tasks/text.py

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


import os
import logging 
import numpy as np
print(os.getcwd())
#
from sentence_transformers import SentenceTransformer
from xgboost import XGBClassifier
import pickle

import xgboost as xgb


#logging 
logging.basicConfig(level=logging.INFO)

logging.info("LAS ESTRELLAS!!!!!")

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"].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")

    #--------------------------------------------------------------------------------------------
    # Load a pre-trained Sentence-BERT model
    print("loading model")
    model = SentenceTransformer('sentence-transformers/all-MPNET-base-v2', device='cpu')
    

    #load the models
    with open("xgb_bin.pkl","rb") as f:
        xgb_bin = pickle.load(f)
        
    with open("xgb_multi.pkl","rb") as f:
        xgb_multi = pickle.load(f)
    




    logging.info("generating embedding")

    # Generate sentence embeddings
    sentence_embeddings = model.encode(test_dataset["quote"])
    logging.info(" embedding done")
    
    
    X_train = sentence_embeddings.copy()
    
    y_train = np.array(test_dataset["label"].copy())

    #binary
    y_train_binary = y_train.copy()
    y_train_binary[y_train_binary != 0] = 1


    


    #multi class
    X_train_multi = X_train[y_train != 0]
    
    y_train_multi = y_train[y_train != 0]
    
    logging.info(f"Xtrain_multi_shape:{X_train_multi.shape}")
    logging.info(f"Xtrain shape:{X_train.shape}")

    
    
                  

    #predictions
    y_pred_bin = xgb_bin.predict(X_train)

    y_pred_multi = xgb_multi.predict(X_train_multi.reshape(-1,768)) + 1

    logging.info(f"y_pred_bin:{y_pred_bin.shape}")
    logging.info(f"y_pred_multi shape:{y_pred_multi.shape}")

    y_pred_bin[y_train_binary==1] = y_pred_multi
    
    
    



    #predictions = xgb.predict(embeddings)
        
    
    # Make random predictions (placeholder for actual model inference)
    true_labels = test_dataset["label"]
    #predictions = xgb.predict(embeddings)

    #--------------------------------------------------------------------------------------------
    # YOUR MODEL INFERENCE STOPS HERE
    #--------------------------------------------------------------------------------------------   

    
    # Stop tracking emissions
    emissions_data = tracker.stop_task()
    
    # Calculate accuracy
    accuracy = accuracy_score(true_labels, y_pred_bin)

    logging.info(f"Accuracy : {accuracy}")
    
    # 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