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from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import numpy as np
import random
import torch
from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
router = APIRouter()
DESCRIPTION = "bert base finetuned"
ROUTE = "/text"
def baseline_model(dataset_length: int):
# Make random predictions (placeholder for actual model inference)
#predictions = [random.randint(0, 7) for _ in range(dataset_length)]
# My favorate baseline is the most common class.
predictions = [0] * dataset_length
return predictions
def bert_model(test_dataset):
print('Starting my code block.')
texts = test_dataset["quote"]
model_repo = 'Nonnormalizable/frugal-ai-text-bert-base'
config = AutoConfig.from_pretrained(model_repo)
model = AutoModelForSequenceClassification.from_pretrained(model_repo)
tokenizer = AutoTokenizer.from_pretrained(model_repo)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
print('device:', device)
model = model.to(device)
test_encoding = tokenizer(
texts,
truncation=True,
padding=True,
return_tensors='pt',
)
model.eval()
with torch.no_grad():
test_input_ids = test_encoding['input_ids'].to(device)
test_attention_mask = test_encoding['attention_mask'].to(device)
print('Starting model run.')
outputs = model(test_input_ids, test_attention_mask)
print('End of model run.')
predictions = torch.argmax(outputs.logits, dim=1)
predictions = predictions.cpu().numpy()
print('End of my code block.')
return predictions
@router.post(ROUTE, tags=["Text Task"],
description=DESCRIPTION)
async def evaluate_text(request: TextEvaluationRequest):
"""
Evaluate text classification for climate disinformation detection.
Current Model: Random Baseline
- Makes random predictions from the label space (0-7)
- Used as a baseline for comparison
"""
# Get space info
username, space_url = get_space_info()
# Define the label mapping
LABEL_MAPPING = {
"0_not_relevant": 0,
"1_not_happening": 1,
"2_not_human": 2,
"3_not_bad": 3,
"4_solutions_harmful_unnecessary": 4,
"5_science_unreliable": 5,
"6_proponents_biased": 6,
"7_fossil_fuels_needed": 7
}
# Load and prepare the dataset
dataset = load_dataset(request.dataset_name)
# Convert string labels to integers
dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})
# Split dataset
train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
test_dataset = train_test["test"]
# Start tracking emissions
tracker.start()
tracker.start_task("inference")
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE CODE HERE
# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
#--------------------------------------------------------------------------------------------
true_labels = test_dataset["label"]
#predictions = baseline_model(len(true_labels))
predictions = bert_model(test_dataset)
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE STOPS HERE
#--------------------------------------------------------------------------------------------
# Stop tracking emissions
emissions_data = tracker.stop_task()
# Calculate accuracy
accuracy = accuracy_score(true_labels, predictions)
# Prepare results dictionary
results = {
"username": username,
"space_url": space_url,
"submission_timestamp": datetime.now().isoformat(),
"model_description": DESCRIPTION,
"accuracy": float(accuracy),
"energy_consumed_wh": emissions_data.energy_consumed * 1000,
"emissions_gco2eq": emissions_data.emissions * 1000,
"emissions_data": clean_emissions_data(emissions_data),
"api_route": ROUTE,
"dataset_config": {
"dataset_name": request.dataset_name,
"test_size": request.test_size,
"test_seed": request.test_seed
}
}
return results |