File size: 6,596 Bytes
9594d90 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | from fastapi import APIRouter, HTTPException, Request, Depends
from pydantic import BaseModel, Field
from uuid import uuid4
from model.inference import model_instance
from db.database import SessionLocal
from db.models import Prediction
from core.advanced_cache import advanced_cache
from slowapi import Limiter
from slowapi.util import get_remote_address
import logging
import time
import torch
from typing import Optional
logger = logging.getLogger(__name__)
limiter = Limiter(key_func=get_remote_address)
router = APIRouter()
class PredictRequest(BaseModel):
prompt: str = Field(..., min_length=1, max_length=2000, description="Context prompt")
response: str = Field(..., min_length=1, max_length=2000, description="AI response to evaluate")
question: str = Field(..., min_length=1, max_length=500, description="Question being answered")
use_cache: Optional[bool] = Field(True, description="Whether to use caching")
class PredictResponse(BaseModel):
is_hallucination: bool
confidence_score: float
raw_prediction: str
processing_time: float
request_id: str
cached: bool
method: Optional[str] = None # Add method field
@router.post("/debug-predict")
async def debug_predict(request: Request, predict_request: PredictRequest):
"""
Debug version of predict endpoint that shows raw model output
"""
req_id = str(uuid4())
try:
# Make prediction with debug info
input_text = model_instance.format_prompt(
predict_request.prompt,
predict_request.response,
predict_request.question
)
# Get raw model prediction
inputs = model_instance.tokenizer(
input_text,
return_tensors="pt",
max_length=512,
truncation=True,
padding=True
)
inputs = {k: v.to(model_instance.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model_instance.model.generate(
**inputs,
max_new_tokens=20,
num_return_sequences=1,
temperature=0.1, # Lower temperature for more deterministic output
do_sample=False, # Disable sampling for debugging
pad_token_id=model_instance.tokenizer.pad_token_id,
eos_token_id=model_instance.tokenizer.eos_token_id
)
# Decode prediction
full_output = model_instance.tokenizer.decode(outputs[0], skip_special_tokens=True)
pred_text = full_output.replace(input_text, "").strip().lower()
# Manual confidence and hallucination detection
confidence_score = model_instance._calculate_confidence(pred_text)
is_hallucination = model_instance._is_hallucination(pred_text)
return {
"request_id": req_id,
"input_prompt": input_text,
"raw_model_output": full_output,
"extracted_prediction": pred_text,
"is_hallucination": is_hallucination,
"confidence_score": confidence_score,
"debug_info": {
"model_name": model_instance.model.config.name_or_path if hasattr(model_instance.model.config, 'name_or_path') else "unknown",
"device": str(model_instance.device),
"input_length": len(input_text),
"output_length": len(full_output)
}
}
except Exception as e:
logger.error(f"Debug prediction error: {str(e)}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Debug prediction failed: {str(e)}")
@router.post("/predict", response_model=PredictResponse)
@limiter.limit("60/minute; 10/10seconds; 3/5seconds")
async def predict(request: Request, predict_request: PredictRequest):
"""
Predict whether an AI response contains hallucination
"""
req_id = str(uuid4())
start_time = time.time()
try:
# Input validation
if not all([predict_request.prompt.strip(), predict_request.response.strip(), predict_request.question.strip()]):
raise HTTPException(status_code=400, detail="All fields must be non-empty")
# Check cache first if enabled
cached_result = None
if predict_request.use_cache:
cached_result = advanced_cache.get(
predict_request.prompt,
predict_request.response,
predict_request.question
)
if cached_result:
logger.info(f"Cache hit for request {req_id}")
return PredictResponse(
**cached_result,
request_id=req_id,
cached=True
)
# Make prediction
result = model_instance.predict(
predict_request.prompt,
predict_request.response,
predict_request.question
)
# Cache the result if caching is enabled
if predict_request.use_cache:
advanced_cache.set(
predict_request.prompt,
predict_request.response,
predict_request.question,
result
)
# Store in database
try:
db = SessionLocal()
pred = Prediction(
id=req_id,
prompt=predict_request.prompt,
response=predict_request.response,
question=predict_request.question,
is_hallucination=result["is_hallucination"],
confidence_score=result["confidence_score"],
raw_prediction=result["raw_prediction"],
processing_time=result["processing_time"]
)
db.add(pred)
db.commit()
logger.info(f"Prediction saved: {req_id}")
except Exception as db_error:
logger.error(f"Database error: {str(db_error)}")
finally:
db.close()
return PredictResponse(
**result,
request_id=req_id,
cached=False
)
except HTTPException:
raise
except Exception as e:
logger.error(f"Prediction error: {str(e)}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Prediction failed: {str(e)}")
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