Create onnx_inference.py

onnx_inference.py

@@ -0,0 +1,103 @@
+import numpy as np
+import json
+from huggingface_hub import hf_hub_download
+import re
+import emoji
+from transformers import BertTokenizer
+import onnxruntime as ort
+
+def preprocess_text(text):
+    """Preprocess the input text to match training conditions."""
+    text = re.sub(r'u/\w+', '[USER]', text)
+    text = re.sub(r'r/\w+', '[SUBREDDIT]', text)
+    text = re.sub(r'http[s]?://\S+', '[URL]', text)
+    text = emoji.demojize(text, delimiters=(" ", " "))
+    text = text.lower()
+    return text
+
+def load_model_and_resources():
+    """Load the ONNX model, tokenizer, emotion labels, and thresholds from Hugging Face."""
+    repo_id = "logasanjeev/goemotions-bert"
+    
+    try:
+        tokenizer = BertTokenizer.from_pretrained(repo_id)
+    except Exception as e:
+        raise RuntimeError(f"Error loading tokenizer: {str(e)}")
+
+    try:
+        model_path = hf_hub_download(repo_id=repo_id, filename="model.onnx")
+        session = ort.InferenceSession(model_path)
+    except Exception as e:
+        raise RuntimeError(f"Error loading ONNX model: {str(e)}")
+
+    try:
+        thresholds_file = hf_hub_download(repo_id=repo_id, filename="optimized_thresholds.json")
+        with open(thresholds_file, "r") as f:
+            thresholds_data = json.load(f)
+        if not (isinstance(thresholds_data, dict) and "emotion_labels" in thresholds_data and "thresholds" in thresholds_data):
+            raise ValueError("Unexpected format in optimized_thresholds.json. Expected a dictionary with keys 'emotion_labels' and 'thresholds'.")
+        emotion_labels = thresholds_data["emotion_labels"]
+        thresholds = thresholds_data["thresholds"]
+    except Exception as e:
+        raise RuntimeError(f"Error loading thresholds: {str(e)}")
+
+    return session, tokenizer, emotion_labels, thresholds
+
+SESSION, TOKENIZER, EMOTION_LABELS, THRESHOLDS = None, None, None, None
+
+def predict_emotions(text):
+    """Predict emotions for the given text using the GoEmotions BERT ONNX model.
+    
+    Args:
+        text (str): The input text to analyze.
+    
+    Returns:
+        tuple: (predictions, processed_text)
+            - predictions (str): Formatted string of predicted emotions and their confidence scores.
+            - processed_text (str): The preprocessed input text.
+    """
+    global SESSION, TOKENIZER, EMOTION_LABELS, THRESHOLDS
+    
+    if SESSION is None:
+        SESSION, TOKENIZER, EMOTION_LABELS, THRESHOLDS = load_model_and_resources()
+
+    processed_text = preprocess_text(text)
+    
+    encodings = TOKENIZER(
+        processed_text,
+        padding='max_length',
+        truncation=True,
+        max_length=128,
+        return_tensors='np'
+    )
+    
+    inputs = {
+        'input_ids': encodings['input_ids'].astype(np.int64),
+        'attention_mask': encodings['attention_mask'].astype(np.int64)
+    }
+    
+    logits = SESSION.run(None, inputs)[0][0]
+    logits = 1 / (1 + np.exp(-logits))  # Sigmoid
+    
+    predictions = []
+    for i, (logit, thresh) in enumerate(zip(logits, THRESHOLDS)):
+        if logit >= thresh:
+            predictions.append((EMOTION_LABELS[i], round(logit, 4)))
+    
+    predictions.sort(key=lambda x: x[1], reverse=True)
+    
+    result = "\n".join([f"{emotion}: {confidence:.4f}" for emotion, confidence in predictions]) or "No emotions predicted."
+    return result, processed_text
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Predict emotions using the GoEmotions BERT ONNX model.")
+    parser.add_argument("text", type=str, help="The input text to analyze for emotions.")
+    args = parser.parse_args()
+    
+    result, processed = predict_emotions(args.text)
+    print(f"Input: {args.text}")
+    print(f"Processed: {processed}")
+    print("Predicted Emotions:")
+    print(result)