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| import os | |
| from transformers import pipeline | |
| 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): | |
| """ 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") | |
| # Define possible health conditions | |
| health_conditions = ["respiratory issues", "mental health conditions", "fever", "asthma", "coughing"] | |
| def analyze_health_condition(text): | |
| """ 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): | |
| """ 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): | |
| """ 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): | |
| """ Store user data to Salesforce """ | |
| try: | |
| # Salesforce login credentials | |
| 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 | |
| feedback = provide_feedback(health_feedback) | |
| print(f"Feedback: {feedback}") | |
| # Step 4: Convert the feedback to speech for accessibility | |
| text_to_speech(feedback) | |
| # Step 5: Store the user interaction data in Salesforce (optional) | |
| store_user_data_to_salesforce(user_first_name, user_last_name, user_email, feedback) | |
| return feedback | |
| # Example Usage: | |
| # Assuming you have a user's voice recorded in "user_voice.wav" | |
| audio_file = "user_voice.wav" | |
| user_first_name = "John" | |
| user_last_name = "Doe" | |
| user_email = "[email protected]" | |
| # Analyze the user's voice and provide feedback | |
| feedback = analyze_voice_health(audio_file, user_first_name, user_last_name, user_email) | |
| print(f"Final Feedback: {feedback}") | |