Create app.py

app.py

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+import yaml
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load test data from YAML file
+with open("test_data.yaml", "r") as file:
+    test_data = yaml.safe_load(file)["test_data"]
+
+# Load pre-trained model and tokenizer
+model_name = "distilbert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+# Load your fine-tuned model weights
+model.load_state_dict(torch.load("path/to/your/fine-tuned/model.pth"))
+model.eval()
+
+# Evaluate on test data
+correct_predictions = 0
+total_samples = 0
+
+for sample in test_data:
+    text = sample["text"]
+    expected_label = sample["label"]
+
+    # Tokenize and encode input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+    # Get model predictions
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_label = "Positive" if logits.argmax().item() else "Negative"
+
+    # Check if prediction matches expected label
+    if predicted_label == expected_label:
+        correct_predictions += 1
+    total_samples += 1
+
+# Calculate accuracy
+accuracy = correct_predictions / total_samples
+print(f"Accuracy on test data: {accuracy * 100:.2f}%")
+
+# Demonstrate model predictions
+print("\nModel Predictions:")
+for sample in test_data:
+    text = sample["text"]
+    expected_label = sample["label"]
+
+    # Tokenize and encode input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+    # Get model predictions
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_label = "Positive" if logits.argmax().item() else "Negative"
+
+    print(f"Text: {text}")
+    print(f"Expected Label: {expected_label}")
+    print(f"Predicted Label: {predicted_label}")
+    print()