| | 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"
|
| |
|
| | )
|
| |
|
| | 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 |
