Create deep_learning_analyzer.py

src/core/deep_learning_analyzer.py

@@ -0,0 +1,56 @@
+import torch
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer, util
+from typing import List, Dict, Any
+
+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: List[Dict[str, str]]) -> List[List[Dict[str, Any]]]:
+        user_messages = [msg['content'] for msg in messages if msg['role'] == 'user']
+        if not user_messages:
+            return []
+        return self.sentiment_analyzer(user_messages)
+
+    def compute_semantic_similarity(self, messages: List[Dict[str, str]], job_requirements: str) -> float:
+        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: List[Dict[str, str]]) -> List[Dict[str, Any]]:
+        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"
+        ]
+        return self.intent_classifier(user_answers, candidate_labels, multi_label=False)
+
+    def run_full_analysis(self, conversation_history: List[Dict[str, str]], job_requirements: str) -> Dict[str, Any]:
+        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)
+        
+        return {
+            "overall_similarity_score": round(similarity_score, 2),
+            "sentiment_analysis": sentiment_results,
+            "intent_analysis": intent_results,
+            "raw_transcript": conversation_history 
+        }