Update app.py

app.py

@@ -1,47 +1,46 @@
 from flask import Flask, request, jsonify
 from transformers import pipeline
 from transformers import AutoTokenizer, AutoModelForTokenClassification
-
-# Initialize the tokenizer and model
 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)
 
-# Load the model once at startup (better performance for multiple requests)
-model = whisper.load_model("small")
+# 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'}
 
 @app.route('/transcribe', methods=['POST'])
 def transcribe_audio():
-    # Check if a file was uploaded
     if 'file' not in request.files:
         return jsonify({'error': 'No file uploaded'}), 400
         
     file = request.files['file']
     
-    # Check if the file is empty
     if file.filename == '':
         return jsonify({'error': 'No selected file'}), 400
         
-    # Check allowed file types
     if not allowed_file(file.filename):
         return jsonify({'error': 'Unsupported file type'}), 400
 
     try:
-        # Save the temporary file
         temp_path = "temp_audio"
         file.save(temp_path)
         
-        # Transcribe the audio
-        result = model.transcribe(temp_path)
+        # Use the renamed whisper_model
+        result = whisper_model.transcribe(temp_path)
         transcription = result["text"]
         
-        # Clean up the temporary file
         if os.path.exists(temp_path):
             os.remove(temp_path)
             
@@ -49,7 +48,6 @@ def transcribe_audio():
     
     except Exception as e:
         return jsonify({'error': str(e)}), 500
-classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
 
 @app.route('/classify', methods=['POST'])
 def classify():
@@ -65,26 +63,23 @@ def classify():
     except Exception as e:
         return jsonify({"error": str(e)}), 500
 
-tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
-model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
-nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 @app.route('/ner', methods=['POST'])
 def ner_endpoint():
     try:
-        # Get text from request
         data = request.get_json()
         text = data.get("text", "")
         
-        # Perform NER
-        ner_results = nlp(text)
+        # Use the renamed ner_pipeline
+        ner_results = ner_pipeline(text)
         
-        # Extract words and their corresponding entities
         words_and_entities = [
             {"word": result['word'], "entity": result['entity']} 
             for result in ner_results
         ]
         
-        # Return JSON response with the words and their entities
         return jsonify({"entities": words_and_entities})
     except Exception as e:
         return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)