app.py

from flask import Flask, request, jsonify
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
import whisper
import os

import ffmpeg

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 convert_audio(input_path, output_path):
    try:
        ffmpeg.input(input_path).output(output_path, acodec='pcm_s16le').run()
        return True
    except ffmpeg.Error as e:
        print(f"FFmpeg error: {e.stderr.decode()}")
        return False
        
@app.route('/transcribe', methods=['POST'])
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)
        
        # Convert audio to a format Whisper can process
        converted_path = "converted_audio.wav"
        if not convert_audio(temp_path, converted_path):
            return jsonify({'error': 'Audio conversion failed'}), 500
        
        # Transcribe the converted audio
        result = whisper_model.transcribe(converted_path)
        transcription = result["text"]
        
        # Clean up temporary files
        if os.path.exists(temp_path):
            os.remove(temp_path)
        if os.path.exists(converted_path):
            os.remove(converted_path)
            
        return jsonify({'transcription': transcription})
    
    except Exception as e:
        return jsonify({'error': str(e)}), 500
        
@app.route('/classify', methods=['POST'])
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

@app.route('/ner', methods=['POST'])
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