# 1. Import the required packages
import torch
import gradio as gr

from typing import Dict
from transformers import pipeline

# 2. Define function to use our model on given text
def spam_not_spam_classifier(text: str) -> Dict[str, float]:
  """
  Takes an input string of text and classifies it into spam/not_spam in the form of a dictionary.
  """

  # 2. Setup the pipeline to use the local model (or Hugging Face model path) 
  spam_not_spam_classifier = pipeline(task="text-classification",
                                      model="drvpokhilko/huggingface_spam_not_spam_classifier-distilbert-base-uncased",
                                      batch_size=32,
                                      device="cuda" if torch.cuda.is_available() else "cpu", # set the device to work in any environment
                                      top_k=None) # return all possible scores (not just top-1)

  # 3. Get outputs from pipeline (as a list of dicts)
  outputs = spam_not_spam_classifier(text)[0]

  # 4. Format output for Gradio (e.g., {"label_1": probability_1, "label_2": probability_2})
  output_dict = {}

  for item in outputs:
    output_dict[item["label"]] = item["score"]

  return output_dict

# 3. Create a Gradio interface with details about our app
description = """
A text classifier to determine if an email text is spam or not spam. 

Fine-tuned from [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on a relatively small [dataset (~11k samples) of spam or not spam emails](https://huggingface.co/datasets/Deysi/spam-detection-dataset).
"""

demo = gr.Interface(fn=spam_not_spam_classifier,
                    inputs="text",
                    outputs=gr.Label(num_top_classes=2),
                    title="📧⌨️👩‍💻Spam or Not Spam Email Classifier",
                    description=description,
                    examples=[["Hi John, here's the project report you requested. Let me know if you need any changes."],
                              ["Get access to unlimited movies and TV shows for free. Sign up today!"]])

# 4. Launch the interface
if __name__ == "__main__":
  demo.launch()