Create app.py

app.py

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+import gradio as gr
+import librosa
+import numpy as np
+import torch
+from transformers import Wav2Vec2Processor, Wav2Vec2Model
+import requests
+import json
+import os
+from datetime import datetime
+
+# Salesforce API credentials (store securely in environment variables)
+SALESFORCE_API_URL = os.getenv("SALESFORCE_API_URL", "https://your-salesforce-instance.salesforce.com/services/data/v60.0/sobjects/HealthAssessment__c")
+SALESFORCE_TOKEN = os.getenv("SALESFORCE_TOKEN", "your_salesforce_token")
+
+# Load Wav2Vec2 model for speech feature extraction
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
+model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
+
+def analyze_voice(audio_file):
+    """Analyze voice for health indicators."""
+    try:
+        # Load audio file
+        audio, sr = librosa.load(audio_file, sr=16000)
+        
+        # Process audio for Wav2Vec2
+        inputs = processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        
+        # Extract features (simplified for demo; real-world needs trained classifier)
+        features = outputs.last_hidden_state.mean(dim=1).numpy()
+        
+        # Placeholder health analysis (replace with trained model)
+        respiratory_score = np.mean(features)  # Mock score
+        mental_health_score = np.std(features)  # Mock score
+        feedback = ""
+        if respiratory_score > 0.5:
+            feedback += "Possible respiratory issue detected; consult a doctor. "
+        if mental_health_score > 0.3:
+            feedback += "Possible stress indicators detected; consider professional advice. "
+        
+        if not feedback:
+            feedback = "No significant health indicators detected."
+        
+        feedback += "\n\n**Disclaimer**: This is not a diagnostic tool. Consult a healthcare provider for medical advice."
+        
+        # Store in Salesforce
+        store_in_salesforce(audio_file, feedback, respiratory_score, mental_health_score)
+        
+        return feedback
+    except Exception as e:
+        return f"Error processing audio: {str(e)}"
+
+def store_in_salesforce(audio_file, feedback, respiratory_score, mental_health_score):
+    """Store analysis results in Salesforce."""
+    headers = {
+        "Authorization": f"Bearer {SALESFORCE_TOKEN}",
+        "Content-Type": "application/json"
+    }
+    data = {
+        "AssessmentDate__c": datetime.utcnow().isoformat(),
+        "Feedback__c": feedback,
+        "RespiratoryScore__c": float(respiratory_score),
+        "MentalHealthScore__c": float(mental_health_score),
+        "AudioFileName__c": os.path.basename(audio_file)
+    }
+    response = requests.post(SALESFORCE_API_URL, headers=headers, json=data)
+    if response.status_code != 201:
+        print(f"Failed to store in Salesforce: {response.text}")
+
+# Gradio interface
+iface = gr.Interface(
+    fn=analyze_voice,
+    inputs=gr.Audio(type="filepath", label="Record or Upload Voice"),
+    outputs=gr.Textbox(label="Health Assessment Feedback"),
+    title="Health Voice Analyzer",
+    description="Record or upload a voice sample for preliminary health assessment. Supports English, Spanish, Hindi, Mandarin."
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)