Update process_interview.py

process_interview.py

@@ -17,6 +17,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 import re
 from typing import Dict, List, Tuple
 import logging
+import tempfile
 # --- Imports for enhanced PDF ---
 from reportlab.lib.pagesizes import letter
 from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, PageBreak
@@ -25,10 +26,9 @@ from reportlab.lib.units import inch
 from reportlab.lib import colors
 import matplotlib.pyplot as plt
 import matplotlib
-
-matplotlib.use('Agg')  # --- FIX: تحديد backend لـ matplotlib ---
+matplotlib.use('Agg')
 from reportlab.platypus import Image
-import io  # --- FIX: إضافة import io لـ BytesIO ---
+import io
 # --- End Imports for enhanced PDF ---
 from transformers import AutoTokenizer, AutoModel
 import spacy
@@ -53,6 +53,27 @@ ASSEMBLYAI_KEY = os.getenv("ASSEMBLYAI_KEY")
 GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
 
 
+# --- NEW HELPER FUNCTION to download from URL ---
+def download_audio_from_url(url: str) -> str:
+    """Downloads an audio file from a URL to a temporary local path."""
+    try:
+        # Create a temporary file to store the downloaded audio
+        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) 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 Exception as e:
+        logger.error(f"Failed to download audio from URL {url}: {e}")
+        raise
+# --- END NEW HELPER FUNCTION ---
+
+
 # Initialize services
 def initialize_services():
     try:
@@ -66,16 +87,13 @@ def initialize_services():
                 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
@@ -102,11 +120,9 @@ def load_speaker_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
 
 
@@ -120,7 +136,6 @@ def convert_to_wav(audio_path: str, output_dir: str = OUTPUT_DIR) -> str:
         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
@@ -135,11 +150,9 @@ def extract_prosodic_features(audio_path: str, start_ms: int, end_ms: int) -> Di
         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,
@@ -151,21 +164,14 @@ def extract_prosodic_features(audio_path: str, start_ms: int, end_ms: int) -> Di
             '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,
+            '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
         }
 
 
@@ -178,7 +184,6 @@ def transcribe(audio_path: str) -> Dict:
                 data=f
             )
         audio_url = upload_response.json()['upload_url']
-
         transcript_response = requests.post(
             "https://api.assemblyai.com/v2/transcript",
             headers={"authorization": ASSEMBLYAI_KEY},
@@ -189,18 +194,15 @@ def transcribe(audio_path: str) -> Dict:
             }
         )
         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)}")
@@ -214,35 +216,27 @@ def process_utterance(utterance, full_audio, wav_file):
         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
-
-        # --- FIX: Convert embedding to a flat list for Pinecone query ---
+            embedding = speaker_model.get_embedding(temp_path).cpu().numpy()
         embedding_list = embedding.flatten().tolist()
-        # --- End FIX ---
-
         query_result = index.query(
-            vector=embedding_list,  # Use the corrected flat list
+            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})])  # Use corrected list
-
+            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  # Store the corrected list
+            'embedding': embedding_list
         }
     except Exception as e:
         logger.error(f"Utterance processing failed: {str(e)}", exc_info=True)
@@ -258,14 +252,12 @@ 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
+        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)}")
@@ -277,26 +269,16 @@ def train_role_classifier(utterances: List[Dict]):
         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'],
+                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('?')),
@@ -305,25 +287,17 @@ def train_role_classifier(utterances: List[Dict]):
                 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'
+            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)}")
@@ -334,24 +308,15 @@ 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'],
+                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('?')),
@@ -360,12 +325,9 @@ def classify_roles(utterances: List[Dict], clf, vectorizer, scaler):
                 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)}")
@@ -375,90 +337,54 @@ def classify_roles(utterances: List[Dict], clf, vectorizer, scaler):
 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_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
-
+        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
-
+        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'
-
+        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
-            }
+            '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)}")
@@ -466,187 +392,102 @@ def analyze_interviewee_voice(audio_path: str, utterances: List[Dict]) -> Dict:
 
 
 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.")
-
+    interpretation_lines = [
+        "Voice Analysis Summary:",
+        f"- Speaking Rate: {analysis['speaking_rate']} words/sec (average)",
+        f"- Filler Words: {analysis['filler_ratio'] * 100:.1f}% of words",
+        f"- Repetition Score: {analysis['repetition_score']:.3f}",
+        f"- Anxiety Level: {analysis['interpretation']['anxiety_level'].upper()} (score: {analysis['composite_scores']['anxiety']:.3f})",
+        f"- Confidence Level: {analysis['interpretation']['confidence_level'].upper()} (score: {analysis['composite_scores']['confidence']:.3f})",
+        f"- Fluency: {analysis['interpretation']['fluency_level'].upper()}",
+        "",
+        "Detailed Interpretation:",
+        "1. A higher speaking rate indicates faster speech, which can suggest nervousness or enthusiasm.",
+        "2. Filler words and repetitions reduce speech clarity and professionalism.",
+        "3. Anxiety is measured through pitch variability and voice instability.",
+        "4. Confidence is assessed through voice intensity and stability.",
+        "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):
+def generate_anxiety_confidence_chart(composite_scores: Dict, chart_path_or_buffer):
     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
+        fig, ax = plt.subplots(figsize=(4, 2.5))
         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
-
+        ax.set_ylim(0, 1.0)
         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
+        plt.savefig(chart_path_or_buffer, format='png', bbox_inches='tight')
+        plt.close(fig)
     except Exception as e:
         logger.error(f"Error generating chart: {str(e)}")
 
 
-# --- Acceptance Probability Calculation ---
 def calculate_acceptance_probability(analysis_data: Dict) -> float:
-    """
-    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.
-    """
     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
+    if 'error' in voice: return 0.0
+    w_confidence, w_anxiety, w_fluency, w_speaking_rate, w_filler_repetition, w_content_strengths = 0.4, -0.3, 0.2, 0.1, -0.1, 0.2
     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
+    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)
-
-    # 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
+    content_strength_val = 0.8 if analysis_data.get('text_analysis', {}).get('total_duration', 0) > 0 else 0.0
+    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)
+    if max_possible_score == 0: return 50.0
+    normalized_score = raw_score / max_possible_score
+    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"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
-
+        interviewee_responses = [f"Speaker {u['speaker']} ({u['role']}): {u['text']}" for u in analysis_data['transcript'] if u['role'] == 'Interviewee'][:5]
         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."
-
+            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 '- '.
-
+        As EvalBot, an AI interview analysis system, generate a highly professional, well-structured, and concise interview analysis report. 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)}
-
         **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)
+        Focus on: Communication Skills, Content Delivery, Professional Presentation.
         """
-
         response = gemini_model.generate_content(prompt)
         return response.text
     except Exception as e:
@@ -654,73 +495,39 @@ def generate_report(analysis_data: Dict) -> str:
         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'))
+        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)
-
+        bullet_style = ParagraphStyle(name='Bullet', parent=body_text, 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)
+        acceptance_prob = analysis_data.get('acceptance_probability')
         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))
+            prob_color = colors.green if acceptance_prob >= 70 else (colors.orange if acceptance_prob >= 40 else colors.red)
+            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)))
+            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.", body_text))
+            else: story.append(Paragraph("This candidate may require significant development or may not be an ideal fit.", 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'
+            r'^\s*\*\*\s*3\.\s*Content Analysis & Strengths/Areas for Development\s*\*\*': 'Content Analysis',
+            r'^\s*\*\*\s*4\.\s*Actionable Recommendations\s*\*\*': 'Recommendations'
         }
-
         for line in gemini_report_text.split('\n'):
             matched_section = False
             for pattern, section_name in section_patterns.items():
@@ -731,132 +538,52 @@ def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text:
                     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))
+        
+        story.append(PageBreak()) # Start detailed report on a new page
+        
+        story.append(Paragraph("<b>1. Detailed Voice Analysis</b>", 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}"],
+                ['Filler Words', f"{voice_analysis['filler_ratio'] * 100:.1f}%", '% of total words'],
+                ['Repetition Score', f"{voice_analysis['repetition_score']:.3f}", 'Lower is better'],
+                ['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'),
+            table = Table(table_data, colWidths=[1.5*inch, 1.5*inch, 3*inch])
+            table.setStyle(TableStyle([
+                ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#4682B4')),
+                ('TEXTCOLOR',(0,0),(-1,0),colors.whitesmoke),
+                ('ALIGN', (0,0), (-1,-1), 'CENTER'),
                 ('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)
+                ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
+                ('BACKGROUND', (0, 1), (-1, -1), colors.HexColor('#F0F8FF')),
+                ('GRID', (0,0), (-1,-1), 1, colors.black)
+            ]))
             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")
-            # --- FIX: Call generate_anxiety_confidence_chart if it is defined and imports are correct ---
-            try:
-                # The generate_anxiety_confidence_chart function is now expected to be defined.
-                # It relies on matplotlib and Image (from reportlab.platypus)
-                generate_anxiety_confidence_chart(voice_analysis['composite_scores'], chart_path)
-                if os.path.exists(chart_path):
-                    img = Image(chart_path, width=3.5 * inch, height=2.0 * inch)
-                    story.append(img)
-                    story.append(Spacer(1, 0.1 * inch))
-                    os.remove(chart_path)
-            except NameError:  # Catch NameError if function is truly not defined
-                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))
-
+            chart_buffer = io.BytesIO()
+            generate_anxiety_confidence_chart(voice_analysis['composite_scores'], chart_buffer)
+            chart_buffer.seek(0)
+            img = Image(chart_buffer, width=4*inch, height=2.5*inch)
+            story.append(img)
         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')
-        )))
-
+            story.append(Paragraph("Voice analysis not available.", body_text))
+
+        story.append(PageBreak())
+        
+        for section_title, key in [("2. Content Analysis", "Content Analysis"), ("3. Recommendations", "Recommendations")]:
+            story.append(Paragraph(f"<b>{section_title}</b>", h2))
+            if key in sections:
+                for line in sections[key]:
+                    if line.strip():
+                        story.append(Paragraph(line.strip().lstrip('-').strip(), bullet if line.strip().startswith('-') else body_text))
+            story.append(Spacer(1, 0.2*inch))
+        
         doc.build(story)
         return True
     except Exception as e:
@@ -865,53 +592,45 @@ def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text:
 
 
 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()
+    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(i) for i in obj]
+    if isinstance(obj, np.ndarray): return obj.tolist()
     return obj
 
-
-def process_interview(audio_path: str):
+# --- MODIFIED MAIN FUNCTION ---
+def process_interview(audio_path_or_url: str):
+    local_audio_path = None
+    wav_file = None
+    is_downloaded = False
     try:
-        logger.info(f"Starting processing for {audio_path}")
-
-        wav_file = convert_to_wav(audio_path)
-
-        logger.info("Starting transcription")
+        logger.info(f"Starting processing for {audio_path_or_url}")
+        
+        if audio_path_or_url.startswith(('http://', 'https://')):
+            local_audio_path = download_audio_from_url(audio_path_or_url)
+            is_downloaded = True
+        else:
+            local_audio_path = audio_path_or_url
+        
+        wav_file = convert_to_wav(local_audio_path)
         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")
+            utterance['prosodic_features'] = extract_prosodic_features(wav_file, utterance['start'], utterance['end'])
+            
         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.
+        
+        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)
-
-        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)),
@@ -921,34 +640,32 @@ def process_interview(audio_path: str):
                 '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")
+        
+        analysis_data['acceptance_probability'] = calculate_acceptance_probability(analysis_data)
         gemini_report_text = generate_report(analysis_data)
-
-        base_name = os.path.splitext(os.path.basename(audio_path))[0]
+        
+        base_name = str(uuid.uuid4())
         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")
+
+        create_pdf_report(analysis_data, pdf_path, gemini_report_text=gemini_report_text)
+        
         with open(json_path, 'w') as f:
             serializable_data = convert_to_serializable(analysis_data)
             json.dump(serializable_data, f, indent=2)
+            
+        logger.info(f"Processing completed for {audio_path_or_url}")
+        
+        return {'pdf_path': pdf_path, 'json_path': json_path}
 
-        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):
+        logger.error(f"Processing failed for {audio_path_or_url}: {str(e)}", exc_info=True)
+        raise
+
+    finally:
+        if wav_file and os.path.exists(wav_file):
             os.remove(wav_file)
-        raise
+        if is_downloaded and local_audio_path and os.path.exists(local_audio_path):
+            os.remove(local_audio_path)
+            logger.info(f"Cleaned up temporary downloaded file: {local_audio_path}")
+# --- END MODIFIED MAIN FUNCTION ---