Update app.py

app.py

@@ -29,12 +29,12 @@ logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %
 logger = logging.getLogger(__name__)
 usage_metrics = {"total_assessments": 0, "assessments_by_language": {}}
 
-# Environment variables
+# Environment variables with fallbacks
 SF_USERNAME = os.getenv("SF_USERNAME", "[email protected]")
 SF_PASSWORD = os.getenv("SF_PASSWORD", "voicebot1")
 SF_SECURITY_TOKEN = os.getenv("SF_SECURITY_TOKEN", "jq4VVHUFti6TmzJDjjegv2h6b")
 SF_INSTANCE_URL = os.getenv("SF_INSTANCE_URL", "https://swe42.sfdc-cehfhs.salesforce.com")
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyBzr5vVpbe8CV1v70l3pGDp9vRJ76yCxdk")
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "your_gemini_api_key_here")  # Placeholder, replace in Dockerfile
 ENCRYPTION_KEY = os.getenv("ENCRYPTION_KEY", Fernet.generate_key().decode())
 DEFAULT_EMAIL = os.getenv("SALESFORCE_USER_EMAIL", "[email protected]")
 
@@ -42,6 +42,7 @@ DEFAULT_EMAIL = os.getenv("SALESFORCE_USER_EMAIL", "[email protected]")
 cipher = Fernet(ENCRYPTION_KEY)
 
 # Initialize Salesforce
+sf = None
 try:
     sf = Salesforce(
         username=SF_USERNAME,
@@ -52,17 +53,19 @@ try:
     logger.info(f"Connected to Salesforce at {SF_INSTANCE_URL}")
 except Exception as e:
     logger.error(f"Salesforce connection failed: {str(e)}")
-    sf = None
 
 # Initialize Google Gemini
-try:
-    genai.configure(api_key=GEMINI_API_KEY)
-    gemini_model = genai.GenerativeModel('gemini-1.5-flash')
-    chat = gemini_model.start_chat(history=[])
-    logger.info("Connected to Google Gemini")
-except Exception as e:
-    logger.error(f"Google Gemini initialization failed: {str(e)}")
-    chat = None
+chat = None
+if GEMINI_API_KEY and GEMINI_API_KEY != "your_gemini_api_key_here":
+    try:
+        genai.configure(api_key=GEMINI_API_KEY)
+        gemini_model = genai.GenerativeModel('gemini-1.5-flash')
+        chat = gemini_model.start_chat(history=[])
+        logger.info("Connected to Google Gemini")
+    except Exception as e:
+        logger.error(f"Google Gemini initialization failed: {str(e)}")
+else:
+    logger.warning("GEMINI_API_KEY not set or invalid, using fallback suggestions")
 
 # Load Whisper model
 SUPPORTED_LANGUAGES = {"English": "english", "Hindi": "hindi", "Spanish": "spanish", "Mandarin": "mandarin"}
@@ -143,7 +146,7 @@ def cached_transcribe(audio_file, language):
 def extract_health_features(audio, sr):
     try:
         audio = librosa.util.normalize(audio)
-        frame_duration = 30
+        frame_duration = 20
         frame_samples = int(sr * frame_duration / 1000)
         frames = [audio[i:i + frame_samples] for i in range(0, len(audio), frame_samples)]
         voiced_frames = [frame for frame in frames if len(frame) == frame_samples and vad.is_speech((frame * 32768).astype(np.int16).tobytes(), sr)]
@@ -151,19 +154,19 @@ def extract_health_features(audio, sr):
             raise ValueError("No voiced segments detected")
         voiced_audio = np.concatenate(voiced_frames)
         
-        frame_step = max(1, len(voiced_audio) // (sr // 8))  # Reduced sampling for faster processing
+        frame_step = max(1, len(voiced_audio) // (sr // 6))
         pitches, magnitudes = librosa.piptrack(y=voiced_audio[::frame_step], sr=sr, fmin=75, fmax=300)
         valid_pitches = [p for p in pitches[magnitudes > 0] if 75 <= p <= 300]
         pitch = np.mean(valid_pitches) if valid_pitches else 0
         jitter = np.std(valid_pitches) / pitch if pitch and valid_pitches else 0
         jitter = min(jitter, 10)
-        amplitudes = librosa.feature.rms(y=voiced_audio, frame_length=512, hop_length=128)[0]
+        amplitudes = librosa.feature.rms(y=voiced_audio, frame_length=256, hop_length=128)[0]
         shimmer = np.std(amplitudes) / np.mean(amplitudes) if np.mean(amplitudes) else 0
         shimmer = min(shimmer, 10)
         energy = np.mean(amplitudes)
         
-        mfcc = np.mean(librosa.feature.mfcc(y=voiced_audio[::4], sr=sr, n_mfcc=4), axis=1)  # Reduced sampling
-        spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=voiced_audio[::4], sr=sr, n_fft=512, hop_length=128))
+        mfcc = np.mean(librosa.feature.mfcc(y=voiced_audio[::2], sr=sr, n_mfcc=3), axis=1)
+        spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=voiced_audio[::2], sr=sr, n_fft=256, hop_length=128))
         
         logger.debug(f"Extracted features: pitch={pitch:.2f}, jitter={jitter*100:.2f}%, shimmer={shimmer*100:.2f}%, energy={energy:.4f}, mfcc_mean={np.mean(mfcc):.2f}, spectral_centroid={spectral_centroid:.2f}")
         return {
@@ -183,9 +186,9 @@ def transcribe_audio(audio, language="en"):
         whisper_model.config.forced_decoder_ids = whisper_processor.get_decoder_prompt_ids(
             language=SUPPORTED_LANGUAGES.get({"en": "English", "hi": "Hindi", "es": "Spanish", "zh": "Mandarin"}.get(language, "English"), "english"), task="transcribe"
         )
-        inputs = whisper_processor(audio, sampling_rate=16000, return_tensors="pt")
+        inputs = whisper_processor(audio[:16000 * 5], sampling_rate=16000, return_tensors="pt")
         with torch.no_grad():
-            generated_ids = whisper_model.generate(inputs["input_features"], max_new_tokens=30)  # Reduced tokens for speed
+            generated_ids = whisper_model.generate(inputs["input_features"], max_new_tokens=50)
         transcription = whisper_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
         logger.info(f"Transcription (language: {language}): {transcription}")
         return transcription
@@ -195,140 +198,104 @@ def transcribe_audio(audio, language="en"):
 
 async def get_chatbot_response(message, language="en"):
     if not chat or not message:
-        return "Unable to generate response.", None
+        fallback_suggestions = (
+            "- Consult a healthcare professional for personalized advice.\n"
+            "- Maintain a balanced diet.\n"
+            "- Stay hydrated.\n"
+            "- Get adequate sleep.\n"
+            "- Exercise regularly.\n"
+            "- Practice stress management techniques.\n"
+            "- Monitor symptoms daily.\n"
+            "- Seek support if needed."
+        )
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
+            tts = gTTS(text=fallback_suggestions, lang={"English": "en", "Hindi": "hi", "Spanish": "es", "Mandarin": "zh"}.get(language, "en"), slow=False)
+            tts.save(temp_audio.name)
+            audio_path = temp_audio.name
+        logger.warning("Chatbot unavailable, using fallback suggestions")
+        return fallback_suggestions, audio_path
+    
     language_code = {"English": "en", "Hindi": "hi", "Spanish": "es", "Mandarin": "zh"}.get(language, "en")
-    full_context = "\n".join(context) + f"\nUser: {message}\nMindCare: Provide response in 6-8 simple bullet points, tailored to the user's input, in a clear and empathetic tone."
+    full_context = "\n".join(context) + f"\nUser: {message}\nMindCare:"
     try:
-        response = await asyncio.get_event_loop().run_in_executor(None, lambda: chat.send_message(full_context).text)
+        response = await asyncio.get_event_loop().run_in_executor(None, lambda: chat.send_message(
+            f"{full_context}\nBased on the health assessment, provide 8 specific health suggestions in bullet points:"
+        ).text)
+        suggestions = "\n".join(["- " + line.strip() for line in response.split("\n") if line.strip()][:8])
+        if len(suggestions.split("\n")) < 8:
+            suggestions += "\n- Consult a healthcare professional for personalized advice.\n- Maintain a balanced diet.\n- Stay hydrated.\n- Get adequate sleep.\n- Exercise regularly.\n- Practice stress management techniques.\n- Monitor symptoms daily.\n- Seek support if needed."
+        
         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
-            tts = gTTS(text=response, lang=language_code, slow=False)
+            tts = gTTS(text=suggestions, lang=language_code, slow=False)
             tts.save(temp_audio.name)
             audio_path = temp_audio.name
-        logger.info(f"Generated response: {response[:100]}... and audio at {audio_path}")
-        return response, audio_path
+        logger.info(f"Generated response: {suggestions[:100]}... and audio at {audio_path}")
+        return suggestions, audio_path
     except Exception as e:
         logger.error(f"Chatbot response failed: {str(e)}")
-        return "Error generating response. Please check your input or API key.", None
+        fallback_suggestions = (
+            "- Consult a healthcare professional for personalized advice.\n"
+            "- Maintain a balanced diet.\n"
+            "- Stay hydrated.\n"
+            "- Get adequate sleep.\n"
+            "- Exercise regularly.\n"
+            "- Practice stress management techniques.\n"
+            "- Monitor symptoms daily.\n"
+            "- Seek support if needed."
+        )
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
+            tts = gTTS(text=fallback_suggestions, lang=language_code, slow=False)
+            tts.save(temp_audio.name)
+            audio_path = temp_audio.name
+        return fallback_suggestions, audio_path
 
 def analyze_symptoms(text, features):
     feedback = []
-    suggestions = []
     text = text.lower() if text else ""
     
-    # Generate health assessment feedback
     if "cough" in text or "coughing" in text:
-        feedback.append("You mentioned a cough, which may suggest a cold or respiratory issue.")
-        suggestions.extend([
-            "• Drink warm fluids like herbal tea or water to soothe your throat.",
-            "• Rest to help your body recover from possible infection.",
-            "• Use a humidifier to ease throat irritation.",
-            "• Consider over-the-counter cough remedies, but consult a doctor first.",
-            "• Monitor symptoms; see a doctor if the cough lasts over a week."
-        ])
+        feedback.append("You mentioned a cough. This could indicate a respiratory issue like a cold or bronchitis. Stay hydrated, rest, and consider consulting a doctor if it persists.")
     elif "fever" in text or "temperature" in text:
-        feedback.append("You mentioned a fever, which could indicate an infection.")
-        suggestions.extend([
-            "• Stay hydrated with water or electrolyte drinks.",
-            "• Rest to support your immune system.",
-            "• Monitor your temperature regularly.",
-            "• Use paracetamol to reduce fever, but follow dosage instructions.",
-            "• Seek medical advice if fever exceeds 100.4°F (38°C) for over 2 days."
-        ])
+        feedback.append("You mentioned a fever. This could be a sign of infection or illness. Monitor your temperature, stay hydrated, and seek medical advice if it exceeds 100.4°F (38°C).")
     elif "headache" in text:
-        feedback.append("You mentioned a headache, possibly due to stress or dehydration.")
-        suggestions.extend([
-            "• Drink plenty of water to stay hydrated.",
-            "• Take short breaks to relax your mind.",
-            "• Try a mild pain reliever like ibuprofen, but consult a doctor.",
-            "• Practice deep breathing to reduce tension.",
-            "• Ensure you're getting enough sleep (7-8 hours)."
-        ])
+        feedback.append("You mentioned a headache. This could be due to stress, dehydration, or tension. Try resting, drinking water, and using a mild pain reliever like Paracetamol. Consult a doctor if severe.")
     elif "stress" in text or "anxious" in text or "mental stress" in text:
-        feedback.append("You mentioned stress or anxiety, which can affect well-being.")
-        suggestions.extend([
-            "• Try 5 minutes of deep breathing to calm your mind.",
-            "• Write in a journal to process your thoughts.",
-            "• Take a short walk in nature to relax.",
-            "• Practice mindfulness or meditation daily.",
-            "• Talk to a trusted friend or professional for support.",
-            "• Prioritize sleep and avoid excessive caffeine."
-        ])
+        feedback.append("You mentioned stress or anxiety. Try deep breathing exercises or mindfulness. If persistent, consider speaking with a mental health professional for support.")
     elif "respiratory" in text or "breathing" in text or "shortness of breath" in text:
-        feedback.append("You mentioned breathing issues, which may indicate asthma or infection.")
-        suggestions.extend([
-            "• Avoid triggers like smoke or allergens.",
-            "• Practice slow, deep breathing exercises.",
-            "• Stay in a well-ventilated area.",
-            "• Monitor symptoms and seek medical help if severe.",
-            "• Rest to reduce strain on your respiratory system."
-        ])
+        feedback.append("You mentioned respiratory issues or shortness of breath. This could indicate asthma or an infection. Seek medical attention if it worsens.")
     elif "cold" in text:
-        feedback.append("You mentioned a cold, likely a viral infection.")
-        suggestions.extend([
-            "• Drink warm fluids like soup or tea.",
-            "• Rest to help your body fight the virus.",
-            "• Use saline nasal spray to relieve congestion.",
-            "• Take over-the-counter cold remedies, but consult a doctor.",
-            "• Stay hydrated and avoid strenuous activity."
-        ])
+        feedback.append("You mentioned a cold. This could be a viral infection. Rest, stay hydrated, and consider over-the-counter remedies like decongestants or honey for cough. Consult a doctor if symptoms worsen.")
     
-    # Voice feature-based feedback and suggestions
     if features["jitter"] > 6.5:
-        feedback.append(f"High jitter ({features['jitter']:.2f}%) suggests vocal strain or respiratory issues.")
-        suggestions.append("• Rest your voice and avoid shouting.")
+        feedback.append(f"High jitter ({features['jitter']:.2f}%) detected, suggesting potential vocal cord strain or respiratory issues. Consult a doctor.")
     elif features["jitter"] > 4.0:
-        feedback.append(f"Moderate jitter ({features['jitter']:.2f}%) indicates possible vocal instability.")
-        suggestions.append("• Sip warm water to soothe your vocal cords.")
+        feedback.append(f"Moderate jitter ({features['jitter']:.2f}%) detected, indicating possible vocal instability. Monitor and rest your voice.")
     
     if features["shimmer"] > 7.5:
-        feedback.append(f"High shimmer ({features['shimmer']:.2f}%) may indicate emotional stress.")
-        suggestions.append("• Try relaxation techniques like yoga or meditation.")
+        feedback.append(f"High shimmer ({features['shimmer']:.2f}%) suggests possible emotional stress or vocal fatigue. Consider professional evaluation.")
     elif features["shimmer"] > 5.0:
-        feedback.append(f"Moderate shimmer ({features['shimmer']:.2f}%) suggests mild vocal strain.")
-        suggestions.append("• Stay hydrated to support vocal health.")
+        feedback.append(f"Moderate shimmer ({features['shimmer']:.2f}%) indicates mild vocal strain. Rest and hydrate recommended.")
     
     if features["energy"] < 0.003:
-        feedback.append(f"Low vocal energy ({features['energy']:.4f}) may indicate fatigue.")
-        suggestions.append("• Ensure 7-8 hours of sleep nightly.")
+        feedback.append(f"Very low vocal energy ({features['energy']:.4f}) detected, possibly indicating fatigue or low mood. Rest and consult a doctor if needed.")
     elif features["energy"] < 0.007:
-        feedback.append(f"Low vocal energy ({features['energy']:.4f}) suggests possible tiredness.")
-        suggestions.append("• Take short naps to boost energy.")
+        feedback.append(f"Low vocal energy ({features['energy']:.4f}) suggests possible fatigue. Ensure adequate rest.")
     
     if features["pitch"] < 70 or features["pitch"] > 290:
-        feedback.append(f"Unusual pitch ({features['pitch']:.2f} Hz) may indicate vocal issues.")
-        suggestions.append("• Consult a doctor for a vocal health check.")
+        feedback.append(f"Unusual pitch ({features['pitch']:.2f} Hz) detected, which may indicate vocal cord issues. Consult a doctor.")
     elif 70 <= features["pitch"] <= 90 or 270 <= features["pitch"] <= 290:
-        feedback.append(f"Pitch ({features['pitch']:.2f} Hz) is slightly outside typical range.")
-        suggestions.append("• Avoid straining your voice during conversations.")
+        feedback.append(f"Pitch ({features['pitch']:.2f} Hz) is slightly outside typical range, possibly due to tension. Monitor your voice.")
     
     if features["spectral_centroid"] > 2700:
-        feedback.append(f"High spectral centroid ({features['spectral_centroid']:.2f} Hz) suggests tense speech.")
-        suggestions.append("• Practice slow, calm speaking to reduce tension.")
+        feedback.append(f"High spectral centroid ({features['spectral_centroid']:.2f} Hz) suggests tense speech, potentially linked to stress or anxiety.")
     elif features["spectral_centroid"] > 2200:
-        feedback.append(f"Elevated spectral centroid ({features['spectral_centroid']:.2f} Hz) may indicate mild tension.")
-        suggestions.append("• Relax your jaw and shoulders while speaking.")
+        feedback.append(f"Elevated spectral centroid ({features['spectral_centroid']:.2f} Hz) may indicate mild tension in speech.")
     
     if not feedback:
-        feedback.append("No significant health concerns detected from voice or text analysis.")
-        suggestions.extend([
-            "• Maintain a balanced diet with fruits and vegetables.",
-            "• Exercise regularly for overall health.",
-            "• Stay hydrated with 8 glasses of water daily.",
-            "• Get 7-8 hours of sleep each night.",
-            "• Practice stress-relief techniques like meditation.",
-            "• Schedule regular health check-ups."
-        ])
+        feedback.append("No significant health concerns detected from voice or text analysis. Maintain a healthy lifestyle and consult a doctor if symptoms arise.")
     
-    # Ensure suggestions are limited to 6-8 unique items
-    suggestions = list(dict.fromkeys(suggestions))[:8]
-    if len(suggestions) < 6:
-        suggestions.extend([
-            "• Stay active with light exercise like walking.",
-            "• Practice gratitude to boost mental well-being."
-        ][:6 - len(suggestions)])
-    
-    logger.debug(f"Generated feedback: {feedback}, Suggestions: {suggestions}")
-    return "\n".join(feedback), "\n".join(suggestions)
+    logger.debug(f"Generated feedback: {feedback}")
+    return "\n".join(feedback)
 
 def store_user_consent(email, language):
     if not sf:
@@ -556,12 +523,16 @@ async def analyze_voice(audio_file=None, language="English", email=None):
     usage_metrics["total_assessments"] += 1
     usage_metrics["assessments_by_language"][language] = usage_metrics["assessments_by_language"].get(language, 0) + 1
 
+    async def timeout_handler():
+        await asyncio.sleep(20)
+        raise TimeoutError("Processing took too long (exceeded 20 seconds)")
+
     try:
         if not audio_file or not os.path.exists(audio_file):
             raise ValueError("No valid audio file provided")
         
         audio, sr = librosa.load(audio_file, sr=16000)
-        max_duration = 5  # Reduced from 10 to 5 seconds for faster processing
+        max_duration = 5
         if len(audio) > max_duration * sr:
             audio = audio[:max_duration * sr]
             logger.info(f"Truncated audio to first {max_duration} seconds for faster processing")
@@ -570,7 +541,7 @@ async def analyze_voice(audio_file=None, language="English", email=None):
 
         language_code = {"English": "en", "Hindi": "hi", "Spanish": "es", "Mandarin": "zh"}.get(language, "en")
         user_id = store_user_consent(email, language)
-        if not user_id:
+        if not user_id and sf:
             logger.warning("Proceeding with analysis despite consent storage failure")
             feedback_message = "Warning: User consent could not be stored in Salesforce, but analysis will proceed.\n"
         else:
@@ -578,7 +549,7 @@ async def analyze_voice(audio_file=None, language="English", email=None):
 
         features = extract_health_features(audio, sr)
         transcription = cached_transcribe(audio_file, language)
-        feedback, suggestions = analyze_symptoms(transcription, features)
+        feedback = analyze_symptoms(transcription, features)
 
         respiratory_score = features["jitter"]
         mental_health_score = features["shimmer"]
@@ -594,6 +565,14 @@ async def analyze_voice(audio_file=None, language="English", email=None):
         feedback += f"- Email: {email if email and email.strip() else DEFAULT_EMAIL}\n"
         feedback += "\n**Disclaimer**: This is a preliminary analysis. Consult a healthcare provider for professional evaluation."
 
+        task = asyncio.create_task(get_chatbot_response(feedback, language))
+        timeout_task = asyncio.create_task(timeout_handler())
+        suggestions, suggestion_audio = await asyncio.gather(task, timeout_task, return_exceptions=True)
+        if isinstance(suggestions, Exception):
+            raise suggestions
+        if isinstance(suggestion_audio, TimeoutError):
+            raise TimeoutError("Health suggestions generation timed out after 20 seconds")
+
         if user_id and sf:
             store_in_salesforce(user_id, audio_file, feedback, respiratory_score, mental_health_score, features, transcription, language)
         else:
@@ -602,16 +581,7 @@ async def analyze_voice(audio_file=None, language="English", email=None):
         file_path, pdf_error = generate_pdf_report(feedback, transcription, features, language, email, suggestions)
         if pdf_error:
             feedback += f"\n\n**Error**: {pdf_error}"
-            return feedback, file_path, suggestions, None
-
-        # Generate audio response based on suggestions
-        response_text = suggestions
-        response, audio_path = await get_chatbot_response(response_text, language)
-        if audio_path:
-            logger.info(f"Generated audio response at {audio_path}")
-        else:
-            logger.warning("Failed to generate audio response")
-            return feedback, file_path, response, None
+            return feedback, file_path, suggestions, suggestion_audio
 
         try:
             os.remove(audio_file)
@@ -619,7 +589,7 @@ async def analyze_voice(audio_file=None, language="English", email=None):
         except Exception as e:
             logger.error(f"Failed to delete audio file: {str(e)}")
 
-        return feedback, file_path, response, audio_path
+        return feedback, file_path, suggestions, suggestion_audio
     except Exception as e:
         logger.error(f"Audio processing failed: {str(e)}")
         return f"Error: {str(e)}", None, "Error: Could not generate suggestions due to audio processing failure.", None
@@ -680,16 +650,7 @@ def launch():
     """
 
     def check_microphone_access():
-        try:
-            from navigator import mediaDevices
-            devices = mediaDevices.enumerateDevices()
-            for device in devices:
-                if device.kind == "audioinput":
-                    return None
-            return "Microphone access is not available. Please upload an audio file or check browser permissions."
-        except Exception as e:
-            logger.error(f"Microphone access check failed: {str(e)}")
-            return "Microphone access is not available. Please upload an audio file or check browser permissions."
+        return "Microphone access is not available. Please upload an audio file or check browser permissions."
 
     with gr.Blocks(title="MindCare Health Assistant", css=custom_css) as demo:
         gr.Markdown("Record your voice or type a message for health assessments and suggestions.")
@@ -697,12 +658,11 @@ def launch():
         with gr.Row():
             with gr.Column():
                 gr.Markdown("### Voice Analysis")
-                mic_warning = gr.Markdown()
-                mic_warning.value = check_microphone_access() or ""
+                mic_warning = gr.Markdown(value=check_microphone_access())
                 gr.Markdown("Upload voice (1+ sec) describing symptoms (e.g., 'I have a cough' or 'I feel stressed'). Note: Microphone recording may not be supported in all contexts; use file upload instead.")
                 email_input = gr.Textbox(label="Enter Your Email", placeholder="e.g., [email protected]", value="")
                 language_input = gr.Dropdown(choices=list(SUPPORTED_LANGUAGES.keys()), label="Select Language", value="English")
-                consent_input = gr.Checkbox(label="I consent to data storage and voice analysis", value=True, interactive=False)
+                consent_input = gr.Checkbox(label="I consent to data storage and voice analysis", value=True, interactive=True)
                 audio_input = gr.Audio(type="filepath", label="Upload Voice (WAV, MP3, FLAC)", format="wav", interactive=True)
                 voice_output = gr.Textbox(label="Health Assessment Results", elem_id="health-results")
                 file_output = gr.File(label="Download Assessment Report (PDF)", file_types=[".pdf"])
@@ -741,4 +701,4 @@ def launch():
     demo.launch(server_name="0.0.0.0", server_port=7860)
 
 if __name__ == "__main__":
-    launch()
+    launch()