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import gradio as gr | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
import torch | |
tokenizer = AutoTokenizer.from_pretrained("akhooli/mistral-7B-llm") | |
model = AutoModelForSequenceClassification.from_pretrained("akhooli/mistral-7B-llm") | |
def predict(image_path): | |
# Load and preprocess the image | |
with open(image_path, "rb") as f: | |
image_bytes = f.read() | |
# Tokenize and predict | |
inputs = tokenizer(image_bytes, return_tensors="pt", padding=True, truncation=True) | |
outputs = model(**inputs) | |
predicted_class_idx = torch.argmax(outputs.logits) | |
# In this example, we are assuming the labels are ['pizza', 'burger', 'sandwich'] | |
labels = ['pizza', 'burger', 'sandwich'] | |
predicted_label = labels[predicted_class_idx] | |
return predicted_label | |
gr.Interface( | |
predict, | |
inputs=gr.Image(label="Upload junk food (sandwich, pizza, burger) candidate", type="file"), | |
outputs=gr.Label(num_top_classes=3), | |
title="Pizza, Burger, or Sandwich?", | |
).launch() | |