Create app.py

app.py

@@ -0,0 +1,44 @@
+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