Rename app.py: to app.py

app.py

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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):
+    with open(audio_file, "rb") as audio:
+        transcription = speech_to_text(audio.read())
+        return transcription['text']
+
+# 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):
+    result = health_assessment(text, candidate_labels=health_conditions)
+    return result
+
+# 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."
+
+# 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)
+
+# 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}.")
+
+# Step 7: Main Function to Process User's Voice Input
+def analyze_voice_health(audio_file, user_first_name, user_last_name, user_email):
+    # Step 1: Convert speech to text
+    text = convert_speech_to_text(audio_file)
+    print(f"User's Speech Transcription: {text}")
+
+    # Step 2: Analyze the transcribed text for health conditions
+    health_feedback = analyze_health_condition(text)
+    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}")