Create text2.py

tasks/text2.py

@@ -0,0 +1,148 @@
+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