.include_router(gemini_score.router, prefix="/gemini", tags=["gemini-live"])
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