Update process_interview.py

process_interview.py

@@ -231,6 +231,27 @@ def analyze_interviewee_voice(audio_path: str, utterances: List[Dict]) -> Dict:
         logger.error(f"Voice analysis failed: {str(e)}")
         return {'error': str(e)}
 
+
+def analyze_text_content(utterances: List[Dict]) -> Dict:
+    interviewee_utterances = [u['text'] for u in utterances if u.get('role') == 'Interviewee']
+    if not interviewee_utterances:
+        return {"overall_sentiment": {"label": "NEUTRAL", "score": 1.0}, "mentioned_technologies": [], "mentioned_soft_skills": []}
+    
+    full_text = " ".join(interviewee_utterances)
+    sentiment_results = sentiment_pipeline(full_text, truncation=True, max_length=512)
+    
+    tech_keywords = ['python', 'react', 'aws', 'docker', 'api', 'fastapi', 'machine learning', 'pytorch', 'tensorflow']
+    soft_skills = ['leadership', 'teamwork', 'communication', 'problem solving', 'management', 'planning']
+    
+    found_tech = [kw for kw in tech_keywords if kw in full_text.lower()]
+    found_skills = [skill for skill in soft_skills if skill in full_text.lower()]
+    
+    return {
+        "overall_sentiment": sentiment_results[0],
+        "mentioned_technologies": list(set(found_tech)),
+        "mentioned_soft_skills": list(set(found_skills))
+    }
+
 def calculate_acceptance_probability(analysis_data: Dict) -> float:
     # Your full, detailed function
     voice = analysis_data.get('voice_analysis', {})