app.py

import gradio as gr
import librosa
import numpy as np
import os
import hashlib
from datetime import datetime
from transformers import pipeline
import soundfile as sf
import torch

# Initialize local models
try:
    # Whisper for speech-to-text (English-only)
    whisper = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en", device=-1)  # CPU; use device=0 for GPU
    print("Whisper model loaded successfully.")
except Exception as e:
    print(f"Failed to load Whisper model: {str(e)}")
    whisper = None

try:
    # Symptom-2-Disease for health analysis
    symptom_classifier = pipeline("text-classification", model="abhirajeshbhai/symptom-2-disease-net", device=-1)  # CPU
    print("Symptom-2-Disease model loaded successfully.")
except Exception as e:
    print(f"Failed to load Symptom-2-Disease model: {str(e)}")
    symptom_classifier = None

def compute_file_hash(file_path):
    """Compute MD5 hash of a file to check uniqueness."""
    hash_md5 = hashlib.md5()
    with open(file_path, "rb") as f:
        for chunk in iter(lambda: f.read(4096), b""):
            hash_md5.update(chunk)
    return hash_md5.hexdigest()

def transcribe_audio(audio_file):
    """Transcribe audio using local Whisper model."""
    if not whisper:
        return "Error: Whisper model not loaded. Check logs for details."
    try:
        # Load and resample audio to 16,000 Hz
        audio, sr = librosa.load(audio_file, sr=16000)
        # Save as WAV for Whisper compatibility
        temp_wav = f"/tmp/{os.path.basename(audio_file)}.wav"
        sf.write(temp_wav, audio, sr)
        
        # Transcribe
        result = whisper(temp_wav)
        transcription = result.get("text", "").strip()
        print(f"Transcription: {transcription}")
        
        # Clean up temp file
        try:
            os.remove(temp_wav)
        except Exception:
            pass
        
        if not transcription:
            return "Transcription empty. Please provide clear audio describing symptoms in English."
        return transcription
    except Exception as e:
        return f"Error transcribing audio: {str(e)}"

def analyze_symptoms(text):
    """Analyze symptoms using local Symptom-2-Disease model."""
    if not symptom_classifier:
        return "Error: Symptom-2-Disease model not loaded. Check logs for details.", 0.0
    try:
        if not text or "Error transcribing" in text:
            return "No valid transcription for analysis.", 0.0
        result = symptom_classifier(text)
        if result and isinstance(result, list) and len(result) > 0:
            prediction = result[0]["label"]
            score = result[0]["score"]
            print(f"Health Prediction: {prediction}, Score: {score:.4f}")
            return prediction, score
        return "No health condition predicted", 0.0
    except Exception as e:
        return f"Error analyzing symptoms: {str(e)}", 0.0

def analyze_voice(audio_file):
    """Analyze voice for health indicators."""
    try:
        # Ensure unique file name to avoid Gradio reuse
        unique_path = f"/tmp/gradio/{datetime.now().strftime('%Y%m%d%H%M%S%f')}_{os.path.basename(audio_file)}"
        os.rename(audio_file, unique_path)
        audio_file = unique_path
        
        # Log audio file info
        file_hash = compute_file_hash(audio_file)
        print(f"Processing audio file: {audio_file}, Hash: {file_hash}")
        
        # Load audio to verify format
        audio, sr = librosa.load(audio_file, sr=16000)
        print(f"Audio shape: {audio.shape}, Sampling rate: {sr}, Duration: {len(audio)/sr:.2f}s, Mean: {np.mean(audio):.4f}, Std: {np.std(audio):.4f}")
        
        # Transcribe audio
        transcription = transcribe_audio(audio_file)
        if "Error transcribing" in transcription:
            return transcription
        
        # Analyze symptoms
        prediction, score = analyze_symptoms(transcription)
        if "Error analyzing" in prediction:
            return prediction
        
        # Generate feedback
        if prediction == "No health condition predicted":
            feedback = "No significant health indicators detected."
        else:
            feedback = f"Possible health condition: {prediction} (confidence: {score:.4f}). Consult a doctor."
        
        feedback += f"\n\n**Debug Info**: Transcription = '{transcription}', Prediction = {prediction}, Confidence = {score:.4f}, File Hash = {file_hash}"
        feedback += "\n**Disclaimer**: This is not a diagnostic tool. Consult a healthcare provider for medical advice."
        
        # Clean up temporary audio file
        try:
            os.remove(audio_file)
            print(f"Deleted temporary audio file: {audio_file}")
        except Exception as e:
            print(f"Failed to delete audio file: {str(e)}")
        
        return feedback
    except Exception as e:
        return f"Error processing audio: {str(e)}"

def test_with_sample_audio():
    """Test the app with sample audio files."""
    samples = ["audio_samples/sample.wav", "audio_samples/common_voice_en.wav"]
    results = []
    for sample in samples:
        if os.path.exists(sample):
            results.append(analyze_voice(sample))
        else:
            results.append(f"Sample not found: {sample}")
    return "\n".join(results)

# 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 describing symptoms for preliminary health assessment. Supports English (transcription), with symptom analysis in English."
)

if __name__ == "__main__":
    print(test_with_sample_audio())
    iface.launch(server_name="0.0.0.0", server_port=7860)