app.py

import torch
import fastapi
import numpy as np
from PIL import Image

class TorchTensor(torch.Tensor):
    pass

class Prediction:
    prediction: TorchTensor

app = fastapi.FastAPI(docs_url="/")
from transformers import ViTForImageClassification

# Define the number of classes in your custom dataset
num_classes = 20

# Initialize the ViTForImageClassification model
model = ViTForImageClassification.from_pretrained(
    'google/vit-base-patch16-224-in21k',
    num_labels=num_classes  # Specify the number of classes
)

# Load your fine-tuned model weights
model.load_state_dict(torch.load('best_model.pth', map_location='cpu'))

# Define class names for your dataset
class_names = [
    "Acral Lick Dermatitis",
    "Acute moist dermatitis",
    "Canine atopic dermatitis",
    "Cherry Eye",
    "Ear infections",
    "External Parasites",
    "Folliculitis",
    "Healthy",
    "Leishmaniasis",
    "Lupus",
    "Nuclear sclerosis",
    "Otitis externa",
    "Pruritus",
    "Pyoderma",
    "Rabies",
    "Ringworm",
    "Sarcoptic Mange",
    "Sebaceous adenitis",
    "Seborrhea",
    "Skin tumor"
]

# Define a function to preprocess the input image
def preprocess_input(input: fastapi.UploadFile):
    image = Image.open(input.file)
    image = image.resize((224, 224)).convert("RGB")
    input_data = np.array(image)
    input_data = np.transpose(input_data, (2, 0, 1))
    input_data = torch.from_numpy(input_data).float()
    input_data = input_data.unsqueeze(0)
    return input_data

@app.post("/predict")
async def predict_endpoint(input: fastapi.UploadFile):
    """Make a prediction on an image uploaded by the user."""

    # Preprocess the input image
    input_data = preprocess_input(input)

    # Make a prediction
    prediction = model(input_data)
    logits = prediction.logits

    # Get the top N predictions
    predicted_class = torch.argmax(logits, dim=1).item()

    # Create a response dictionary
    return {"prediction": predicted_class}