app.py

import torch
from transformers import AutoModelForSequenceClassification

# Update this variable with your model name from Hugging Face Hub
MODEL_NAME = "model.pt"

# Load the model (no tokenizer needed)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

# Function to make predictions (replace with your actual prediction logic)
def predict(text):
  """
  This function takes a text input, preprocesses it using the tokenizer,
  makes a prediction using the loaded model, and returns the predicted output.
  **Replace this function with your actual prediction logic.**
  """
  # Modify this part to handle input processing if your model requires it
  # (assuming your model doesn't need a tokenizer in this example)
  inputs = text  # Replace with preprocessing steps if necessary

  with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)
  return predictions.item()

# Function to handle user input and make predictions (modify for your UI framework)
def handle_request(data):
  """
  This function takes user input data (modify based on your UI framework),
  extracts the relevant text, and calls the predict function to make a prediction.
  """
  text = data["text"]  # Assuming "text" is the key in your data dictionary
  prediction = predict(text)
  return {"prediction": prediction}

if __name__ == "__main__":
  from fastapi import FastAPI

  app = FastAPI()

  @app.post("/predict")
  async def predict_from_text(data: dict):
    response = handle_request(data)
    return response