Update process_interview.py

process_interview.py

@@ -1,207 +1,950 @@
 import os
+import torch
+import numpy as np
 import uuid
-import shutil
-import json
 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
-from fastapi import FastAPI, HTTPException, Body
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
-from pydantic import BaseModel, HttpUrl
-from process_interview import process_interview # Assuming process_interview is in a separate file
+# --- Imports for enhanced PDF ---
+from reportlab.lib.pagesizes import letter
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, PageBreak
+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')
+from reportlab.platypus import Image
+import io # --- FIX: إضافة import io لـ BytesIO ---
+# --- End Imports for enhanced PDF ---
+from transformers import AutoTokenizer, AutoModel
+import spacy
+import google.generativeai as genai
+import joblib
+from concurrent.futures import ThreadPoolExecutor
 
-# Logging setup
+# Setup logging
 logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("EvalBot-Audio-Processor")
-
-# Initialize FastAPI app
-app = FastAPI()
-
-# Directories
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-TEMP_DIR = os.path.join(BASE_DIR, "temp_files")
-STATIC_DIR = os.path.join(BASE_DIR, "static")
-OUTPUT_DIR = os.path.join(STATIC_DIR, "outputs") # Outputs are within static to be servable
-JSON_DIR = os.path.join(OUTPUT_DIR, "json")
-PDF_DIR = os.path.join(OUTPUT_DIR, "pdf")
-
-# Create necessary directories
-for folder in [TEMP_DIR, JSON_DIR, PDF_DIR]:
-    os.makedirs(folder, exist_ok=True)
-
-# Mount static files directory to be accessible via /static URL
-app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
-
-# Configuration Constants
-VALID_EXTENSIONS = ('.wav', '.mp3', '.m4a', '.flac')
-MAX_FILE_SIZE_MB = 300
-
-# Base URL for the deployed application (e.g., from Hugging Face Space or ngrok)
-# This should NOT include /static or any subpaths.
-# Example: https://evalbot-audio-evalbot.hf.space
-# Example: https://your-ngrok-url.ngrok-free.app
-BASE_URL = os.getenv("BASE_URL", "http://localhost:7860") # Default for local testing
-
-# Pydantic Models for Request/Response validation
-class ProcessResponse(BaseModel):
-    """Response model for the /process-audio endpoint."""
-    summary: str
-    json_url: str
-    pdf_url: str
-
-class ProcessAudioRequest(BaseModel):
-    """Request model for the /process-audio endpoint."""
-    file_url: HttpUrl # URL of the audio file to process
-    user_id: str      # Identifier for the user submitting the audio
-
-# Helper Functions
-def download_file(file_url: str, dest_path: str):
-    """Downloads a file from a given URL to a specified destination path."""
-    logger.info(f"Attempting to download file from {file_url}")
+logger = logging.getLogger(__name__)
+logging.getLogger("nemo_logging").setLevel(logging.ERROR)
+logging.getLogger("nemo").setLevel(logging.ERROR)
+
+# Configuration
+AUDIO_DIR = "./uploads"
+OUTPUT_DIR = "./processed_audio"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# API Keys
+PINECONE_KEY = os.getenv("PINECONE_KEY")
+ASSEMBLYAI_KEY = os.getenv("ASSEMBLYAI_KEY")
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+
+
+# Initialize services
+def initialize_services():
     try:
-        resp = requests.get(file_url, stream=True, timeout=60) # Increased timeout
-        resp.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
-        
-        # Ensure the destination directory exists
-        os.makedirs(os.path.dirname(dest_path), exist_ok=True)
+        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 setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+logger.info(f"Using device: {device}")
+
+
+def load_speaker_model():
+    try:
+        import torch
+        torch.set_num_threads(5)
+        model = EncDecSpeakerLabelModel.from_pretrained(
+            "nvidia/speakerverification_en_titanet_large",
+            map_location=torch.device('cpu')
+        )
+        model.eval()
+        return model
+    except Exception as e:
+        logger.error(f"Model loading failed: {str(e)}")
+        raise RuntimeError("Could not load speaker verification model")
+
+
+# Load ML models
+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()
+
+
+# Audio processing functions
+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, full_audio, wav_file):
+    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() # Ensure numpy array
         
-        with open(dest_path, "wb") as f:
-            for chunk in resp.iter_content(chunk_size=8192):
-                if chunk: # Filter out keep-alive new chunks
-                    f.write(chunk)
-        logger.info(f"File downloaded successfully to {dest_path}")
-    except requests.exceptions.RequestException as e:
-        logger.error(f"Error downloading file from {file_url}: {e}")
-        raise HTTPException(status_code=400, detail=f"Failed to download file from URL: {e}")
+        # --- FIX: Convert embedding to a flat list for Pinecone query ---
+        embedding_list = embedding.flatten().tolist()
+        # --- End FIX ---
+
+        query_result = index.query(
+            vector=embedding_list, # Use the corrected flat 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})]) # Use corrected list
+
+        os.remove(temp_path)
+
+        return {
+            **utterance,
+            'speaker': speaker_name,
+            'speaker_id': speaker_id,
+            'embedding': embedding_list # Store the corrected list
+        }
     except Exception as e:
-        logger.error(f"Unexpected error during file download: {e}", exc_info=True)
-        raise HTTPException(status_code=500, detail="Internal server error during file download")
-
-def validate_file_size(file_path: str):
-    """Validates the size of a file against MAX_FILE_SIZE_MB."""
-    file_size_mb = os.path.getsize(file_path) / (1024 * 1024)
-    if file_size_mb > MAX_FILE_SIZE_MB:
-        logger.warning(f"File too large: {file_size_mb:.2f} MB. Max allowed: {MAX_FILE_SIZE_MB} MB")
-        os.remove(file_path) # Clean up the oversized file
-        raise HTTPException(status_code=400, detail=f"File too large: {file_size_mb:.2f} MB. Max size: {MAX_FILE_SIZE_MB} MB")
-
-def generate_public_url(full_local_path: str) -> str:
-    """
-    Generates a public URL for a locally stored file.
-    Assumes the file is located within the STATIC_DIR.
-    The URL will be BASE_URL/static/relative_path_from_static_dir.
-    """
-    # Calculate the path relative to STATIC_DIR
-    # Example: If STATIC_DIR is /app/static and full_local_path is /app/static/outputs/json/file.json
-    # relative_path will be "outputs/json/file.json" (or "outputs\json\file.json" on Windows)
-    relative_path = os.path.relpath(full_local_path, STATIC_DIR)
-    
-    # Replace backslashes with forward slashes for web URL compatibility (especially on Windows)
-    web_path = relative_path.replace(os.path.sep, "/") 
-    
-    # Construct the full public URL using the BASE_URL and the mounted static path prefix
-    return f"{BASE_URL}/static/{web_path}"
-
-# Main API Endpoint
-@app.post("/process-audio", response_model=ProcessResponse)
-async def process_audio(request: ProcessAudioRequest = Body(...)):
+        logger.error(f"Utterance processing failed: {str(e)}", exc_info=True)
+        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:  # Changed to 5 workers
+            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] # تم حذف الـ 'u' الزائدة
+        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)
+
+        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:
+            return {'error': 'No interviewee utterances found'}
+
+        segments = []
+        for u in interviewee_utterances:
+            start = int(u['start'] * sr / 1000)
+            end = int(u['end'] * sr / 1000)
+            segments.append(y[start:end])
+
+        combined_audio = np.concatenate(segments)
+
+        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 = librosa.feature.rms(y=segment)[0]
+            intensities.extend(rms)
+
+        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)}")
+        return {'error': str(e)}
+
+
+def generate_voice_interpretation(analysis: Dict) -> str:
+    # This function is used to provide the text interpretation for Gemini's prompt.
+    if 'error' in analysis:
+        return "Voice analysis not available."
+
+    interpretation_lines = []
+    interpretation_lines.append("Voice Analysis Summary:")
+    interpretation_lines.append(f"- Speaking Rate: {analysis['speaking_rate']} words/sec (average)")
+    interpretation_lines.append(f"- Filler Words: {analysis['filler_ratio'] * 100:.1f}% of words")
+    interpretation_lines.append(f"- Repetition Score: {analysis['repetition_score']:.3f}")
+    interpretation_lines.append(
+        f"- Anxiety Level: {analysis['interpretation']['anxiety_level'].upper()} (score: {analysis['composite_scores']['anxiety']:.3f})")
+    interpretation_lines.append(
+        f"- Confidence Level: {analysis['interpretation']['confidence_level'].upper()} (score: {analysis['composite_scores']['confidence']:.3f})")
+    interpretation_lines.append(f"- Fluency: {analysis['interpretation']['fluency_level'].upper()}")
+    interpretation_lines.append("")
+    interpretation_lines.append("Detailed Interpretation:")
+    interpretation_lines.append(
+        "1. A higher speaking rate indicates faster speech, which can suggest nervousness or enthusiasm.")
+    interpretation_lines.append("2. Filler words and repetitions reduce speech clarity and professionalism.")
+    interpretation_lines.append("3. Anxiety is measured through pitch variability and voice instability.")
+    interpretation_lines.append("4. Confidence is assessed through voice intensity and stability.")
+    interpretation_lines.append("5. Fluency combines filler words and repetition metrics.")
+
+    return "\n".join(interpretation_lines)
+
+
+# --- Chart Generation Function ---
+def generate_anxiety_confidence_chart(composite_scores: Dict, chart_path: str):
+    try:
+        labels = ['Anxiety', 'Confidence']
+        scores = [composite_scores.get('anxiety', 0), composite_scores.get('confidence', 0)]
+
+        fig, ax = plt.subplots(figsize=(4, 2.5))  # Smaller size for embedding in PDF
+        ax.bar(labels, scores, color=['lightcoral', 'lightskyblue'])
+        ax.set_ylabel('Score')
+        ax.set_title('Anxiety vs. Confidence Scores')
+        ax.set_ylim(0, 1.0)  # Assuming scores are normalized 0-1
+
+        for i, v in enumerate(scores):
+            ax.text(i, v + 0.05, f"{v:.2f}", color='black', ha='center', fontweight='bold')
+
+        # هذه الأوامر يجب أن تكون خارج الـ loop عشان يتم تنفيذها مرة واحدة بعد رسم كل العناصر
+        plt.tight_layout()
+        plt.savefig(chart_path)
+        plt.close(fig)  # Close the figure to free up memory
+    except Exception as e:
+        logger.error(f"Error generating chart: {str(e)}")
+
+
+# --- Acceptance Probability Calculation ---
+def calculate_acceptance_probability(analysis_data: Dict) -> float:
     """
-    Endpoint to process an audio file from a given URL.
-    Downloads the audio, processes it through the interview analysis pipeline,
-    and returns URLs for the generated JSON analysis and PDF report.
+    Calculates a hypothetical acceptance probability based on voice and content analysis.
+    This is a simplified, heuristic model and can be refined with more data/ML.
     """
-    file_url = str(request.file_url)
-    user_id = request.user_id
-    
-    # Validate file extension based on URL
-    file_ext = os.path.splitext(file_url)[1].lower()
-    if file_ext not in VALID_EXTENSIONS:
-        logger.error(f"Invalid file extension: {file_ext}. Supported: {VALID_EXTENSIONS}")
-        raise HTTPException(status_code=400, detail=f"Invalid file extension: {file_ext}. Supported formats: {', '.join(VALID_EXTENSIONS)}")
-    
-    # Create a unique temporary path for the downloaded audio file
-    temp_filename = f"{user_id}_{uuid.uuid4().hex}{file_ext}"
-    temp_path = os.path.join(TEMP_DIR, temp_filename)
-    
+    voice = analysis_data.get('voice_analysis', {})
+
+    if 'error' in voice:
+        return 0.0  # Cannot calculate if voice analysis failed
+
+    # Weights for different factors (adjust these to fine-tune the model)
+    w_confidence = 0.4
+    w_anxiety = -0.3  # Negative weight for anxiety
+    w_fluency = 0.2
+    w_speaking_rate = 0.1  # Ideal rate gets higher score
+    w_filler_repetition = -0.1  # Negative weight for filler/repetition
+    w_content_strengths = 0.2  # Placeholder, ideally from deeper content analysis
+
+    # Normalize/interpret scores
+    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 mapping (higher score for more fluent)
+    fluency_map = {'fluent': 1.0, 'moderate': 0.5, 'disfluent': 0.0}
+    fluency_val = fluency_map.get(fluency_level, 0.0)
+
+    # Speaking rate scoring (e.g., ideal is around 2.5 words/sec, gets lower for too fast/slow)
+    # This is a simple inverse of deviation from ideal
+    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))  # Max 1.0, min 0.0
+
+    # Filler/Repetition score (lower is better, so 1 - score)
+    filler_repetition_composite = (filler_ratio + repetition_score) / 2  # Average them
+    filler_repetition_score = max(0, 1 - filler_repetition_composite)
+
+    # Simplified content strength score (you might need a more sophisticated NLP method here)
+    # For now, based on presence of strengths in Gemini's content analysis
+    content_strength_val = 0.0
+    # This part would ideally come from a structured output from Gemini's content analysis.
+    # For now, we'll make a simplified assumption based on the analysis data:
+    # If content analysis found "strengths" (which is likely if Gemini generates a full report)
+    # This needs refinement if Gemini output is not structured for this.
+    if analysis_data.get('text_analysis', {}).get('total_duration', 0) > 0:  # Basic check if interview happened
+        content_strength_val = 0.8  # Assume moderate strength if analysis went through
+        # You could parse gemini_report_text for specific phrases like "Strengths:" and count items.
+
+    # Calculate raw score
+    raw_score = (
+            confidence_score * w_confidence +
+            (1 - anxiety_score) * abs(w_anxiety) +  # (1 - anxiety) because lower anxiety is better
+            fluency_val * w_fluency +
+            speaking_rate_score * w_speaking_rate +
+            filler_repetition_score * abs(w_filler_repetition) +  # Use abs weight as score is already inverted
+            content_strength_val * w_content_strengths
+    )
+
+    # Normalize to 0-1 and then to percentage
+    # These max/min values are rough estimates and should be calibrated with real data
+    min_possible_score = (0 * w_confidence) + (0 * abs(w_anxiety)) + (0 * w_fluency) + (0 * w_speaking_rate) + (
+                0 * abs(w_filler_repetition)) + (0 * w_content_strengths)
+    max_possible_score = (1 * w_confidence) + (1 * abs(w_anxiety)) + (1 * w_fluency) + (1 * w_speaking_rate) + (
+                1 * abs(w_filler_repetition)) + (1 * w_content_strengths)
+
+    # Prevent division by zero if all weights are zero or min/max are same
+    if max_possible_score == min_possible_score:
+        normalized_score = 0.5  # Default if no variation
+    else:
+        normalized_score = (raw_score - min_possible_score) / (max_possible_score - min_possible_score)
+
+    acceptance_probability = max(0.0, min(1.0, normalized_score))  # Clamp between 0 and 1
+
+    return float(f"{acceptance_probability * 100:.2f}")  # Return as percentage
+
+
+def generate_report(analysis_data: Dict) -> str:
     try:
-        # 1. Download the audio file
-        download_file(file_url, temp_path)
-        
-        # 2. Validate downloaded file size
-        validate_file_size(temp_path)
-        
-        logger.info(f"Starting interview processing for user: {user_id} from {temp_path}")
-        
-        # 3. Process the interview audio using the external process_interview module
-        # process_interview returns a dictionary with local paths to the generated JSON and PDF
-        result = process_interview(temp_path) 
-        
-        if not result:
-            logger.error(f"process_interview returned no result for {user_id}")
-            raise HTTPException(status_code=500, detail="Audio processing failed: No result from analysis pipeline.")
+        voice = analysis_data.get('voice_analysis', {})
+        voice_interpretation = generate_voice_interpretation(voice)
 
-        # 4. Generate unique filenames for outputs and copy them to static outputs directory
-        json_filename = f"{user_id}_{uuid.uuid4().hex}.json"
-        pdf_filename = f"{user_id}_{uuid.uuid4().hex}.pdf"
-        
-        json_dest_path = os.path.join(JSON_DIR, json_filename)
-        pdf_dest_path = os.path.join(PDF_DIR, pdf_filename)
-        
-        shutil.copyfile(result['json_path'], json_dest_path)
-        shutil.copyfile(result['pdf_path'], pdf_dest_path)
-        logger.info(f"Analysis outputs copied to: {json_dest_path} and {pdf_dest_path}")
+        interviewee_responses = [
+                                    f"Speaker {u['speaker']} ({u['role']}): {u['text']}"
+                                    for u in analysis_data['transcript']
+                                    if u['role'] == 'Interviewee'
+                                ][:5]  # Limit to first 5 for prompt brevity
 
-        # 5. Load analysis data for summary and generate public URLs
-        with open(json_dest_path, "r") as jf: # Use json_dest_path to read the *copied* file
-            analysis_data = json.load(jf)
-        
-        voice_interpretation = analysis_data.get('voice_analysis', {}).get('interpretation', {})
-        speakers_list = analysis_data.get('speakers', [])
-        total_duration = analysis_data.get('text_analysis', {}).get('total_duration', 0.0)
-        
-        summary_text = (
-            f"User ID: {user_id}\n"
-            f"Speakers: {', '.join(speakers_list)}\n"
-            f"Duration: {total_duration:.2f} sec\n"
-            f"Confidence: {voice_interpretation.get('confidence_level', 'N/A')}\n"
-            f"Anxiety: {voice_interpretation.get('anxiety_level', 'N/A')}"
-        )
-        
-        # This is the crucial part: generate public URLs using the correct local paths
-        json_public_url = generate_public_url(json_dest_path)
-        pdf_public_url = generate_public_url(pdf_dest_path)
-        
-        logger.info("Audio processing and URL generation completed successfully.")
-        return ProcessResponse(summary=summary_text, json_url=json_public_url, pdf_url=pdf_public_url)
+        acceptance_prob = analysis_data.get('acceptance_probability', None)
+        acceptance_line = ""
+        if acceptance_prob is not None:
+            acceptance_line = f"\n**Estimated Acceptance Probability: {acceptance_prob:.2f}%**\n"
+            if acceptance_prob >= 80:
+                acceptance_line += "This indicates a very strong candidate. Well done!"
+            elif acceptance_prob >= 50:
+                acceptance_line += "This indicates a solid candidate with potential for improvement."
+            else:
+                acceptance_line += "This candidate may require significant development or may not be a strong fit."
+
+        prompt = f"""
+        As EvalBot, an AI interview analysis system, generate a highly professional, well-structured, and concise interview analysis report.
+        The report should be suitable for a professional setting and clearly highlight key findings and actionable recommendations.
+        Use clear headings and subheadings. For bullet points, use '- '.
+
+        {acceptance_line}
+
+        **1. Executive Summary**
+        Provide a brief, high-level overview of the interview.
+        - Overall interview duration: {analysis_data['text_analysis']['total_duration']:.2f} seconds
+        - Number of speaker turns: {analysis_data['text_analysis']['speaker_turns']}
+        - Main participants: {', '.join(analysis_data['speakers'])}
+
+        **2. Voice Analysis Insights**
+        Analyze key voice metrics and provide a detailed interpretation.
+        {voice_interpretation}
+
+        **3. Content Analysis & Strengths/Areas for Development**
+        Analyze the key themes and identify both strengths and areas for development in the interviewee's responses.
+        Key responses from interviewee (for context):
+        {chr(10).join(interviewee_responses)}
 
-    except HTTPException as e:
-        # Re-raise HTTPException directly as it already contains appropriate status/detail
-        raise e
+        **4. Actionable Recommendations**
+        Offer specific, actionable suggestions for improvement.
+        Focus on:
+        - Communication Skills (e.g., pacing, clarity, filler words)
+        - Content Delivery (e.g., quantifying achievements, structuring answers)
+        - Professional Presentation (e.g., research, specific examples, mock interviews)
+        """
+
+        response = gemini_model.generate_content(prompt)
+        return response.text
     except Exception as e:
-        # Catch any other unexpected errors during the process
-        logger.exception(f"Unexpected error during audio processing for user {user_id}: {e}")
-        raise HTTPException(status_code=500, detail=f"Internal server error during processing: {e}")
-    finally:
-        # Clean up the temporary downloaded audio file
-        if os.path.exists(temp_path):
-            os.remove(temp_path)
-            logger.info(f"Cleaned up temporary file: {temp_path}")
-        
-# Routes to serve output files directly if needed (though /static mount handles this)
-# These explicit routes are often redundant if /static mount is configured correctly,
-# but can be useful for specific media types or debugging.
-@app.get("/outputs/json/{filename}", response_class=FileResponse)
-async def get_json_file(filename: str):
-    """Serves a JSON analysis file from the outputs directory."""
-    file_path = os.path.join(JSON_DIR, filename)
-    if not os.path.exists(file_path):
-        raise HTTPException(status_code=404, detail="JSON file not found")
-    return FileResponse(file_path, media_type="application/json", filename=filename)
-
-@app.get("/outputs/pdf/{filename}", response_class=FileResponse)
-async def get_pdf_file(filename: str):
-    """Serves a PDF report file from the outputs directory."""
-    file_path = os.path.join(PDF_DIR, filename)
-    if not os.path.exists(file_path):
-        raise HTTPException(status_code=404, detail="PDF file not found")
-    return FileResponse(file_path, media_type="application/pdf", filename=filename)
+        logger.error(f"Report generation failed: {str(e)}")
+        return f"Error generating report: {str(e)}"
+
+
+# --- ENHANCED PDF GENERATION FUNCTION ---
+def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text: str):
+    try:
+        doc = SimpleDocTemplate(output_path, pagesize=letter)
+        styles = getSampleStyleSheet()
+
+        # Define custom styles
+        h1 = ParagraphStyle(name='Heading1', parent=styles['h1'], fontSize=16, spaceAfter=14, alignment=1,
+                            textColor=colors.HexColor('#003366'))
+        h2 = ParagraphStyle(name='Heading2', parent=styles['h2'], fontSize=12, spaceBefore=10, spaceAfter=8,
+                            textColor=colors.HexColor('#336699'))
+        h3 = ParagraphStyle(name='Heading3', parent=styles['h3'], fontSize=10, spaceBefore=8, spaceAfter=4,
+                            textColor=colors.HexColor('#0055AA'))
+        body_text = ParagraphStyle(name='BodyText', parent=styles['Normal'], fontSize=9, leading=12, spaceAfter=4)
+        bullet_style = ParagraphStyle(name='Bullet', parent=styles['Normal'], fontSize=9, leading=12, leftIndent=18,
+                                      bulletIndent=9)
+
+        story = []
+
+        # Title and Date
+        story.append(Paragraph(f"<b>EvalBot Interview Analysis Report</b>", h1))
+        story.append(Spacer(1, 0.2 * inch))
+        story.append(Paragraph(f"<b>Date:</b> {time.strftime('%Y-%m-%d')}", body_text))
+        story.append(Spacer(1, 0.3 * inch))
+
+        # --- Acceptance Probability (New Section) ---
+        acceptance_prob = analysis_data.get('acceptance_probability', None)
+        if acceptance_prob is not None:
+            story.append(Paragraph("<b>Candidate Evaluation Summary</b>", h2))
+            story.append(Spacer(1, 0.1 * inch))
+
+            prob_color = colors.green if acceptance_prob >= 70 else (
+                colors.orange if acceptance_prob >= 40 else colors.red)
+
+            # --- FIX: Call .hexval() as a method ---
+            story.append(Paragraph(
+                f"<font size='12' color='{prob_color.hexval()}'><b>Estimated Acceptance Probability: {acceptance_prob:.2f}%</b></font>",
+                ParagraphStyle(name='AcceptanceProbability', parent=styles['Normal'], fontSize=12, spaceAfter=10,
+                               alignment=1)
+            ))
+            # --- End FIX ---
+
+            if acceptance_prob >= 80:
+                story.append(
+                    Paragraph("This indicates a very strong candidate with high potential. Well done!", body_text))
+            elif acceptance_prob >= 50:
+                story.append(Paragraph(
+                    "This candidate shows solid potential but has areas for improvement to become an even stronger fit.",
+                    body_text))
+            else:
+                story.append(Paragraph(
+                    "This candidate may require significant development or may not be the ideal fit at this time.",
+                    body_text))
+            story.append(Spacer(1, 0.3 * inch))
+        # --- End Acceptance Probability ---
+
+        # Parse Gemini's report into sections for better PDF structuring
+        sections = {}
+        current_section = None
+        # Use regex to robustly identify sections, especially with varied bullet points
+        section_patterns = {
+            r'^\s*\*\*\s*1\.\s*Executive Summary\s*\*\*': 'Executive Summary',
+            r'^\s*\*\*\s*2\.\s*Voice Analysis Insights\s*\*\*': 'Voice Analysis Insights',
+            r'^\s*\*\*\s*3\.\s*Content Analysis & Strengths/Areas for Development\s*\*\*': 'Content Analysis & Strengths/Areas for Development',
+            r'^\s*\*\*\s*4\.\s*Actionable Recommendations\s*\*\*': 'Actionable Recommendations'
+        }
+
+        for line in gemini_report_text.split('\n'):
+            matched_section = False
+            for pattern, section_name in section_patterns.items():
+                if re.match(pattern, line):
+                    current_section = section_name
+                    sections[current_section] = []
+                    matched_section = True
+                    break
+            if not matched_section and current_section:
+                sections[current_section].append(line)
+
+        # 1. Executive Summary
+        story.append(Paragraph("1. Executive Summary", h2))
+        story.append(Spacer(1, 0.1 * inch))
+        if 'Executive Summary' in sections:
+            for line in sections['Executive Summary']:
+                if line.strip():
+                    story.append(Paragraph(line.strip(), body_text))
+            story.append(Spacer(1, 0.2 * inch))
+
+        # 2. Voice Analysis (Detailed - using Table for summary)
+        story.append(Paragraph("2. Voice Analysis", h2))
+        voice_analysis = analysis_data.get('voice_analysis', {})
+
+        if voice_analysis and 'error' not in voice_analysis:
+            # Voice Analysis Summary Table
+            table_data = [
+                ['Metric', 'Value', 'Interpretation'],
+                ['Speaking Rate', f"{voice_analysis['speaking_rate']:.2f} words/sec", 'Average rate'],
+                ['Filler Words', f"{voice_analysis['filler_ratio'] * 100:.1f}%", 'Percentage of total words'],
+                ['Repetition Score', f"{voice_analysis['repetition_score']:.3f}", 'Lower is better articulation'],
+                ['Anxiety Level', voice_analysis['interpretation']['anxiety_level'].upper(),
+                 f"Score: {voice_analysis['composite_scores']['anxiety']:.3f}"],
+                ['Confidence Level', voice_analysis['interpretation']['confidence_level'].upper(),
+                 f"Score: {voice_analysis['composite_scores']['confidence']:.3f}"],
+                ['Fluency', voice_analysis['interpretation']['fluency_level'].upper(), 'Overall speech flow']
+            ]
+
+            table_style = TableStyle([
+                ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#6699CC')),
+                ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
+                ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
+                ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+                ('BOTTOMPADDING', (0, 0), (-1, 0), 10),
+                ('BACKGROUND', (0, 1), (-1, -1), colors.HexColor('#EFEFEF')),
+                ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#CCCCCC')),
+                ('LEFTPADDING', (0, 0), (-1, -1), 6),
+                ('RIGHTPADDING', (0, 0), (-1, -1), 6),
+                ('TOPPADDING', (0, 0), (-1, -1), 6),
+                ('BOTTOMPADDING', (0, 0), (-1, -1), 6),
+            ])
+
+            table = Table(table_data)
+            table.setStyle(table_style)
+            story.append(table)
+            story.append(Spacer(1, 0.2 * inch))
+
+            # --- Charts ---
+            story.append(Paragraph("Score Visualization:", h3))
+            # chart_path = os.path.join(OUTPUT_DIR, f"anxiety_confidence_{uuid.uuid4().hex[:8]}.png") # Removed from here
+            # --- FIX: Generate chart in memory (BytesIO) ---
+            chart_buffer = io.BytesIO() # Create in-memory buffer
+            try:
+                generate_anxiety_confidence_chart(voice_analysis['composite_scores'], chart_buffer) # Pass buffer instead of path
+                chart_buffer.seek(0) # Rewind the buffer to the beginning
+                img = Image(chart_buffer, width=3.5*inch, height=2.0*inch) # Load image from buffer
+                story.append(img)
+                story.append(Spacer(1, 0.1 * inch))
+            except NameError:
+                logger.warning("Chart generation function 'generate_anxiety_confidence_chart' is not defined. Skipping chart.")
+            except Exception as chart_e:
+                logger.warning(f"Could not add chart image to PDF: {chart_e}. Please check matplotlib installation.")
+            # --- End FIX ---
+            # --- End Charts ---
+
+            # Detailed Interpretation from Gemini (if present)
+            if 'Voice Analysis Insights' in sections:
+                story.append(Paragraph("Detailed Interpretation:", h3))
+                for line in sections['Voice Analysis Insights']:
+                    if line.strip():
+                        # Handle numbered lists from Gemini
+                        if re.match(r'^\d+\.\s', line.strip()):
+                            story.append(
+                                Paragraph(line.strip(), bullet_style))
+                        else:
+                            story.append(Paragraph(line.strip(), body_text))
+                story.append(Spacer(1, 0.2 * inch))
+
+        else:
+            story.append(Paragraph("Voice analysis not available or encountered an error.", body_text))
+        story.append(Spacer(1, 0.3 * inch))
+
+        # 3. Content Analysis
+        story.append(Paragraph("3. Content Analysis", h2))
+        if 'Content Analysis & Strengths/Areas for Development' in sections:
+            for line in sections['Content Analysis & Strengths/Areas for Development']:
+                if line.strip():
+                    # Handle bullet points from Gemini
+                    if line.strip().startswith('-'):
+                        story.append(Paragraph(line.strip()[1:].strip(), bullet_style)) # Remove the '-' and strip
+                    else:
+                        story.append(Paragraph(line.strip(), body_text))
+            story.append(Spacer(1, 0.2 * inch))
+
+        # Add some interviewee responses to the report (can be formatted as a list)
+        story.append(Paragraph("Key Interviewee Responses (Contextual):", h3))
+        interviewee_responses = [
+                                    f"Speaker {u['speaker']} ({u['role']}): {u['text']}"
+                                    for u in analysis_data['transcript']
+                                    if u['role'] == 'Interviewee'
+                                ][:5]
+        for res in interviewee_responses:
+            story.append(Paragraph(res, bullet_style))
+        story.append(Spacer(1, 0.3 * inch))
+
+        # 4. Recommendations
+        story.append(Paragraph("4. Recommendations", h2))
+        if 'Actionable Recommendations' in sections:
+            for line in sections['Actionable Recommendations']:
+                if line.strip():
+                    # Handle bullet points from Gemini
+                    if line.strip().startswith('-'):
+                        story.append(Paragraph(line.strip()[1:].strip(), bullet_style)) # Remove the '-' and strip
+                    else:
+                        story.append(Paragraph(line.strip(), body_text))
+            story.append(Spacer(1, 0.2 * inch))
+
+        # Footer Text
+        story.append(Spacer(1, 0.5 * inch))
+        story.append(Paragraph("--- Analysis by EvalBot ---", ParagraphStyle(
+            name='FooterText', parent=styles['Normal'], fontSize=8, alignment=1, textColor=colors.HexColor('#666666')
+        )))
+
+        doc.build(story)
+        return True
+    except Exception as e:
+        logger.error(f"PDF creation failed: {str(e)}", exc_info=True)
+        return False
+
+
+def convert_to_serializable(obj):
+    if isinstance(obj, np.generic):
+        return obj.item()
+    elif isinstance(obj, dict):
+        return {key: convert_to_serializable(value) for key, value in obj.items()}
+    elif isinstance(obj, list):
+        return [convert_to_serializable(item) for item in obj]
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    return obj
+
+
+def process_interview(audio_path: str):
+    try:
+        logger.info(f"Starting processing for {audio_path}")
+
+        wav_file = convert_to_wav(audio_path)
+
+        logger.info("Starting transcription")
+        transcript = transcribe(wav_file)
+
+        logger.info("Extracting prosodic features")
+        for utterance in transcript['utterances']:
+            utterance['prosodic_features'] = extract_prosodic_features(
+                wav_file,
+                utterance['start'],
+                utterance['end']
+            )
+
+        logger.info("Identifying speakers")
+        utterances_with_speakers = identify_speakers(transcript, wav_file)
+
+        logger.info("Classifying roles")
+        # Ensure role classifier models are loaded/trained only once if possible,
+        # or handled carefully in a multi-threaded context.
+        # For simplicity, keeping it inside process_interview for now.
+        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)
+
+        logger.info("Analyzing interviewee voice")
+        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)
+            }
+        }
+
+        # --- Calculate Acceptance Probability ---
+        acceptance_probability = calculate_acceptance_probability(analysis_data)
+        analysis_data['acceptance_probability'] = acceptance_probability
+        # --- End Acceptance Probability ---
+
+        logger.info("Generating report text using Gemini")
+        gemini_report_text = generate_report(analysis_data)
+
+        base_name = os.path.splitext(os.path.basename(audio_path))[0]
+        pdf_path = os.path.join(OUTPUT_DIR, f"{base_name}_report.pdf")
+        create_pdf_report(analysis_data, pdf_path, gemini_report_text=gemini_report_text)
+
+        json_path = os.path.join(OUTPUT_DIR, f"{base_name}_analysis.json")
+        with open(json_path, 'w') as f:
+            serializable_data = convert_to_serializable(analysis_data)
+            json.dump(serializable_data, f, indent=2)
+
+        os.remove(wav_file)  # Clean up WAV file after processing
+
+        logger.info(f"Processing completed for {audio_path}")
+        return {
+            'pdf_path': pdf_path,
+            'json_path': json_path
+        }
+    except Exception as e:
+        logger.error(f"Processing failed: {str(e)}", exc_info=True)
+        # Clean up wav_file in case of error
+        if 'wav_file' in locals() and os.path.exists(wav_file):
+            os.remove(wav_file)
+        raise