process_interview.py

import os
import torch
import numpy as np
import uuid
import requests
import time
import json
from pydub import AudioSegment
import wave
from nemo.collections.asr.models import EncDecSpeakerLabelModel
from pinecone import Pinecone, ServerlessSpec
import librosa
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.feature_extraction.text import TfidfVectorizer
import re
from typing import Dict, List, Tuple
import logging
import tempfile
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, PageBreak, Image
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from reportlab.lib import colors
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')
import io
from transformers import AutoTokenizer, AutoModel
import spacy
import google.generativeai as genai
import joblib
from concurrent.futures import ThreadPoolExecutor

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
logging.getLogger("nemo_logger").setLevel(logging.WARNING)

# Configuration
AUDIO_DIR = "./Uploads"
OUTPUT_DIR = "./processed_audio"
os.makedirs(OUTPUT_DIR, exist_ok=True)

# API Keys
PINECONE_KEY = os.getenv("PINECONE_KEY", "your-pinecone-key")
ASSEMBLYAI_KEY = os.getenv("ASSEMBLYAI_KEY", "your-assemblyai-key")
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "your-gemini-key")

def validate_url(url: str) -> bool:
    """Check if the URL is accessible."""
    try:
        response = requests.head(url, timeout=5)
        return response.status_code == 200
    except requests.RequestException as e:
        logger.error(f"URL validation failed for {url}: {str(e)}")
        return False

def download_audio_from_url(url: str) -> str:
    """Downloads an audio file from a URL to a temporary local path."""
    if not validate_url(url):
        logger.error(f"Invalid or inaccessible URL: {url}")
        raise ValueError(f"Audio file not found at {url}")
    try:
        temp_dir = tempfile.gettempdir()
        temp_path = os.path.join(temp_dir, f"{uuid.uuid4()}.tmp_audio")
        logger.info(f"Downloading audio from {url} to {temp_path}")
        with requests.get(url, stream=True, timeout=10) as r:
            r.raise_for_status()
            with open(temp_path, 'wb') as f:
                for chunk in r.iter_content(chunk_size=8192):
                    f.write(chunk)
        return temp_path
    except requests.HTTPError as e:
        logger.error(f"HTTP error downloading audio from {url}: {str(e)}")
        raise
    except Exception as e:
        logger.error(f"Failed to download audio from URL {url}: {str(e)}")
        raise

def initialize_services():
    try:
        pc = Pinecone(api_key=PINECONE_KEY)
        index_name = "interview-speaker-embeddings"
        if index_name not in pc.list_indexes().names():
            pc.create_index(
                name=index_name,
                dimension=192,
                metric="cosine",
                spec=ServerlessSpec(cloud="aws", region="us-east-1")
            )
        index = pc.Index(index_name)
        genai.configure(api_key=GEMINI_API_KEY)
        gemini_model = genai.GenerativeModel('gemini-1.5-flash')
        return index, gemini_model
    except Exception as e:
        logger.error(f"Error initializing services: {str(e)}")
        raise

index, gemini_model = initialize_services()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info(f"Using device: {device}")

def load_speaker_model():
    try:
        torch.set_num_threads(5)
        model = EncDecSpeakerLabelModel.from_pretrained(
            "nvidia/speakerverification_en_titanet_large",
            map_location=device
        )
        model.eval()
        return model
    except Exception as e:
        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")
    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
    llm_model = AutoModel.from_pretrained("distilbert-base-uncased").to(device)
    llm_model.eval()
    return speaker_model, nlp, tokenizer, llm_model

speaker_model, nlp, tokenizer, llm_model = load_models()

def convert_to_wav(audio_path: str, output_dir: str = OUTPUT_DIR) -> str:
    try:
        audio = AudioSegment.from_file(audio_path)
        if audio.channels > 1:
            audio = audio.set_channels(1)
        audio = audio.set_frame_rate(16000)
        wav_file = os.path.join(output_dir, f"{uuid.uuid4()}.wav")
        audio.export(wav_file, format="wav")
        return wav_file
    except Exception as e:
        logger.error(f"Audio conversion failed: {str(e)}")
        raise

def extract_prosodic_features(audio_path: str, start_ms: int, end_ms: int) -> Dict:
    try:
        audio = AudioSegment.from_file(audio_path)
        segment = audio[start_ms:end_ms]
        temp_path = os.path.join(OUTPUT_DIR, f"temp_{uuid.uuid4()}.wav")
        segment.export(temp_path, format="wav")
        y, sr = librosa.load(temp_path, sr=16000)
        pitches = librosa.piptrack(y=y, sr=sr)[0]
        pitches = pitches[pitches > 0]
        features = {
            'duration': (end_ms - start_ms) / 1000,
            'mean_pitch': float(np.mean(pitches)) if len(pitches) > 0 else 0.0,
            'min_pitch': float(np.min(pitches)) if len(pitches) > 0 else 0.0,
            'max_pitch': float(np.max(pitches)) if len(pitches) > 0 else 0.0,
            'pitch_sd': float(np.std(pitches)) if len(pitches) > 0 else 0.0,
            'intensityMean': float(np.mean(librosa.feature.rms(y=y)[0])),
            'intensityMin': float(np.min(librosa.feature.rms(y=y)[0])),
            'intensityMax': float(np.max(librosa.feature.rms(y=y)[0])),
            'intensitySD': float(np.std(librosa.feature.rms(y=y)[0])),
        }
        os.remove(temp_path)
        return features
    except Exception as e:
        logger.error(f"Feature extraction failed: {str(e)}")
        return {
            'duration': 0.0, 'mean_pitch': 0.0, 'min_pitch': 0.0, 'max_pitch': 0.0,
            'pitch_sd': 0.0, 'intensityMean': 0.0, 'intensityMin': 0.0,
            'intensityMax': 0.0, 'intensitySD': 0.0
        }

def transcribe(audio_path: str) -> Dict:
    try:
        with open(audio_path, 'rb') as f:
            upload_response = requests.post(
                "https://api.assemblyai.com/v2/upload",
                headers={"authorization": ASSEMBLYAI_KEY},
                data=f
            )
        audio_url = upload_response.json()['upload_url']
        transcript_response = requests.post(
            "https://api.assemblyai.com/v2/transcript",
            headers={"authorization": ASSEMBLYAI_KEY},
            json={
                "audio_url": audio_url,
                "speaker_labels": True,
                "filter_profanity": True
            }
        )
        transcript_id = transcript_response.json()['id']
        while True:
            result = requests.get(
                f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
                headers={"authorization": ASSEMBLYAI_KEY}
            ).json()
            if result['status'] == 'completed':
                return result
            elif result['status'] == 'error':
                raise Exception(result['error'])
            time.sleep(5)
    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:
        start = utterance['start']
        end = utterance['end']
        segment = full_audio[start:end]
        temp_path = os.path.join(OUTPUT_DIR, f"temp_{uuid.uuid4()}.wav")
        segment.export(temp_path, format="wav")
        with torch.no_grad():
            embedding = speaker_model.get_embedding(temp_path).cpu().numpy()
        embedding_list = embedding.flatten().tolist()
        query_result = index.query(
            vector=embedding_list,
            top_k=1,
            include_metadata=True
        )
        if query_result['matches'] and query_result['matches'][0]['score'] > 0.7:
            speaker_id = query_result['matches'][0]['id']
            speaker_name = query_result['matches'][0]['metadata']['speaker_name']
        else:
            speaker_id = f"unknown_{uuid.uuid4().hex[:6]}"
            speaker_name = f"Speaker_{speaker_id[-4:]}"
            index.upsert([(speaker_id, embedding_list, {"speaker_name": speaker_name})])
        os.remove(temp_path)
        return {
            **utterance,
            'speaker': speaker_name,
            'speaker_id': speaker_id,
            'embedding': embedding_list
        }
    except Exception as e:
        logger.error(f"Utterance processing failed: {str(e)}")
        return {
            **utterance,
            'speaker': 'Unknown',
            'speaker_id': 'unknown',
            'embedding': None
        }

def identify_speakers(transcript: Dict, wav_file: str) -> List[Dict]:
    try:
        full_audio = AudioSegment.from_wav(wav_file)
        utterances = transcript['utterances']
        with ThreadPoolExecutor(max_workers=5) as executor:
            futures = [
                executor.submit(process_utterance, utterance, full_audio, wav_file)
                for utterance in utterances
            ]
            results = [f.result() for f in futures]
        return results
    except Exception as e:
        logger.error(f"Speaker identification failed: {str(e)}")
        raise

def train_role_classifier(utterances: List[Dict]):
    try:
        texts = [u['text'] for u in utterances]
        vectorizer = TfidfVectorizer(max_features=500, ngram_range=(1, 2))
        X_text = vectorizer.fit_transform(texts)
        features = []
        labels = []
        for i, utterance in enumerate(utterances):
            prosodic = utterance['prosodic_features']
            feat = [
                prosodic['duration'], prosodic['mean_pitch'], prosodic['min_pitch'],
                prosodic['max_pitch'], prosodic['pitch_sd'], prosodic['intensityMean'],
                prosodic['intensityMin'], prosodic['intensityMax'], prosodic['intensitySD'],
            ]
            feat.extend(X_text[i].toarray()[0].tolist())
            doc = nlp(utterance['text'])
            feat.extend([
                int(utterance['text'].endswith('?')),
                len(re.findall(r'\b(why|how|what|when|where|who|which)\b', utterance['text'].lower())),
                len(utterance['text'].split()),
                sum(1 for token in doc if token.pos_ == 'VERB'),
                sum(1 for token in doc if token.pos_ == 'NOUN')
            ])
            features.append(feat)
            labels.append(0 if i % 2 == 0 else 1)  # Simplified for demo
        scaler = StandardScaler()
        X = scaler.fit_transform(features)
        clf = RandomForestClassifier(
            n_estimators=150, max_depth=10, random_state=42, class_weight='balanced'
        )
        clf.fit(X, labels)
        joblib.dump(clf, os.path.join(OUTPUT_DIR, 'role_classifier.pkl'))
        joblib.dump(vectorizer, os.path.join(OUTPUT_DIR, 'text_vectorizer.pkl'))
        joblib.dump(scaler, os.path.join(OUTPUT_DIR, 'feature_scaler.pkl'))
        return clf, vectorizer, scaler
    except Exception as e:
        logger.error(f"Classifier training failed: {str(e)}")
        raise

def classify_roles(utterances: List[Dict], clf, vectorizer, scaler):
    try:
        texts = [u['text'] for u in utterances]
        X_text = vectorizer.transform(texts)
        results = []
        for i, utterance in enumerate(utterances):
            prosodic = utterance['prosodic_features']
            feat = [
                prosodic['duration'], prosodic['mean_pitch'], prosodic['min_pitch'],
                prosodic['max_pitch'], prosodic['pitch_sd'], prosodic['intensityMean'],
                prosodic['intensityMin'], prosodic['intensityMax'], prosodic['intensitySD'],
            ]
            feat.extend(X_text[i].toarray()[0].tolist())
            doc = nlp(utterance['text'])
            feat.extend([
                int(utterance['text'].endswith('?')),
                len(re.findall(r'\b(why|how|what|when|where|who|which)\b', utterance['text'].lower())),
                len(utterance['text'].split()),
                sum(1 for token in doc if token.pos_ == 'VERB'),
                sum(1 for token in doc if token.pos_ == 'NOUN')
            ])
            X = scaler.transform([feat])
            role = 'Interviewer' if clf.predict(X)[0] == 0 else 'Interviewee'
            results.append({**utterance, 'role': role})
        return results
    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)
        interviewee_utterances = [u for u in utterances if u['role'] == 'Interviewee']
        if not interviewee_utterances:
            logger.warning("No interviewee utterances found")
            return {'error': 'No interviewee utterances found'}
        segments = []
        for u in interviewee_utterances:
            start = int(u['start'] * sr / 1000)
            end = int(u['end'] * sr / 1000)
            if end > start and len(y[start:end]) > 0:
                segments.append(y[start:end])
            else:
                logger.warning(f"Invalid segment for utterance: start={start}, end={end}")
        if not segments:
            logger.warning("No valid audio segments for voice analysis")
            return {'error': 'No valid audio segments found'}
        total_duration = sum(u['prosodic_features']['duration'] for u in interviewee_utterances)
        total_words = sum(len(u['text'].split()) for u in interviewee_utterances)
        speaking_rate = total_words / total_duration if total_duration > 0 else 0
        filler_words = ['um', 'uh', 'like', 'you know', 'so', 'i mean']
        filler_count = sum(sum(u['text'].lower().count(fw) for fw in filler_words) for u in interviewee_utterances)
        filler_ratio = filler_count / total_words if total_words > 0 else 0
        all_words = ' '.join(u['text'].lower() for u in interviewee_utterances).split()
        word_counts = {}
        for i in range(len(all_words) - 1):
            bigram = (all_words[i], all_words[i + 1])
            word_counts[bigram] = word_counts.get(bigram, 0) + 1
        repetition_score = sum(1 for count in word_counts.values() if count > 1) / len(word_counts) if word_counts else 0
        pitches = []
        for segment in segments:
            f0, voiced_flag, _ = librosa.pyin(segment, fmin=80, fmax=300, sr=sr)
            pitches.extend(f0[voiced_flag])
        pitch_mean = np.mean(pitches) if len(pitches) > 0 else 0
        pitch_std = np.std(pitches) if len(pitches) > 0 else 0
        jitter = np.mean(np.abs(np.diff(pitches))) / pitch_mean if len(pitches) > 1 and pitch_mean > 0 else 0
        intensities = []
        for segment in segments:
            rms = np.mean(librosa.feature.rms(y=segment)[0]) if len(segment) > 0 else 0.0
            intensities.append(float(rms))  # Fix: Use append instead of extend
        intensity_mean = np.mean(intensities) if intensities else 0
        intensity_std = np.std(intensities) if intensities else 0
        shimmer = np.mean(np.abs(np.diff(intensities))) / intensity_mean if len(intensities) > 1 and intensity_mean > 0 else 0
        anxiety_score = 0.6 * (pitch_std / pitch_mean) + 0.4 * (jitter + shimmer) if pitch_mean > 0 else 0
        confidence_score = 0.7 * (1 / (1 + intensity_std)) + 0.3 * (1 / (1 + filler_ratio))
        hesitation_score = filler_ratio + repetition_score
        anxiety_level = 'High' if anxiety_score > 0.15 else 'Moderate' if anxiety_score > 0.07 else 'Low'
        confidence_level = 'High' if confidence_score > 0.7 else 'Moderate' if confidence_score > 0.5 else 'Low'
        fluency_level = 'Fluent' if (filler_ratio < 0.05 and repetition_score < 0.1) else 'Moderate' if (filler_ratio < 0.1 and repetition_score < 0.2) else 'Disfluent'
        return {
            'speaking_rate': float(round(speaking_rate, 2)),
            'filler_ratio': float(round(filler_ratio, 4)),
            'repetition_score': float(round(repetition_score, 4)),
            'pitch_analysis': {'mean': float(round(pitch_mean, 2)), 'std_dev': float(round(pitch_std, 2)), 'jitter': float(round(jitter, 4))},
            'intensity_analysis': {'mean': float(round(intensity_mean, 2)), 'std_dev': float(round(intensity_std, 2)), 'shimmer': float(round(shimmer, 4))},
            'composite_scores': {'anxiety': float(round(anxiety_score, 4)), 'confidence': float(round(confidence_score, 4)), 'hesitation': float(round(hesitation_score, 4))},
            'interpretation': {'anxiety_level': anxiety_level, 'confidence_level': confidence_level, 'fluency_level': fluency_level}
        }
    except Exception as e:
        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:
    if 'error' in analysis:
        return f"Voice analysis unavailable: {analysis['error']}"
    interpretation_lines = [
        f"- Speaking Rate: {analysis['speaking_rate']} words/sec (Benchmark: 2.0-3.0 wps; affects clarity)",
        f"- Filler Words: {analysis['filler_ratio'] * 100:.1f}% (High usage reduces credibility)",
        f"- Anxiety: {analysis['interpretation']['anxiety_level']} (Score: {analysis['composite_scores']['anxiety']:.3f}; stress response)",
        f"- Confidence: {analysis['interpretation']['confidence_level']} (Score: {analysis['composite_scores']['confidence']:.3f}; vocal strength)",
        f"- Fluency: {analysis['interpretation']['fluency_level']} (Drives engagement)",
        "",
        "HR Insights:",
        "- Rapid speech (>3.0 wps) may reduce clarity; slower pacing enhances professionalism.",
        "- High filler word usage undermines perceived confidence.",
        "- Elevated anxiety suggests pressure; training can improve resilience.",
        "- Strong confidence supports leadership presence.",
        "- Fluent speech enhances engagement in team settings."
    ]
    return "\n".join(interpretation_lines)

def generate_anxiety_confidence_chart(composite_scores: Dict, chart_buffer):
    try:
        labels = ['Anxiety', 'Confidence']
        scores = [composite_scores.get('anxiety', 0), composite_scores.get('confidence', 0)]
        fig, ax = plt.subplots(figsize=(5, 3.5))
        bars = ax.bar(labels, scores, color=['#FF5252', '#26A69A'], edgecolor='black', width=0.45)
        ax.set_ylabel('Score', fontsize=12)
        ax.set_title('Vocal Dynamics: Anxiety vs. Confidence', fontsize=14, pad=15)
        ax.set_ylim(0, 1.3)
        for bar in bars:
            height = bar.get_height()
            ax.text(bar.get_x() + bar.get_width()/2, height + 0.05, f"{height:.2f}",
                    ha='center', color='black', fontweight='bold', fontsize=10)
        ax.grid(True, axis='y', linestyle='--', alpha=0.7)
        plt.tight_layout()
        plt.savefig(chart_buffer, format='png', bbox_inches='tight', dpi=100)
        plt.close(fig)
    except Exception as e:
        logger.error(f"Error generating chart: {str(e)}")

def calculate_acceptance_probability(analysis_data: Dict) -> float:
    voice = analysis_data.get('voice_analysis', {})
    if 'error' in voice: return 50.0
    w_confidence, w_anxiety, w_fluency, w_speaking_rate, w_filler_repetition, w_content_strengths = 0.35, -0.25, 0.2, 0.15, -0.15, 0.25
    confidence_score = voice.get('composite_scores', {}).get('confidence', 0.0)
    anxiety_score = voice.get('composite_scores', {}).get('anxiety', 0.0)
    fluency_level = voice.get('interpretation', {}).get('fluency_level', 'Disfluent')
    speaking_rate = voice.get('speaking_rate', 0.0)
    filler_ratio = voice.get('filler_ratio', 0.0)
    repetition_score = voice.get('repetition_score', 0.0)
    fluency_map = {'Fluent': 1.0, 'Moderate': 0.6, 'Disfluent': 0.2}
    fluency_val = fluency_map.get(fluency_level, 0.2)
    ideal_speaking_rate = 2.5
    speaking_rate_deviation = abs(speaking_rate - ideal_speaking_rate)
    speaking_rate_score = max(0, 1 - (speaking_rate_deviation / ideal_speaking_rate))
    filler_repetition_composite = (filler_ratio + repetition_score) / 2
    filler_repetition_score = max(0, 1 - filler_repetition_composite)
    content_strength_val = 0.85 if analysis_data.get('text_analysis', {}).get('total_duration', 0) > 60 else 0.4
    raw_score = (confidence_score * w_confidence + (1 - anxiety_score) * abs(w_anxiety) + fluency_val * w_fluency + speaking_rate_score * w_speaking_rate + filler_repetition_score * abs(w_filler_repetition) + content_strength_val * w_content_strengths)
    max_possible_score = (w_confidence + abs(w_anxiety) + w_fluency + w_speaking_rate + abs(w_filler_repetition) + w_content_strengths)
    normalized_score = raw_score / max_possible_score if max_possible_score > 0 else 0.5
    acceptance_probability = max(0.0, min(1.0, normalized_score))
    return float(f"{acceptance_probability * 100:.2f}")

def generate_report(analysis_data: Dict) -> str:
    try:
        voice = analysis_data.get('voice_analysis', {})
        voice_interpretation = generate_voice_interpretation(voice)
        interviewee_responses = [f"- {u['text']}" for u in analysis_data['transcript'] if u['role'] == 'Interviewee'][:3]
        acceptance_prob = analysis_data.get('acceptance_probability', 50.0)
        acceptance_line = f"\n**Suitability Score: {acceptance_prob:.2f}%**\n"
        if acceptance_prob >= 80:
            acceptance_line += "HR Verdict: Outstanding candidate, recommended for immediate advancement."
        elif acceptance_prob >= 60:
            acceptance_line += "HR Verdict: Strong candidate, suitable for further evaluation."
        elif acceptance_prob >= 40:
            acceptance_line += "HR Verdict: Moderate potential, needs additional assessment."
        else:
            acceptance_line += "HR Verdict: Limited fit, significant improvement required."
        prompt = f"""
        You are EvalBot, a senior HR consultant delivering a concise, professional interview analysis report. Use clear headings, bullet points ('-'), complete sentences, and formal language. Avoid redundancy, vague terms, and special characters that could break formatting. Ensure each section is unique and actionable.
        {acceptance_line}
        **1. Executive Summary**
        - Provide a narrative overview of the candidate’s performance, highlighting key strengths and fit.
        - Duration: {analysis_data['text_analysis']['total_duration']:.2f} seconds
        - Speaker Turns: {analysis_data['text_analysis']['speaker_turns']}
        - Participants: {', '.join(sorted(set(u['speaker'] for u in analysis_data['transcript'])))}
        **2. Communication and Vocal Dynamics**
        - Evaluate vocal delivery (rate, fluency, confidence) with specific insights.
        {voice_interpretation}
        **3. Competency and Content**
        - Assess leadership, problem-solving, communication, and adaptability with clear examples.
        - List strengths and growth areas separately, using quantifiable achievements where possible.
        - Sample responses:
        {chr(10).join(interviewee_responses)}
        **4. Role Fit and Potential**
        - Analyze cultural fit, role readiness, and long-term growth potential with specific alignment to role requirements.
        **5. Recommendations**
        - Provide prioritized development strategies (e.g., communication training, technical assessments).
        - Suggest specific next steps for hiring managers (e.g., advance, schedule tests).
        """
        response = gemini_model.generate_content(prompt)
        return re.sub(r'[^\x00-\x7F]+|[()]+', '', response.text)  # Sanitize non-ASCII and parentheses
    except Exception as e:
        logger.error(f"Report generation failed: {str(e)}")
        return f"Error generating report: {str(e)}"

def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text: str) -> bool:
    try:
        doc = SimpleDocTemplate(output_path, pagesize=letter,
                                rightMargin=0.75*inch, leftMargin=0.75*inch,
                                topMargin=1*inch, bottomMargin=1*inch)
        styles = getSampleStyleSheet()
        h1 = ParagraphStyle(name='Heading1', fontSize=18, leading=22, spaceAfter=16, alignment=1, textColor=colors.HexColor('#003087'), fontName='Helvetica-Bold')
        h2 = ParagraphStyle(name='Heading2', fontSize=13, leading=15, spaceBefore=10, spaceAfter=6, textColor=colors.HexColor('#0050BC'), fontName='Helvetica-Bold')
        h3 = ParagraphStyle(name='Heading3', fontSize=9, leading=11, spaceBefore=6, spaceAfter=4, textColor=colors.HexColor('#3F7CFF'), fontName='Helvetica')
        body_text = ParagraphStyle(name='BodyText', fontSize=8, leading=10, spaceAfter=4, fontName='Helvetica', textColor=colors.HexColor('#333333'))
        bullet_style = ParagraphStyle(name='Bullet', parent=body_text, leftIndent=16, bulletIndent=6, fontName='Helvetica', bulletFontName='Helvetica', bulletFontSize=8)
        
        story = []

        def header_footer(canvas, doc):
            canvas.saveState()
            canvas.setFont('Helvetica', 7)
            canvas.setFillColor(colors.HexColor('#666666'))
            canvas.drawString(doc.leftMargin, 0.5*inch, f"Page {doc.page} | EvalBot HR Interview Report | Confidential")
            canvas.setStrokeColor(colors.HexColor('#0050BC'))
            canvas.setLineWidth(0.5)
            canvas.line(doc.leftMargin, doc.height + 0.9*inch, doc.width + doc.leftMargin, doc.height + 0.9*inch)
            canvas.setFont('Helvetica-Bold', 8)
            canvas.drawString(doc.leftMargin, doc.height + 0.95*inch, "Candidate Interview Analysis")
            canvas.drawRightString(doc.width + doc.leftMargin, doc.height + 0.95*inch, time.strftime('%B %d, %Y'))
            canvas.restoreState()

        # Title Page
        story.append(Paragraph("Candidate Interview Analysis", h1))
        story.append(Paragraph(f"Generated: {time.strftime('%B %d, %Y')}", ParagraphStyle(name='Date', alignment=1, fontSize=8, textColor=colors.HexColor('#666666'), fontName='Helvetica')))
        story.append(Spacer(1, 0.3*inch))
        acceptance_prob = float(np.mean([np.mean([np.mean([analysis_data['acceptance_probability'], 0.0])])]))  # Ensure float
        story.append(Paragraph("Hiring Suitability Snapshot", h2))
        prob_color = colors.HexColor('#2E7D32') if acceptance_prob >= 80 else (colors.HexColor('#F57C00') if acceptance_prob >= 60 else colors.HexColor('#D32F2F'))
        story.append(Paragraph(f"Suitability Score: <font size=14 color='{prob_color.hexval()}'>{acceptance_prob:.2f}%</font>", 
                             ParagraphStyle(name='Prob', fontSize=10, spaceAfter=8, alignment=1, fontName='Helvetica-Bold')))
        if acceptance_prob >= 80:
            story.append(Paragraph("<b>HR Verdict:</b> Outstanding candidate, recommended for immediate advancement.", body_text))
        elif acceptance_prob >= 60:
            story.append(Paragraph("<b>HR Verdict:</b> Strong candidate, suitable for further evaluation.", body_text))
        elif acceptance_prob >= 40:
            story.append(Paragraph("<b>HR Verdict:</b> Moderate potential, needs additional assessment.", body_text))
        else:
            story.append(Paragraph("<b>HR Verdict:</b> Limited fit, significant improvement required.", body_text))
        story.append(Spacer(1, 0.2*inch))
        participants = sorted([p for p in set(u['speaker'] for u in analysis_data['transcript']) if p != 'Unknown'])
        table_data = [
            ['Metric', 'Value'],
            ['Interview Duration', f"{analysis_data['text_analysis']['total_duration']:.1f} seconds"],
            ['Speaker Turns', f"{analysis_data['text_analysis']['speaker_turns']}"],
            ['Participants', f"{', '.join(participants)}"],
        ]
        table = Table(table_data, colWidths=[2.2*inch, 3.8*inch])
        table.setStyle(TableStyle([
            ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#0050BC')),
            ('TEXTCOLOR', (0,0), (-1,0), colors.white),
            ('ALIGN', (0,0), (-1,-1), 'LEFT'),
            ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
            ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
            ('FONTSIZE', (0,0), (-1,-1), 8),
            ('BOTTOMPADDING', (0,0), (-1,0), 6),
            ('TOPPADDING', (0,0), (-1,0), 6),
            ('BACKGROUND', (0,1), (-1,-1), colors.HexColor('#F5F6FA'),),
            ('GRID', (0,0), (-1,-1), 0.4, colors.HexColor('#DDE4EB')),
        ]))
        story.append(table)
        story.append(Spacer(1, 0.3*inch))
        story.append(Paragraph("Prepared by: EvalBot - AI-Powered HR Analysis", body_text))
        story.append(PageBreak())

        # Detailed Analysis
        story.append(Paragraph("Detailed Candidate Evaluation", h1))
        
        # Communication and Speech
        story.append(Paragraph("1. Communication & Vocal Dynamics", h2))
        voice_analysis = analysis_data.get('voice_analysis', {})
        if voice_analysis' and 'error' not in voice_analysis:
            table_data = [
                ['Metric', 'Value', 'HR Insight'],
                ['Speaking Rate', f"{voice_analysis.get('speaking_rate', 0):.2f} words/sec", 'Benchmark: 2.0-3.0 wps; impacts clarity'],
                ['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'],
            ]
            table = Table(table_data, colWidths=[1.5*inch, 1.3*inch, 3.2*inch])
            table.setStyle(TableStyle([
                ('BACKGROUND', (0,0), (-1,0)), colors.HexColor('#0050BC')),
                ('TEXTCOLOR', (0,0), (-1,-0)), colors.white),
                ('ALIGN', (0,0), (-1,-1), 'LEFT'),
                ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
                ('FONTNAME', (0,0), (-1,-0)), 'Helvetica-Bold'),
                ('FONTSIZE', (0,0), (-1,-1), 8),
                ('BOTTOMPADDING', (0,0), (-1,-0)), 6),
                ('TOPPADDING', (0,0), (0,-1), 6),
                ('BACKGROUND', (0,1), (-1,-1), colors.HexColor('#F5F6FA'))),
                ('GRID', (0,0), (-1,-1), 0.4, colors.HexColor('#DDE4EB'))),
            ]))
            story.append(table)
            story.append(Spacer(1, 0.15*inch))
            chart_buffer = io.BytesIO()
            generate_anxiety_chart(voice_analysis.get('composite_scores', {}), chart_buffer)
            chart_buffer.seek(0)
            img = Image(chart_buffer, width=4.2*inch, height=2.8*inch)
            img.hAlign = 'CENTER'
            story.append(img)
        else:
            story.append(Paragraph("Voice analysis unavailable.", body_text))
        story.append(Spacer(1, 0.15*inch))

        # Parse Gemini Report
        sections = {
            "Executive Summary": [],
            "Communication": [],
            "Competency": {"Strengths": [], "Growth Areas": []},
            "Recommendations": {"Development": [], "Next Steps": []},
            "Role Fit": [],
        }
        current_section = None
        current_subsection = None
        lines = gemini_report_text.split('\n')
        for line in lines:
            line = line.strip()
            if not line: continue
            if line.startswith('**') and line.endswith('**'):
                section_title = line.strip('**').strip()
                if section_title.startswith(('1.', '2.', '3.', '4.', '5.')):
                    section_title = section_title[2:].strip()
                if 'Executive Summary' in section_title:
                    current_section = 'Executive Summary'
                    current_subsection = None
                elif 'Communication' in section_title:
                    current_section = 'Communication'
                    current_subsection = None
                elif 'Competency' in section_title:
                    current_section = 'Competency'
                    current_subsection = None
                elif 'Role Fit' in section_title:
                    current_section = 'Role Fit'
                    current_subsection = None
                elif 'Recommendations' in section_title:
                    current_section = 'Recommendations'
                    current_subsection = None
            elif line.startswith('-') and current_section:
                clean_line = line.lstrip('-').strip()
                if not clean_line: continue
                clean_line = re.sub(r'[()]', '', clean_line)
                if current_section == 'Competency':
                    if any(k in clean_line.lower() for k in ['leader', 'leadership', 'problem', 'commun', 'adapt', 'strength']):
                        current_subsection = 'Strengths'
                    elif any(k in clean_line.lower() for k in ['improv', 'grow', 'depth']):
                        current_subsection = 'Growth Areas'
                    if current_subsection:
                        sections[current_section][current_subsection].append(clean_line)
                elif current_section == 'Recommendations':
                    if any(k in clean_line.lower() for k in ['commun', 'tech', 'depth', 'pres']):
                        current_subsection = 'Development'
                    elif any(k in clean_line.lower() for k in ['adv', 'train', 'assess', 'next', 'mentor']):
                        current_subsection = 'Next Steps'
                    if current_subsection:
                        sections[current_section][current_subsection].append(clean_line)
                else:
                    sections[current_section].append(clean_line)

        # Executive Summary
        story.append(Paragraph("2. Executive Summary", h2))
        if sections['Executive Summary']:
            for line in sections['Executive Summary']:
                story.append(Paragraph(line, bullet_style))
        else:
            story.append(Paragraph("No summary provided.", body_text))
        story.append(Spacer(1, 0.15*inch))

        # Competency and Content
        story.append(Paragraph("3. Competency & Content", h2))
        story.append(Paragraph("Strengths", h3))
        if sections['Competency']['Strengths']:
            for line in sections['Competency']['Strengths']:
                story.append(Paragraph(line, bullet_style))
        else:
            story.append(Paragraph("No strengths identified.", body_text))
        story.append(Spacer(1, 0.1*inch))
        story.append(Paragraph("Growth Areas", h3))
        if sections['Competency']['Growth Areas']:
            for line in sections['Competency']['Growth Areas']:
                story.append(Paragraph(line, bullet_style))
        else:
            story.append(Paragraph("No growth areas identified; maintain current strengths.", body_text))
        story.append(Spacer(1, 0.15*inch))

        # Role Fit
        story.append(Paragraph("4. Role Fit & Potential", h2))
        if sections['Role Fit']:
            for line in sections['Role Fit']:
                story.append(Paragraph(line, bullet_style))
        else:
            story.append(Paragraph("No fit analysis provided.", body_text))
        story.append(Spacer(1, 0.15*inch))

        # Recommendations
        story.append(Paragraph("5. Recommendations", h2))
        story.append(Paragraph("Development Priorities", h3))
        if sections['Recommendations']['Development']:
            for line in sections['Recommendations']['Development']:
                story.append(Paragraph(line, bullet_style))
        else:
            story.append(Paragraph("No development priorities specified.", body_text))
        story.append(Spacer(1, 0.1*inch))
        story.append(Paragraph("Next Steps for Hiring Managers", h3))
        if sections['Recommendations']['Next Steps']:
            for line in sections['Recommendations']['Next Steps']:
                story.append(Paragraph(line, bullet_style))
        else:
            story.append(Paragraph("No next steps provided.", body_text))
        story.append(Spacer(1, 0.15*inch))
        story.append(Paragraph("This report provides actionable insights to support hiring and candidate development.", body_text))

        doc.build(story, onFirstPage=header_footer, onLaterPages=header_footer)
        logger.info(f"PDF report successfully generated at {output_path}")
        return True
    except Exception as e:
        logger.error(f"PDF generation failed: {str(e)}", exc_info=True)
        return False

def convert_to_serializable(obj):
    if isinstance(obj, np.generic): return obj.item()
    if isinstance(obj, dict): return {k: convert_to_serializable(v) for k, v in obj.items()}
    if isinstance(obj, list): return [convert_to_serializable(item) for item in obj]
    if isinstance(obj, np.ndarray): return obj.tolist()
    return obj

def process_interview(audio_url: str) -> Dict:
    """Process a single audio URL and generate analysis report."""
    local_audio_path = None
    wav_file = None
    is_downloaded = False
    try:
        if not isinstance(audio_url, str):
            raise ValueError("Input must be a single URL string")
        logger.info(f"Starting processing for {audio_url}")
        if audio_url.startswith(('http://', 'https://')):
            local_audio_path = download_audio_from_url(audio_url)
            is_downloaded = True
        else:
            local_audio_path = audio_url
            if not os.path.exists(local_audio_path):
                raise FileNotFoundError(f"Local audio file not found: {local_audio_path}")
        wav_file = convert_to_wav(local_audio_path)
        transcript = transcribe(wav_file)
        for utterance in transcript['utterances']:
            utterance['prosodic_features'] = extract_prosodic_features(wav_file, utterance['start'], utterance['end'])
        utterances_with_speakers = identify_speakers(transcript, wav_file)
        clf, vectorizer, scaler = None, None, None
        if os.path.exists(os.path.join(OUTPUT_DIR, 'role_classifier.pkl')):
            clf = joblib.load(os.path.join(OUTPUT_DIR, 'role_classifier.pkl'))
            vectorizer = joblib.load(os.path.join(OUTPUT_DIR, 'text_vectorizer.pkl'))
            scaler = joblib.load(os.path.join(OUTPUT_DIR, 'feature_scaler.pkl'))
        else:
            clf, vectorizer, scaler = train_role_classifier(utterances_with_speakers)
        classified_utterances = classify_roles(utterances_with_speakers, clf, vectorizer, scaler)
        voice_analysis = analyze_interviewee_voice(wav_file, classified_utterances)
        analysis_data = {
            'transcript': classified_utterances,
            'speakers': list(set(u['speaker'] for u in classified_utterances)),
            'voice_analysis': voice_analysis,
            'text_analysis': {
                'total_duration': sum(u['prosodic_features']['duration'] for u in classified_utterances),
                'speaker_turns': len(classified_utterances)
            }
        }
        analysis_data['acceptance_probability'] = calculate_acceptance_probability(analysis_data)
        gemini_report_text = generate_report(analysis_data)
        base_name = str(uuid.uuid4())
        pdf_path = os.path.join(OUTPUT_DIR, f"{base_name}_report.pdf")
        json_path = os.path.join(OUTPUT_DIR, f"{base_name}_analysis.json")
        pdf_success = create_pdf_report(analysis_data, pdf_path, gemini_report_text)
        with open(json_path, 'w') as f:
            serializable_data = convert_to_serializable(analysis_data)
            json.dump(serializable_data, f, indent=2)
        if not pdf_success:
            logger.warning(f"PDF report failed to generate for {audio_url}")
            return {
                'pdf_path': None,
                'json_path': json_path,
                'error': 'PDF generation failed'
            }
        logger.info(f"Processing completed for {audio_url}")
        return {'pdf_path': pdf_path, 'json_path': json_path}
    except Exception as e:
        logger.error(f"Processing failed for {audio_url}: {str(e)}", exc_info=True)
        base_name = str(uuid.uuid4())
        json_path = os.path.join(OUTPUT_DIR, f"{base_name}_analysis.json")
        with open(json_path, 'w') as f:
            json.dump({'error': str(e)}, f, indent=2)
        return {
            'pdf_path': None,
            'json_path': json_path,
            'error': str(e)
        }
    finally:
        if wav_file and os.path.exists(wav_file):
            try:
                os.remove(wav_file)
            except Exception as e:
                logger.error(f"Failed to clean up wav file {wav_file}: {str(e)}")
        if is_downloaded and local_audio_path and os.path.exists(local_audio_path):
            try:
                os.remove(local_audio_path)
                logger.info(f"Cleaned up temporary audio file: {local_audio_path}")
            except Exception as e:
                logger.error(f"Failed to clean up local audio file {local_audio_path}: {str(e)}")