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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
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")
#--------------------------------------------------------------------------------------------
# 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.
#--------------------------------------------------------------------------------------------
# Make random predictions (placeholder for actual model inference)
#true_labels = test_dataset["label"]
#predictions = [random.randint(0, 7) for _ in range(len(true_labels))]
from transformers import DistilBertTokenizer
import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download
# Load the ONNX model and tokenizer
MODEL_REPO = "ClimateDebunk/Quantized_DistilBertForSequenceClassification"
MODEL_FILENAME = "distilbert_quantized_dynamic.onnx"
MODEL_PATH = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
ort_session = ort.InferenceSession(MODEL_PATH, providers=["CPUExecutionProvider"])
# Preprocess the text data
def preprocess(texts):
return tokenizer(
texts,
padding=True,
truncation=True,
max_length=365,
return_tensors="np"
)
# Run inference
def predict(texts):
inputs = preprocess(texts)
ort_inputs = {
"input_ids": inputs["input_ids"].astype(np.int64),
"attention_mask": inputs["attention_mask"].astype(np.int64)
}
ort_outputs = ort_session.run(None, ort_inputs)
logits = ort_outputs[0]
predictions = np.argmax(logits, axis=1)
return predictions
# Replace the random predictions with actual model predictions
texts = test_dataset["text"]
predictions = predict(texts)
true_labels = test_dataset["label"]
#--------------------------------------------------------------------------------------------
# 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 |