| from flask import Flask, render_template, request, jsonify |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline |
| from mistral_7b import generate_text |
| import torch |
| from inference import voice_inference |
|
|
|
|
| app = Flask(__name__, static_url_path='/static') |
|
|
| |
| tokenizer_name = "gogamza/kobart-base-v2" |
| tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) |
|
|
| model_name = "/content/flask/eojin/checkpoint-142243" |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
|
|
| |
| nlg_pipeline = pipeline('translation_ko_to_ko', model=model, tokenizer=tokenizer) |
|
|
| @app.route('/') |
| def index(): |
| return render_template("index.html") |
|
|
| @app.route('/voice/<sentence>') |
| def voice(sentence): |
| OUTPUT_WAV_PATH = voice_inference(sentence) |
| return OUTPUT_WAV_PATH |
|
|
| @app.route('/chatbot') |
| def chatbot(): |
| return render_template("chatbot.html") |
|
|
| |
| @app.route('/process_input/<input_text>') |
| def process_input(input_text): |
| try: |
| print("input_text", input_text, "==========================================") |
| |
| |
| with torch.no_grad(): |
| answer = generate_text(input_text) |
| |
| print(answer, "=============================================") |
| |
| |
| jeju_answer = nlg_pipeline(answer, max_length=60)[0]['translation_text'] |
|
|
| print(jeju_answer, "=============================================") |
| |
| |
| return jsonify({'answer': answer, 'jeju_answer': jeju_answer}) |
| |
| except Exception as e: |
| print("Exception:", str(e)) |
| |
| return jsonify({'error': str(e)}) |
|
|
|
|
| if __name__ == '__main__': |
| app.run(debug=True, host='0.0.0.0', port=8000) |
|
|
