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#!/usr/bin/env python
import sys
sys.path.append('/home/fwl/src/textmining')
from web_app.model import CEFRModel
import torch
print("Loading model...")
model = CEFRModel(model_path='runs/metric-proto-k3/metric_proto.pt')
# Test simple sentence
sentences = ["Jag är bra."]
print(f"\nTesting: {sentences}")
# Tokenize
encoded = model.tokenize(sentences)
input_ids = encoded["input_ids"].to(model.device)
attention_mask = encoded["attention_mask"].to(model.device)
print(f"Input shape: {input_ids.shape}")
print(f"Device: {model.device}")
# Predict
with torch.no_grad():
logits = model.model(input_ids, attention_mask)["logits"]
print(f"Logits shape: {logits.shape}")
print(f"Logits: {logits}")
probs = torch.softmax(logits, dim=1)
print(f"Probs shape: {probs.shape}")
print(f"Probs: {probs}")
predictions = torch.argmax(logits, dim=1)
print(f"Predictions: {predictions}")
# Test different ways to extract confidence
cpu_probs = probs.cpu()
for i, pred in enumerate(predictions.cpu().numpy()):
print(f"\nSentence {i}: '{sentences[i]}'")
print(f" Predicted class: {pred}")
print(f" Predicted level: {model.id_to_label[pred]}")
print(f" Method 1 - probs[i][pred]: {probs[i][pred].item()}")
print(f" Method 2 - cpu_probs[i][pred]: {cpu_probs[i][pred].item()}")
print(f" Method 3 - float(cpu_probs[i][pred].item()): {float(cpu_probs[i][pred].item())}")
# Test using predict_batch
print("\n" + "="*60)
print("Using predict_batch method:")
results = model.predict_batch(sentences)
for sent, (level, conf) in zip(sentences, results):
print(f" {level} ({conf*100:.1f}%): {sent}")
# Test using predict_sentence
print("\n" + "="*60)
print("Using predict_sentence method:")
level, conf = model.predict_sentence(sentences[0])
print(f" {level} ({conf*100:.1f}%): {sentences[0]}")
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