Update process_interview.py

process_interview.py

@@ -116,6 +116,8 @@ def load_speaker_model():
         logger.error(f"Model loading failed: {str(e)}")
         raise RuntimeError("Could not load speaker verification model")
 
+
+        
 def load_models():
     speaker_model = load_speaker_model()
     nlp = spacy.load("en_core_web_sm")
@@ -198,9 +200,9 @@ def transcribe(audio_path: str) -> Dict:
             elif result['status'] == 'error':
                 raise Exception(result['error'])
             time.sleep(5)
-        except Exception as e:
-            logger.error(f"Transcription failed: {str(e)}")
-            raise
+    except Exception as e:
+        logger.error(f"Transcription failed: {str(e)}")
+        raise
 
 def process_utterance(utterance: Dict, full_audio: AudioSegment, wav_file: str) -> Dict:
     try:
@@ -279,7 +281,7 @@ def train_role_classifier(utterances: List[Dict]):
                 sum(1 for token in doc if token.pos_ == 'NOUN')
             ])
             features.append(feat)
-            labels.append(0 if i % 2 == 0 else 1)
+            labels.append(0 if i % 2 == 0 else 1)  # Simplified for demo
         scaler = StandardScaler()
         X = scaler.fit_transform(features)
         clf = RandomForestClassifier(
@@ -322,7 +324,6 @@ def classify_roles(utterances: List[Dict], clf, vectorizer, scaler):
     except Exception as e:
         logger.error(f"Role classification failed: {str(e)}")
         raise
-
 def analyze_interviewee_voice(audio_path: str, utterances: List[Dict]) -> Dict:
     try:
         y, sr = librosa.load(audio_path, sr=16000)
@@ -386,6 +387,8 @@ def analyze_interviewee_voice(audio_path: str, utterances: List[Dict]) -> Dict:
         logger.error(f"Voice analysis failed: {str(e)}", exc_info=True)
         return {'error': f'Voice analysis incomplete due to audio processing issues: {str(e)}'}
 
+
+
 def generate_voice_interpretation(analysis: Dict) -> str:
     try:
         if 'error' in analysis:
@@ -844,7 +847,7 @@ def create_company_pdf_report(analysis_data: Dict, output_path: str, gemini_repo
                     ['Filler Words', f"{voice_analysis.get('filler_ratio', 0) * 100:.1f}%", 'High usage reduces credibility'],
                     ['Anxiety', voice_analysis.get('interpretation', {}).get('anxiety_level', 'N/A'), f"Score: {voice_analysis.get('composite_scores'), {}).get('anxiety', 0):.3f}"],
                     ['Confidence', voice_analysis.get('('interpretation', {}).get('confidence_level'), 'N/A'), f"Score: {voice_analysis.get('('composite_scores', {}).get('confidence', 0):.3f}"],
-                    ['Fluency', voice_analysis.get('interpretation', {}).get('fluency_level', 'N/A'), 'Drives engagement'],
+                    ['Fluency', voice_analysis.get('interpretation', {}).get('fluency_level', 'N/A'), 'Drives engagement'),
                 ]
                 table = Table(table_data, colWidths=[1.5*inch, 1.3*inch, 3.2*inch])
                 table.setStyle(TableStyle([