Update app.py

app.py

@@ -1,6 +1,5 @@
 from flask import Flask, request, jsonify
-from transformers import pipeline
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+
 import whisper
 import os
 import tempfile
@@ -12,13 +11,7 @@ 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
 
 
 @app.route('/transcribe', methods=['POST'])
@@ -47,36 +40,4 @@ def transcribe():
         print("Error:", str(e))  # Log error for debugging
         return jsonify({"error": "Internal Server Error", "details": 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
+