Delete model-manager.py

model-manager.py

@@ -1,79 +0,0 @@
-from transformers import (
-    WhisperProcessor, WhisperForConditionalGeneration,
-    AutoModelForSequenceClassification, AutoTokenizer
-)
-import torch
-
-class ModelManager:
-    def __init__(self):
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.models = {}
-        self.tokenizers = {}
-        self.processors = {}
-        
-    def load_models(self):
-        # Load Whisper for speech recognition
-        self.processors['whisper'] = WhisperProcessor.from_pretrained("openai/whisper-base")
-        self.models['whisper'] = WhisperForConditionalGeneration.from_pretrained(
-            "openai/whisper-base"
-        ).to(self.device)
-        
-        # Load EmoBERTa for emotion detection
-        self.tokenizers['emotion'] = AutoTokenizer.from_pretrained("arpanghoshal/EmoRoBERTa")
-        self.models['emotion'] = AutoModelForSequenceClassification.from_pretrained(
-            "arpanghoshal/EmoRoBERTa"
-        ).to(self.device)
-        
-        # Load ClinicalBERT for analysis
-        self.tokenizers['clinical'] = AutoTokenizer.from_pretrained(
-            "emilyalsentzer/Bio_ClinicalBERT"
-        )
-        self.models['clinical'] = AutoModelForSequenceClassification.from_pretrained(
-            "emilyalsentzer/Bio_ClinicalBERT"
-        ).to(self.device)
-        
-    def transcribe(self, audio_input):
-        inputs = self.processors['whisper'](
-            audio_input, 
-            return_tensors="pt"
-        ).input_features.to(self.device)
-        
-        generated_ids = self.models['whisper'].generate(inputs)
-        transcription = self.processors['whisper'].batch_decode(
-            generated_ids, 
-            skip_special_tokens=True
-        )[0]
-        return transcription
-        
-    def analyze_emotions(self, text):
-        inputs = self.tokenizers['emotion'](
-            text,
-            return_tensors="pt",
-            padding=True,
-            truncation=True,
-            max_length=512
-        ).to(self.device)
-        
-        outputs = self.models['emotion'](**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        
-        emotions = ['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']
-        return {emotion: float(prob) for emotion, prob in zip(emotions, probs[0])}
-        
-    def analyze_mental_health(self, text):
-        inputs = self.tokenizers['clinical'](
-            text,
-            return_tensors="pt",
-            padding=True,
-            truncation=True,
-            max_length=512
-        ).to(self.device)
-        
-        outputs = self.models['clinical'](**inputs)
-        scores = torch.sigmoid(outputs.logits)
-        
-        return {
-            'depression_risk': float(scores[0][0]),
-            'anxiety_risk': float(scores[0][1]),
-            'stress_level': float(scores[0][2])
-        }