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from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import random
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from torch.utils.data import Dataset, DataLoader
from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
router = APIRouter()
DESCRIPTION = "Random Baseline"
ROUTE = "/text"
@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"]
test_dataset = dataset["test"]
# Start tracking emissions
tracker.start()
tracker.start_task("inference")
# Load the model and tokenizer
model_name = "Tonic/climate-guard-toxic-agent"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
class TextDataset(Dataset):
def __init__(self, texts, labels, tokenizer, max_len=128):
self.texts = texts
self.labels = labels
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.texts)
def __getitem__(self, idx):
text = self.texts[idx]
label = self.labels[idx]
encodings = self.tokenizer(
text,
max_length=self.max_len,
padding='max_length',
truncation=True,
return_tensors="pt"
)
return {
'input_ids': encodings['input_ids'].squeeze(0),
'attention_mask': encodings['attention_mask'].squeeze(0),
'labels': torch.tensor(label, dtype=torch.long)
}
# Create dataset and dataloader
test_dataset = TextDataset(texts, labels, tokenizer)
test_loader = DataLoader(test_dataset, batch_size=16)
# Model inference
model.eval()
predictions = []
ground_truth = []
DEVICE = 'cpu'
with torch.no_grad():
for batch in test_loader:
input_ids = batch['input_ids'].to(DEVICE)
attention_mask = batch['attention_mask'].to(DEVICE)
labels = batch['labels'].to(DEVICE)
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
_, predicted = torch.max(outputs.logits, 1)
predictions.extend(predicted.cpu().numpy())
ground_truth.extend(labels.cpu().numpy())
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE STOPS HERE
#--------------------------------------------------------------------------------------------
# Stop tracking emissions
emissions_data = tracker.stop_task()
# Calculate accuracy
accuracy = accuracy_score(ground_truth, 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 |