Delete src/deep_learning_analyzer.py

src/deep_learning_analyzer.py

@@ -1,57 +0,0 @@
-import torch
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer, util
-
-class MultiModelInterviewAnalyzer:
-    def __init__(self):
-        self.sentiment_analyzer = pipeline(
-            "text-classification",
-            model="astrosbd/french_emotion_camembert",
-            return_all_scores=True,
-            device=0 if torch.cuda.is_available() else -1,
-        )
-        self.similarity_model = SentenceTransformer('all-MiniLM-L6-v2')
-        self.intent_classifier = pipeline(
-            "zero-shot-classification",
-            model="joeddav/xlm-roberta-large-xnli"
-            #device=0 if torch.cuda.is_available() else -1,
-        )
-
-    def analyze_sentiment(self, messages):
-        user_messages = [msg['content'] for msg in messages if msg['role'] == 'user']
-        if not user_messages:
-            return []
-        sentiments = self.sentiment_analyzer(user_messages)
-        return sentiments
-
-    def compute_semantic_similarity(self, messages, job_requirements):
-        user_answers = " ".join([msg['content'] for msg in messages if msg['role'] == 'user'])
-        embedding_answers = self.similarity_model.encode(user_answers, convert_to_tensor=True)
-        embedding_requirements = self.similarity_model.encode(job_requirements, convert_to_tensor=True)
-        cosine_score = util.cos_sim(embedding_answers, embedding_requirements)
-        return cosine_score.item()
-
-    def classify_candidate_intent(self, messages):
-        user_answers = [msg['content'] for msg in messages if msg['role'] == 'user']
-        if not user_answers:
-            return []
-        candidate_labels = [
-            "parle de son expérience technique",
-            "exprime sa motivation",
-            "pose une question",
-            "exprime de l’incertitude ou du stress"
-        ]
-        classifications = self.intent_classifier(user_answers, candidate_labels, multi_label=False)
-        return classifications
-
-    def run_full_analysis(self, conversation_history, job_requirements):
-        sentiment_results = self.analyze_sentiment(conversation_history)
-        similarity_score = self.compute_semantic_similarity(conversation_history, job_requirements)
-        intent_results = self.classify_candidate_intent(conversation_history)
-        analysis_output = {
-            "overall_similarity_score": round(similarity_score, 2),
-            "sentiment_analysis": sentiment_results,
-            "intent_analysis": intent_results,
-            "raw_transcript": conversation_history 
-        }
-        return analysis_output