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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}")
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