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from flask import Flask, request, jsonify | |
from transformers import pipeline | |
from transformers import AutoTokenizer, AutoModelForTokenClassification | |
import whisper | |
import os | |
app = Flask(__name__) | |
# Initialize Whisper model | |
whisper_model = whisper.load_model("small") # Renamed variable | |
# Initialize Emotion Classifier | |
classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True) | |
# Initialize NER pipeline | |
ner_tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER") | |
ner_model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER") # Renamed variable | |
ner_pipeline = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer) # Renamed variable | |
def allowed_file(filename): | |
return '.' in filename and filename.rsplit('.', 1)[1].lower() in {'wav', 'mp3', 'ogg', 'flac', 'm4a'} | |
def transcribe_audio(): | |
if 'file' not in request.files: | |
return jsonify({'error': 'No file uploaded'}), 400 | |
file = request.files['file'] | |
if file.filename == '': | |
return jsonify({'error': 'No selected file'}), 400 | |
if not allowed_file(file.filename): | |
return jsonify({'error': 'Unsupported file type'}), 400 | |
try: | |
temp_path = "temp_audio" | |
file.save(temp_path) | |
# Use the renamed whisper_model | |
result = whisper_model.transcribe(temp_path) | |
transcription = result["text"] | |
if os.path.exists(temp_path): | |
os.remove(temp_path) | |
return jsonify({'transcription': transcription}) | |
except Exception as e: | |
return jsonify({'error': str(e)}), 500 | |
def classify(): | |
try: | |
data = request.get_json() | |
if 'text' not in data: | |
return jsonify({"error": "Missing 'text' field"}), 400 | |
text = data['text'] | |
result = classifier(text) | |
return jsonify(result) | |
except Exception as e: | |
return jsonify({"error": str(e)}), 500 | |
def ner_endpoint(): | |
try: | |
data = request.get_json() | |
text = data.get("text", "") | |
# Use the renamed ner_pipeline | |
ner_results = ner_pipeline(text) | |
words_and_entities = [ | |
{"word": result['word'], "entity": result['entity']} | |
for result in ner_results | |
] | |
return jsonify({"entities": words_and_entities}) | |
except Exception as e: | |
return jsonify({"error": str(e)}), 500 | |