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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
#packages needed for inference
from sentence_transformers import SentenceTransformer
from xgboost import XGBClassifier
import pickle
import torch
import os
router = APIRouter()
DESCRIPTION = "Embedding + Neural Network"
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"].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
# Set the device to MPS (if available)
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
print(f"Using device: {device}")
model_name = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2" # You can use other Sentence Transformers models as needed
sentence_model = SentenceTransformer(model_name)
# Convert each sentence into a vector representation (embedding)
embeddings = sentence_model.encode(test_dataset['quote'], convert_to_tensor=True)
# Make random predictions (placeholder for actual model inference)
true_labels = test_dataset["label"]
"""
from torch import nn, optim
class SimpleNN2(nn.Module):
def __init__(self, input_dim, output_dim):
super(SimpleNN2, self).__init__()
self.fc1 = nn.Linear(input_dim, 128) # Reduce hidden units
self.fc2 = nn.Linear(128, 64) # Further reduce units
self.fc3 = nn.Linear(64, output_dim)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.3) # Add dropout
self.batch_norm1 = nn.BatchNorm1d(128)
self.batch_norm2 = nn.BatchNorm1d(64)
def forward(self, x):
x = self.relu(self.batch_norm1(self.fc1(x)))
x = self.dropout(x) # Apply dropout
x = self.relu(self.batch_norm2(self.fc2(x)))
x = self.dropout(x) # Apply dropout
x = self.fc3(x) # Output raw logits
return x
"""
current_file_path = os.path.abspath(__file__)
current_dir = os.path.dirname(current_file_path)
# model_nn = torch.load(os.path.join(current_dir,"model_nn.pth"), map_location=device)
model_nn = torch.jit.load(os.path.join(current_dir,"model_nn_scripted.pth"), map_location=device)
# Set the model to evaluation mode
model_nn.eval()
# Make predictions
with torch.no_grad():
outputs = model_nn(embeddings)
_, predicted = torch.max(outputs, 1) # Get the class with the highest score
# Decode the predictions back to original labels using label_encoder
predictions = predicted.cpu().numpy()
#--------------------------------------------------------------------------------------------
# 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 |