Update tasks/text.py

tasks/text.py

@@ -1,23 +1,103 @@
-from fastapi import APIRouter
+from fastapi import APIRouter, Query
 from datetime import datetime
 from datasets import load_dataset
 from sklearn.metrics import accuracy_score
+import numpy as np
 import random
+import torch
+from torch.utils.data import Dataset, DataLoader
+from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
 
 from .utils.evaluation import TextEvaluationRequest
 from .utils.emissions import tracker, clean_emissions_data, get_space_info
 
 router = APIRouter()
 
-DESCRIPTION = "Random Baseline"
+MODEL_TYPE = "bert-mini"
+DESCRIPTIONS = {
+    "baseline": "baseline most common class",
+    "bert-base": "bert base fine tuned on just training data, Nvidia T4 small",
+    "bert-medium": "bert medium fine tuned on just training data, Nvidia T4 small",
+    "bert-small": "bert small fine tuned on just training data, Nvidia T4 small",
+    "bert-mini": "bert mini fine tuned on just training data, Nvidia T4 small",
+    "bert-tiny": "bert tiny fine tuned on just training data, Nvidia T4 small",
+}
 ROUTE = "/text"
 
-@router.post(ROUTE, tags=["Text Task"], 
-             description=DESCRIPTION)
-async def evaluate_text(request: TextEvaluationRequest):
+
+class TextDataset(Dataset):
+    def __init__(self, texts, tokenizer, max_length=256):
+        self.texts = texts
+        self.encodings = tokenizer(
+            texts,
+            truncation=True,
+            padding=True,
+            max_length=max_length,
+            return_tensors="pt",
+        )
+
+    def __getitem__(self, idx):
+        item = {key: val[idx] for key, val in self.encodings.items()}
+        return item
+
+    def __len__(self) -> int:
+        return len(self.texts)
+
+
+def baseline_model(dataset_length: int):
+    # Make random predictions (placeholder for actual model inference)
+    # predictions = [random.randint(0, 7) for _ in range(dataset_length)]
+
+    # My favorite baseline is the most common class.
+    predictions = [0] * dataset_length
+
+    return predictions
+
+
+def bert_model(test_dataset: dict, model_type: str):
+    print("Starting my code block.")
+    texts = test_dataset["quote"]
+
+    model_repo = f"Nonnormalizable/frugal-ai-text-{model_type}"
+    print(f"Loading from model_repo: {model_repo}")
+    config = AutoConfig.from_pretrained(model_repo)
+    model = AutoModelForSequenceClassification.from_pretrained(model_repo)
+    tokenizer = AutoTokenizer.from_pretrained(model_repo)
+
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+    print("Using device:", device)
+    model = model.to(device)
+    dataset = TextDataset(texts, tokenizer=tokenizer)
+    dataloader = DataLoader(dataset, batch_size=32, shuffle=False)
+    model.eval()
+    with torch.no_grad():
+        print("Starting model run.")
+        predictions = np.array([])
+        for batch in dataloader:
+            test_input_ids = batch["input_ids"].to(device)
+            test_attention_mask = batch["attention_mask"].to(device)
+            outputs = model(test_input_ids, test_attention_mask)
+            p = torch.argmax(outputs.logits, dim=1)
+            predictions = np.append(predictions, p.cpu().numpy())
+        print("End of model run.")
+
+    print("End of my code block.")
+    return predictions
+
+
+@router.post(ROUTE, tags=["Text Task"])
+async def evaluate_text(
+    request: TextEvaluationRequest,
+    model_type: str = MODEL_TYPE,
+    # This should be an API query parameter, but it looks like the submission repo
+    # https://huggingface.co/spaces/frugal-ai-challenge/submission-portal
+    # is built in a way to not accept any other endpoints or parameters.
+):
     """
     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
@@ -34,7 +114,7 @@ async def evaluate_text(request: TextEvaluationRequest):
         "4_solutions_harmful_unnecessary": 4,
         "5_science_unreliable": 5,
         "6_proponents_biased": 6,
-        "7_fossil_fuels_needed": 7
+        "7_fossil_fuels_needed": 7,
     }
 
     # Load and prepare the dataset
@@ -44,39 +124,44 @@ async def evaluate_text(request: TextEvaluationRequest):
     dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})
 
     # Split dataset
-    train_test = dataset["train"]
-    test_dataset = dataset["test"]
-    
+    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))]
+    if model_type == "baseline":
+        predictions = baseline_model(len(true_labels))
+    elif model_type[:5] == "bert-":
+        predictions = bert_model(test_dataset, model_type)
+    else:
+        raise ValueError(model_type)
 
-    #--------------------------------------------------------------------------------------------
+    # --------------------------------------------------------------------------------------------
     # 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,
+        "model_description": DESCRIPTIONS[model_type],
         "accuracy": float(accuracy),
         "energy_consumed_wh": emissions_data.energy_consumed * 1000,
         "emissions_gco2eq": emissions_data.emissions * 1000,
@@ -85,8 +170,8 @@ async def evaluate_text(request: TextEvaluationRequest):
         "dataset_config": {
             "dataset_name": request.dataset_name,
             "test_size": request.test_size,
-            "test_seed": request.test_seed
-        }
+            "test_seed": request.test_seed,
+        },
     }
-    
-    return results 
+
+    return results