app_local_backup.py

import os
import sys
import torch
import numpy as np
import gradio as gr
import torchaudio
import torchvision
import spaces

# # Import Gradio Spaces GPU decorator
# try:
#     from gradio import spaces
#     HAS_SPACES = True
#     print("\033[92mINFO\033[0m: Gradio Spaces detected, GPU acceleration will be enabled")
# except ImportError:
#     HAS_SPACES = False
#     print("\033[93mWARN\033[0m: gradio.spaces not available, running without GPU optimization")

# Add parent directory to path to import preprocess functions
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

# Import functions from infer_watermelon.py and train_watermelon for the model
from train_watermelon import WatermelonModel

# Modified version of process_audio_data specifically for the app to handle various tensor shapes
def app_process_audio_data(waveform, sample_rate):
    """Modified version of process_audio_data for the app that handles different tensor dimensions"""
    try:
        print(f"\033[92mDEBUG\033[0m: Processing audio - Initial shape: {waveform.shape}, Sample rate: {sample_rate}")
        
        # Handle different tensor dimensions
        if waveform.dim() == 3:
            print(f"\033[92mDEBUG\033[0m: Found 3D tensor, converting to 2D")
            # For 3D tensor, take the first item (batch dimension)
            waveform = waveform[0]
            
        if waveform.dim() == 2:
            # Use the first channel for stereo audio
            waveform = waveform[0]
            print(f"\033[92mDEBUG\033[0m: Using first channel, new shape: {waveform.shape}")
        
        # Resample to 16kHz if needed
        resample_rate = 16000
        if sample_rate != resample_rate:
            print(f"\033[92mDEBUG\033[0m: Resampling from {sample_rate}Hz to {resample_rate}Hz")
            waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=resample_rate)(waveform)
        
        # Ensure 3 seconds of audio
        if waveform.size(0) < 3 * resample_rate:
            print(f"\033[92mDEBUG\033[0m: Padding audio from {waveform.size(0)} to {3 * resample_rate} samples")
            waveform = torch.nn.functional.pad(waveform, (0, 3 * resample_rate - waveform.size(0)))
        else:
            print(f"\033[92mDEBUG\033[0m: Trimming audio from {waveform.size(0)} to {3 * resample_rate} samples")
            waveform = waveform[: 3 * resample_rate]
        
        # Apply MFCC transformation
        print(f"\033[92mDEBUG\033[0m: Applying MFCC transformation")
        mfcc_transform = torchaudio.transforms.MFCC(
            sample_rate=resample_rate,
            n_mfcc=13,
            melkwargs={
                "n_fft": 256,
                "win_length": 256,
                "hop_length": 128,
                "n_mels": 40,
            }
        )
        
        mfcc = mfcc_transform(waveform)
        print(f"\033[92mDEBUG\033[0m: MFCC output shape: {mfcc.shape}")
        
        return mfcc
    except Exception as e:
        import traceback
        print(f"\033[91mERR!\033[0m: Error in audio processing: {e}")
        print(traceback.format_exc())
        return None

# Similarly for images, but let's import the original one
from preprocess import process_image_data

    # Using the decorator directly on the function definition
@spaces.GPU
def predict_sugar_content(audio, image, model_path):
    """Function with GPU acceleration to predict watermelon sugar content in Brix"""
    try:
        # Now check CUDA availability inside the GPU-decorated function
        if torch.cuda.is_available():
            device = torch.device("cuda")
            print(f"\033[92mINFO\033[0m: CUDA is available. Using device: {device}")
        else:
            device = torch.device("cpu")
            print(f"\033[92mINFO\033[0m: CUDA is not available. Using device: {device}")
        
        # Load model inside the function to ensure it's on the correct device
        model = WatermelonModel().to(device)
        model.load_state_dict(torch.load(model_path, map_location=device))
        model.eval()
        print(f"\033[92mINFO\033[0m: Loaded model from {model_path}")
        
        # Debug information about input types
        print(f"\033[92mDEBUG\033[0m: Audio input type: {type(audio)}")
        print(f"\033[92mDEBUG\033[0m: Audio input shape/length: {len(audio)}")
        print(f"\033[92mDEBUG\033[0m: Image input type: {type(image)}")
        if isinstance(image, np.ndarray):
            print(f"\033[92mDEBUG\033[0m: Image input shape: {image.shape}")
        
        # Handle different audio input formats
        if isinstance(audio, tuple) and len(audio) == 2:
            # Standard Gradio format: (sample_rate, audio_data)
            sample_rate, audio_data = audio
            print(f"\033[92mDEBUG\033[0m: Audio sample rate: {sample_rate}")
            print(f"\033[92mDEBUG\033[0m: Audio data shape: {audio_data.shape}")
        elif isinstance(audio, tuple) and len(audio) > 2:
            # Sometimes Gradio returns (sample_rate, audio_data, other_info...)
            sample_rate, audio_data = audio[0], audio[-1]
            print(f"\033[92mDEBUG\033[0m: Audio sample rate: {sample_rate}")
            print(f"\033[92mDEBUG\033[0m: Audio data shape: {audio_data.shape}")
        elif isinstance(audio, str):
            # Direct path to audio file
            audio_data, sample_rate = torchaudio.load(audio)
            print(f"\033[92mDEBUG\033[0m: Loaded audio from path with shape: {audio_data.shape}")
        else:
            return f"Error: Unsupported audio format. Got {type(audio)}"
        
        # Create a temporary file path for the audio and image
        temp_dir = "temp"
        os.makedirs(temp_dir, exist_ok=True)
        
        temp_audio_path = os.path.join(temp_dir, "temp_audio.wav")
        temp_image_path = os.path.join(temp_dir, "temp_image.jpg")
        
        # Import necessary libraries
        from PIL import Image
        
        # Audio handling - direct processing from the data in memory
        if isinstance(audio_data, np.ndarray):
            # Convert numpy array to tensor
            print(f"\033[92mDEBUG\033[0m: Converting numpy audio with shape {audio_data.shape} to tensor")
            audio_tensor = torch.tensor(audio_data).float()
            
            # Handle different audio dimensions
            if audio_data.ndim == 1:
                # Single channel audio
                audio_tensor = audio_tensor.unsqueeze(0)
            elif audio_data.ndim == 2:
                # Ensure channels are first dimension
                if audio_data.shape[0] > audio_data.shape[1]:
                    # More rows than columns, probably (samples, channels)
                    audio_tensor = torch.tensor(audio_data.T).float()
        else:
            # Already a tensor
            audio_tensor = audio_data.float()
        
        print(f"\033[92mDEBUG\033[0m: Audio tensor shape before processing: {audio_tensor.shape}")
        
        # Skip saving/loading and process directly
        mfcc = app_process_audio_data(audio_tensor, sample_rate)
        print(f"\033[92mDEBUG\033[0m: MFCC tensor shape after processing: {mfcc.shape if mfcc is not None else None}")
        
        # Image handling
        if isinstance(image, np.ndarray):
            print(f"\033[92mDEBUG\033[0m: Converting numpy image with shape {image.shape} to PIL")
            pil_image = Image.fromarray(image)
            pil_image.save(temp_image_path)
            print(f"\033[92mDEBUG\033[0m: Saved image to {temp_image_path}")
        elif isinstance(image, str):
            # If image is already a path
            temp_image_path = image
            print(f"\033[92mDEBUG\033[0m: Using provided image path: {temp_image_path}")
        else:
            return f"Error: Unsupported image format. Got {type(image)}"
        
        # Process image
        print(f"\033[92mDEBUG\033[0m: Loading and preprocessing image from {temp_image_path}")
        image_tensor = torchvision.io.read_image(temp_image_path)
        print(f"\033[92mDEBUG\033[0m: Loaded image shape: {image_tensor.shape}")
        image_tensor = image_tensor.float()
        processed_image = process_image_data(image_tensor)
        print(f"\033[92mDEBUG\033[0m: Processed image shape: {processed_image.shape if processed_image is not None else None}")
        
        # Add batch dimension for inference and move to device
        if mfcc is not None:
            mfcc = mfcc.unsqueeze(0).to(device)
            print(f"\033[92mDEBUG\033[0m: Final MFCC shape with batch dimension: {mfcc.shape}")
        
        if processed_image is not None:
            processed_image = processed_image.unsqueeze(0).to(device)
            print(f"\033[92mDEBUG\033[0m: Final image shape with batch dimension: {processed_image.shape}")
        
        # Run inference
        print(f"\033[92mDEBUG\033[0m: Running inference on device: {device}")
        if mfcc is not None and processed_image is not None:
            with torch.no_grad():
                brix_value = model(mfcc, processed_image)
                print(f"\033[92mDEBUG\033[0m: Prediction successful: {brix_value.item()}")
        else:
            return "Error: Failed to process inputs. Please check the debug logs."
        
        # Format the result with a range display
        if brix_value is not None:
            brix_score = brix_value.item()
            
            # Create a header with the numerical result
            result = f"🍉 Predicted Sugar Content: {brix_score:.1f}° Brix 🍉\n\n"
            
            # Add Brix scale visualization
            result += "Sugar Content Scale (in °Brix):\n"
            result += "──────────────────────────────────\n"
            
            # Create the scale display with Brix ranges
            scale_ranges = [
                (0, 8, "Low Sugar (< 8° Brix)"),
                (8, 9, "Mild Sweetness (8-9° Brix)"),
                (9, 10, "Medium Sweetness (9-10° Brix)"),
                (10, 11, "Sweet (10-11° Brix)"),
                (11, 13, "Very Sweet (11-13° Brix)")
            ]
            
            # Find which category the prediction falls into
            user_category = None
            for min_val, max_val, category_name in scale_ranges:
                if min_val <= brix_score < max_val:
                    user_category = category_name
                    break
            if brix_score >= scale_ranges[-1][0]:  # Handle edge case
                user_category = scale_ranges[-1][2]
            
            # Display the scale with the user's result highlighted
            for min_val, max_val, category_name in scale_ranges:
                if category_name == user_category:
                    result += f"▶ {min_val}-{max_val}: {category_name} ◀ (YOUR WATERMELON)\n"
                else:
                    result += f"  {min_val}-{max_val}: {category_name}\n"
            
            result += "──────────────────────────────────\n\n"
            
            # Add assessment of the watermelon's sugar content
            if brix_score < 8:
                result += "Assessment: This watermelon has low sugar content. It may taste bland or slightly bitter."
            elif brix_score < 9:
                result += "Assessment: This watermelon has mild sweetness. Acceptable flavor but not very sweet."
            elif brix_score < 10:
                result += "Assessment: This watermelon has moderate sugar content. It should have pleasant sweetness."
            elif brix_score < 11:
                result += "Assessment: This watermelon has good sugar content! It should be sweet and juicy."
            else:
                result += "Assessment: This watermelon has excellent sugar content! Perfect choice for maximum sweetness and flavor."
                
            return result
        else:
            return "Error: Could not predict sugar content. Please try again with different inputs."
    
    except Exception as e:
        import traceback
        error_msg = f"Error: {str(e)}\n\n"
        error_msg += traceback.format_exc()
        print(f"\033[91mERR!\033[0m: {error_msg}")
        return error_msg
    
    print("\033[92mINFO\033[0m: GPU-accelerated prediction function created with @spaces.GPU decorator")


def create_app(model_path):
    """Create and launch the Gradio interface"""
    # Define the prediction function with model path
    def predict_fn(audio, image):
        return predict_sugar_content(audio, image, model_path)
    
    # Create Gradio interface
    with gr.Blocks(title="Watermelon Sugar Content Predictor", theme=gr.themes.Soft()) as interface:
        gr.Markdown("# 🍉 Watermelon Sugar Content Predictor")
        gr.Markdown("""
        This app predicts the sugar content (in °Brix) of a watermelon based on its sound and appearance.
        
        ## Instructions:
        1. Upload or record an audio of tapping the watermelon
        2. Upload or capture an image of the watermelon
        3. Click 'Predict' to get the sugar content estimation
        """)
        
        with gr.Row():
            with gr.Column():
                audio_input = gr.Audio(label="Upload or Record Audio", type="numpy")
                image_input = gr.Image(label="Upload or Capture Image")
                submit_btn = gr.Button("Predict Sugar Content", variant="primary")
            
            with gr.Column():
                output = gr.Textbox(label="Prediction Results", lines=12)
                
        submit_btn.click(
            fn=predict_fn,
            inputs=[audio_input, image_input],
            outputs=output
        )
        
        gr.Markdown("""
        ## Tips for best results
        - For audio: Tap the watermelon with your knuckle and record the sound
        - For image: Take a clear photo of the whole watermelon in good lighting
        
        ## About Brix Measurement
        Brix (°Bx) is a measurement of sugar content in a solution. For watermelons, higher Brix values indicate sweeter fruit.
        The average ripe watermelon has a Brix value between 9-11°.
        """)
    
    return interface

if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description="Watermelon Sugar Content Prediction App")
    parser.add_argument(
        "--model_path", 
        type=str, 
        default="models/watermelon_model_final.pt", 
        help="Path to the trained model file"
    )
    parser.add_argument(
        "--share", 
        action="store_true", 
        help="Create a shareable link for the app"
    )
    parser.add_argument(
        "--debug", 
        action="store_true", 
        help="Enable verbose debug output"
    )
    
    args = parser.parse_args()
    
    if args.debug:
        print(f"\033[92mINFO\033[0m: Debug mode enabled")
    
    # Check if model exists
    if not os.path.exists(args.model_path):
        print(f"\033[91mERR!\033[0m: Model not found at {args.model_path}")
        print("\033[92mINFO\033[0m: Please train a model first or provide a valid model path")
        sys.exit(1)
    
    # Create and launch the app
    app = create_app(args.model_path)
    app.launch(share=args.share)