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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 |