app.py

import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
import gradio as gr

# 📦 Class names
class_names = [
    "accordion",
    "banjo",
    "drum",
    "flute",
    "guitar",
    "harmonica",
    "saxophone",
    "sitar",
    "tabla",
    "violin"
]

# 📐 Transformations (same as during training)
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                         [0.229, 0.224, 0.225])
])

# 🧠 Load model with enhanced FC head
def load_model(model_path="music_model.pth"):
    model = models.resnet18(weights=None)

    # ✨ Enhanced classifier head (512 → 256 → 10)
    model.fc = nn.Sequential(
        nn.Linear(model.fc.in_features, 256),
        nn.ReLU(),
        nn.Dropout(0.4),
        nn.Linear(256, 10)
    )

    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
    model.eval()
    return model

model = load_model("music_model.pth")

# 🔍 Prediction function
def predict(image):
    image = Image.fromarray(image).convert("RGB")
    img_tensor = transform(image).unsqueeze(0)
    with torch.no_grad():
        outputs = model(img_tensor)
        _, predicted = torch.max(outputs, 1)
        prediction = class_names[predicted.item()]
        confidences = torch.nn.functional.softmax(outputs[0], dim=0)
        confidences_dict = {class_names[i]: float(confidences[i]) for i in range(10)}
    return prediction, confidences_dict

# 🎛️ Gradio Interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="numpy", label="Upload Instrument Image"),
    outputs=[
        gr.Label(label="Predicted Instrument"),
        gr.Label(label="Confidence Scores")
    ],
    title="🎵 Musical Instrument Classifier",
    description="Upload an image of a musical instrument and get the predicted class (accordion, guitar, etc.)"
)

# 🚀 Launch the app
if __name__ == "__main__":
    interface.launch()