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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 |