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import spacy
from pathlib import Path
import sys
# --- Configuration ---
# Ensure this path points to your best trained model directory
# We are using the one trained on the CPU from the previous steps.
MODEL_PATH = Path("./training_400/model-best")
# --- End Configuration ---
def load_model(path):
"""Loads the spaCy model."""
if not path.exists():
print(f"✘ Error: Model directory not found at {path.resolve()}")
print("Please ensure the path is correct and you have trained the model.")
sys.exit(1)
try:
# The CuPy warnings might still appear here if CUDA PATH isn't set,
# but loading should proceed using CPU for this model.
nlp = spacy.load(path)
print(f"\n✔ Successfully loaded model from: {path.resolve()}")
return nlp
except Exception as e:
print(f"✘ Error loading model from {path.resolve()}: {e}")
print("Please ensure the model path is correct and the model files are intact (especially meta.json).")
sys.exit(1) # Exit if model can't be loaded
def predict_entities(nlp, text):
"""Processes text and prints found entities."""
if not text or text.isspace():
print("Input text is empty.")
return
# Limit display length for very long inputs in the prompt message
display_text = f"\"{text[:100]}...\"" if len(text) > 100 else f"\"{text}\""
print(f"\n---> Processing text: {display_text}")
# Process the text with the loaded NLP model
doc = nlp(text)
# Check if any entities were found
if doc.ents:
print("\n--- Entities Found ---")
for ent in doc.ents:
print(f" Text: '{ent.text}'")
print(f" Label: {ent.label_}")
print(f" Start: {ent.start_char}, End: {ent.end_char}")
print("-" * 25) # Separator between entities
else:
print("\n--- No entities found in this text. ---")
print("=" * 40) # Separator between different predictions
def main():
"""Main function to load model and run interactive prediction loop."""
nlp_model = load_model(MODEL_PATH)
print("\n==============================")
print(" Interactive NER Predictor")
print("==============================")
print(f"Model loaded: {MODEL_PATH.name}")
print("Enter Tamil text below to identify entities.")
print("Type 'quit' or 'exit' (or just press Enter on an empty line) to stop.")
print("-" * 40)
while True:
try:
# Get input from the user
user_input = input("Enter text >> ")
# Check for exit conditions
if user_input.lower() in ["quit", "exit", ""]:
print("\nExiting predictor.")
break
# Perform prediction
predict_entities(nlp_model, user_input)
except EOFError: # Handle Ctrl+D if used in some terminals
print("\nExiting predictor.")
break
except KeyboardInterrupt: # Handle Ctrl+C cleanly
print("\nExiting predictor.")
break
except Exception as e:
print(f"\nAn unexpected error occurred: {e}")
# Optionally continue or break based on error severity
# break
if __name__ == "__main__":
main() |