[ref] remove audio and text endpoints

tasks/audio.py

@@ -1,88 +0,0 @@
-from fastapi import APIRouter
-from datetime import datetime
-from datasets import load_dataset
-from sklearn.metrics import accuracy_score
-import random
-import os
-
-from .utils.evaluation import AudioEvaluationRequest
-from .utils.emissions import tracker, clean_emissions_data, get_space_info
-
-from dotenv import load_dotenv
-load_dotenv()
-
-router = APIRouter()
-
-DESCRIPTION = "Random Baseline"
-ROUTE = "/audio"
-
-
-
-@router.post(ROUTE, tags=["Audio Task"],
-             description=DESCRIPTION)
-async def evaluate_audio(request: AudioEvaluationRequest):
-    """
-    Evaluate audio classification for rainforest sound detection.
-    
-    Current Model: Random Baseline
-    - Makes random predictions from the label space (0-1)
-    - Used as a baseline for comparison
-    """
-    # Get space info
-    username, space_url = get_space_info()
-
-    # Define the label mapping
-    LABEL_MAPPING = {
-        "chainsaw": 0,
-        "environment": 1
-    }
-    # Load and prepare the dataset
-    # Because the dataset is gated, we need to use the HF_TOKEN environment variable to authenticate
-    dataset = load_dataset(request.dataset_name,token=os.getenv("HF_TOKEN"))
-    
-    # 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
-    # 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, 1) 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 

tasks/text.py

@@ -1,92 +0,0 @@
-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"].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
-    # 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))]
-
-    #--------------------------------------------------------------------------------------------
-    # 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