tasks/text.py

from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import random
import os
import numpy as np
from huggingface_hub import PyTorchModelHubMixin
from tqdm import tqdm, trange
from sentence_transformers import SentenceTransformer


import torch
import torch.nn as nn
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import BertForPreTraining, BertModel, AutoTokenizer, AutoModel, ModernBertForSequenceClassification, BertForSequenceClassification, RobertaForSequenceClassification


from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info

router = APIRouter()

DESCRIPTION = "Submission 2: SBERT+MLP"
ROUTE = "/text"


if torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")


MODEL = "mlp" #sk, mlp, ct, modern-base, modern-large, gte-base, gte-large 

class ConspiracyClassification768(
    nn.Module,
    PyTorchModelHubMixin, 
    # optionally, you can add metadata which gets pushed to the model card
):    
    def __init__(self, num_classes=8):
        super().__init__()
        self.h1 = nn.Linear(768, 100)
        self.h2 = nn.Linear(100, 100)
        self.h3 = nn.Linear(100, 100)
        self.h4 = nn.Linear(100, 50)
        self.h5 = nn.Linear(50, num_classes)
        self.dropout = nn.Dropout(0.2)
        self.activation = nn.ReLU()

        
    def forward(self, input_texts):
        outputs = self.h1(input_texts)
        outputs = self.activation(outputs)
        outputs = self.dropout(outputs)
        outputs = self.h2(outputs)
        outputs = self.activation(outputs)
        outputs = self.dropout(outputs)
        outputs = self.h3(outputs)
        outputs = self.activation(outputs)
        outputs = self.dropout(outputs)
        outputs = self.h4(outputs)
        outputs = self.activation(outputs)
        outputs = self.dropout(outputs)
        outputs = self.h5(outputs)
        
        return outputs  

class CTBERT(
    nn.Module,
    PyTorchModelHubMixin, 
    # optionally, you can add metadata which gets pushed to the model card
):
    def __init__(self, num_classes=8):
        super().__init__()
        self.bert = BertForPreTraining.from_pretrained('digitalepidemiologylab/covid-twitter-bert-v2')    
        self.bert.cls.seq_relationship = nn.Linear(1024, num_classes)
        
    def forward(self, input_ids, input_mask, token_type_ids):
        outputs = self.bert(input_ids = input_ids, token_type_ids = token_type_ids, attention_mask = input_mask)
        logits = outputs[1]
        
        return logits 

class conspiracyModelBase(
    nn.Module,
    PyTorchModelHubMixin, 
    # optionally, you can add metadata which gets pushed to the model card
):    
    def __init__(self, num_classes=8):
        super().__init__()
        self.n_classes = num_classes
        self.bert = ModernBertForSequenceClassification.from_pretrained('answerdotai/ModernBERT-base', num_labels=num_classes)    
        
    def forward(self, input_ids, input_mask):
        outputs = self.bert(input_ids = input_ids, attention_mask = input_mask)
        
        return outputs.logits

class conspiracyModelLarge(
    nn.Module,
    PyTorchModelHubMixin, 
    # optionally, you can add metadata which gets pushed to the model card
):    
    def __init__(self, num_classes=8):
        super().__init__()
        self.n_classes = num_classes
        self.bert = ModernBertForSequenceClassification.from_pretrained('answerdotai/ModernBERT-large', num_labels=num_classes)    
        
    def forward(self, input_ids, input_mask):
        outputs = self.bert(input_ids = input_ids, attention_mask = input_mask)
        
        return outputs.logits

class gteModelLarge(
    nn.Module,
    PyTorchModelHubMixin, 
    # optionally, you can add metadata which gets pushed to the model card
):    
    def __init__(self, num_classes=8):
        super().__init__()
        self.n_classes = num_classes
        self.gte = AutoModel.from_pretrained('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)
        self.cls = nn.Linear(1024, num_classes)
        
    def forward(self, input_ids, input_mask, input_type_ids):
        outputs = self.gte(input_ids = input_ids, attention_mask = input_mask, token_type_ids = input_type_ids)
        embeddings = outputs.last_hidden_state[:, 0]
        logits = self.cls(embeddings)
        return logits

class gteModel(
    nn.Module,
    PyTorchModelHubMixin, 
    # optionally, you can add metadata which gets pushed to the model card
):    
    def __init__(self, num_classes=8):
        super().__init__()
        self.n_classes = num_classes
        self.gte = AutoModel.from_pretrained('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
        self.cls = nn.Linear(768, num_classes)
        
    def forward(self, input_ids, input_mask, input_type_ids):
        outputs = self.gte(input_ids = input_ids, attention_mask = input_mask, token_type_ids = input_type_ids)
        embeddings = outputs.last_hidden_state[:, 0]
        logits = self.cls(embeddings)
        return logits        

@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, token=os.getenv("HF_TOKEN"))

    # Convert string labels to integers
    dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})

    # Split dataset
    test_dataset = dataset["test"]

    if MODEL =="mlp":
        model = ConspiracyClassification768.from_pretrained("ypesk/frugal-ai-EURECOM-mlp-768-fullset")
        model = model.to(device)
        emb_model = SentenceTransformer("sentence-transformers/sentence-t5-large")
        batch_size = 6
    
        test_texts = torch.Tensor(emb_model.encode([t['quote'] for t in test_dataset]))
        test_data = TensorDataset(test_texts)
        test_sampler = SequentialSampler(test_data)
        test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
    elif MODEL == "sk":
        emb_model = SentenceTransformer("sentence-transformers/sentence-t5-large")
        batch_size = 512
    
        test_texts = torch.Tensor(emb_model.encode([t['quote'] for t in test_dataset]))
        test_data = TensorDataset(test_texts)
        test_sampler = SequentialSampler(test_data)
        test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
    
        model = pickle.load(open('../svm.pkl', "rb"))
    
    
    elif MODEL == "ct":
        model = CTBERT.from_pretrained("ypesk/frugal-ai-EURECOM-ct-bert-baseline")
        model = model.to(device)
        tokenizer = AutoTokenizer.from_pretrained('digitalepidemiologylab/covid-twitter-bert-fullset')
        
        test_texts = [t['quote'] for t in test_dataset]
    
        MAX_LEN = 256 #1024 # < m some tweets will be truncated
        
        tokenized_test = tokenizer(test_texts, max_length=MAX_LEN, padding='max_length', truncation=True)
        test_input_ids, test_token_type_ids, test_attention_mask = tokenized_test['input_ids'], tokenized_test['token_type_ids'], tokenized_test['attention_mask']
        test_token_type_ids = torch.tensor(test_token_type_ids)
        
        test_input_ids = torch.tensor(test_input_ids)
        test_attention_mask = torch.tensor(test_attention_mask)
    
        batch_size = 12 #
        test_data = TensorDataset(test_input_ids, test_attention_mask, test_token_type_ids)
        
        test_sampler = SequentialSampler(test_data)
        test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
    
    elif MODEL == "modern-base":
        model = conspiracyModelBase.from_pretrained("ypesk/frugal-ai-EURECOM-modern-base-fullset")
        model = model.to(device)
        tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
            
        test_texts = [t['quote'] for t in test_dataset]
    
        MAX_LEN = 256 #1024 # < m some tweets will be truncated
        
        tokenized_test = tokenizer(test_texts, max_length=MAX_LEN, padding='max_length', truncation=True)
        test_input_ids, test_attention_mask = tokenized_test['input_ids'], tokenized_test['attention_mask']
        
        test_input_ids = torch.tensor(test_input_ids)
        test_attention_mask = torch.tensor(test_attention_mask)
    
        batch_size = 12 #
        test_data = TensorDataset(test_input_ids, test_attention_mask)
        
        test_sampler = SequentialSampler(test_data)
        test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
    
    elif MODEL == "modern-large":
        model = conspiracyModelLarge.from_pretrained('ypesk/frugal-ai-EURECOM-modern-large-fullset')
        model = model.to(device)
        tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-large")
            
        test_texts = [t['quote'] for t in test_dataset]
    
        MAX_LEN = 256 #1024 # < m some tweets will be truncated
        
        tokenized_test = tokenizer(test_texts, max_length=MAX_LEN, padding='max_length', truncation=True)
        test_input_ids, test_attention_mask = tokenized_test['input_ids'], tokenized_test['attention_mask']
        
        test_input_ids = torch.tensor(test_input_ids)
        test_attention_mask = torch.tensor(test_attention_mask)
    
        batch_size = 12 #
        test_data = TensorDataset(test_input_ids, test_attention_mask)
        
        test_sampler = SequentialSampler(test_data)
        test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
    
    elif MODEL == "gte-base":
        model = gteModel.from_pretrained("ypesk/frugal-ai-EURECOM-gte-base-fullset")
        model = model.to(device)
        tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-base-en-v1.5')
            
        test_texts = [t['quote'] for t in test_dataset]
    
        MAX_LEN = 256 #1024 # < m some tweets will be truncated
        
        tokenized_test = tokenizer(test_texts, max_length=MAX_LEN, padding='max_length', truncation=True)
        test_input_ids, test_attention_mask, test_token_type_ids = tokenized_test['input_ids'], tokenized_test['attention_mask'], tokenized_test['token_type_ids']
        
        test_input_ids = torch.tensor(test_input_ids)
        test_attention_mask = torch.tensor(test_attention_mask)
        test_token_type_ids = torch.tensor(test_token_type_ids)
        
        batch_size = 12 #
        test_data = TensorDataset(test_input_ids, test_attention_mask, test_token_type_ids)
        
        test_sampler = SequentialSampler(test_data)
        test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
    
    elif MODEL == "gte-large":
        model = gteModelLarge.from_pretrained("ypesk/frugal-ai-EURECOM-gte-large-fullset")
        model = model.to(device)
        tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-large-en-v1.5')
            
        test_texts = [t['quote'] for t in test_dataset]
    
        MAX_LEN = 256 #1024 # < m some tweets will be truncated
        
        tokenized_test = tokenizer(test_texts, max_length=MAX_LEN, padding='max_length', truncation=True)
        test_input_ids, test_attention_mask, test_token_type_ids = tokenized_test['input_ids'], tokenized_test['attention_mask'], tokenized_test['token_type_ids']
        
        test_input_ids = torch.tensor(test_input_ids)
        test_attention_mask = torch.tensor(test_attention_mask)
        test_token_type_ids = torch.tensor(test_token_type_ids)
        
        batch_size = 12 #
        test_data = TensorDataset(test_input_ids, test_attention_mask, test_token_type_ids)
        
        test_sampler = SequentialSampler(test_data)
        test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)



    
    # 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.
    #--------------------------------------------------------------------------------------------   
    
    predictions = []
    model.eval()
    for batch in tqdm(test_dataloader):
        batch = tuple(t.to(device) for t in batch)
        with torch.no_grad():
            if MODEL =="mlp":
                b_texts = batch[0]
                logits = model(b_texts)
            elif MODEL == "modern-base" or MODEL=="modern-large":
                b_input_ids, b_input_mask = batch
                logits = model(b_input_ids, b_input_mask)
            elif MODEL == "gte-base" or MODEL=="gte-large" or MODEL=="ct":
                b_input_ids, b_input_mask, b_token_type_ids = batch
                logits = model(b_input_ids, b_input_mask, b_token_type_ids)
                
        logits = logits.detach().cpu().numpy()
        predictions.extend(logits.argmax(1))        
    
    true_labels = test_dataset["label"]   
    # Make random predictions (placeholder for actual model inference)
    #true_labels = test_dataset["label"]
    #predictions = [random.randint(0, 7) for _ in range(len(true_labels))]

    #--------------------------------------------------------------------------------------------
    # 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