Update app.py

app.py

@@ -4,15 +4,19 @@ from gtts import gTTS
 from simple_salesforce import Salesforce
 import soundfile as sf
 
-
 # Step 1: Hugging Face Speech-to-Text Pipeline Setup
 speech_to_text = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-960h")
 
 # Step 2: Convert Speech to Text
 def convert_speech_to_text(audio_file):
-    with open(audio_file, "rb") as audio:
-        transcription = speech_to_text(audio.read())
-        return transcription['text']
+    """ Convert audio file to text """
+    try:
+        with open(audio_file, "rb") as audio:
+            transcription = speech_to_text(audio.read())
+            return transcription['text']
+    except Exception as e:
+        print(f"Error in converting speech to text: {e}")
+        return None
 
 # Step 3: Analyze the text for health-related indicators (e.g., respiratory issues)
 health_assessment = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
@@ -21,45 +25,73 @@ health_assessment = pipeline("zero-shot-classification", model="facebook/bart-la
 health_conditions = ["respiratory issues", "mental health conditions", "fever", "asthma", "coughing"]
 
 def analyze_health_condition(text):
-    result = health_assessment(text, candidate_labels=health_conditions)
-    return result
+    """ Analyze the health condition based on transcribed text """
+    try:
+        result = health_assessment(text, candidate_labels=health_conditions)
+        return result
+    except Exception as e:
+        print(f"Error in analyzing health condition: {e}")
+        return None
 
 # Step 4: Provide Feedback to the User Based on Health Assessment
 def provide_feedback(health_assessment_result):
-    if 'respiratory issues' in health_assessment_result['labels']:
-        return "Possible respiratory issue detected, consult a doctor."
-    elif 'mental health conditions' in health_assessment_result['labels']:
-        return "Possible mental health concern detected, seek professional help."
-    else:
-        return "No significant health concerns detected. Keep monitoring your health."
+    """ Provide feedback based on health assessment """
+    if health_assessment_result is None:
+        return "Error in analyzing health, please try again later."
+
+    try:
+        if 'respiratory issues' in health_assessment_result['labels']:
+            return "Possible respiratory issue detected, consult a doctor."
+        elif 'mental health conditions' in health_assessment_result['labels']:
+            return "Possible mental health concern detected, seek professional help."
+        else:
+            return "No significant health concerns detected. Keep monitoring your health."
+    except Exception as e:
+        print(f"Error in providing feedback: {e}")
+        return "An error occurred while processing your health assessment."
 
 # Step 5: Convert Text Feedback to Speech (Text-to-Speech)
 def text_to_speech(text):
-    tts = gTTS(text, lang='en')
-    tts.save("response.mp3")
-    os.system("start response.mp3")  # Play the audio file (Windows-specific command)
+    """ Convert text feedback to speech """
+    try:
+        tts = gTTS(text, lang='en')
+        tts.save("response.mp3")
+        os.system("start response.mp3")  # Play the audio file (Windows-specific command)
+    except Exception as e:
+        print(f"Error in text to speech: {e}")
 
 # Step 6: Integration with Salesforce for Storing User Data
 def store_user_data_to_salesforce(user_first_name, user_last_name, user_email, feedback):
-    sf = Salesforce(username='your_username', password='your_password', security_token='your_token')
-
-    # Create a new record for the user interaction in Salesforce
-    sf.Contact.create({
-        'FirstName': user_first_name,
-        'LastName': user_last_name,
-        'Email': user_email,
-        'VoiceAnalysisResult': feedback,
-    })
-    print(f"Data stored successfully for {user_first_name} {user_last_name}.")
+    """ Store user data to Salesforce """
+    try:
+        sf = Salesforce(username='your_username', password='your_password', security_token='your_token')
+
+        # Create a new record for the user interaction in Salesforce
+        sf.Contact.create({
+            'FirstName': user_first_name,
+            'LastName': user_last_name,
+            'Email': user_email,
+            'VoiceAnalysisResult': feedback,
+        })
+        print(f"Data stored successfully for {user_first_name} {user_last_name}.")
+    except Exception as e:
+        print(f"Error in storing data to Salesforce: {e}")
 
 # Step 7: Main Function to Process User's Voice Input
 def analyze_voice_health(audio_file, user_first_name, user_last_name, user_email):
+    """ Main function to analyze voice and provide feedback """
     # Step 1: Convert speech to text
     text = convert_speech_to_text(audio_file)
+    if text is None:
+        return "Error in transcribing the audio."
+
     print(f"User's Speech Transcription: {text}")
 
     # Step 2: Analyze the transcribed text for health conditions
     health_feedback = analyze_health_condition(text)
+    if health_feedback is None:
+        return "Error in analyzing health conditions."
+
     print(f"Health assessment: {health_feedback}")
 
     # Step 3: Provide feedback based on the health analysis