Delete app.py

app.py

@@ -1,77 +0,0 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-import torch
-from transformers import BertTokenizer
-from models.bert_model import BertMultiOutputModel
-from config import TEXT_COLUMN, LABEL_COLUMNS, MAX_LEN, DEVICE
-from dataset_utils import load_label_encoders
-import numpy as np
-import os
-
-app = FastAPI()
-
-# Load the model and tokenizer
-model_path = "BERT_model.pth"
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-model = BertMultiOutputModel([len(load_label_encoders()[col].classes_) for col in LABEL_COLUMNS]).to(DEVICE)
-model.load_state_dict(torch.load(model_path, map_location=DEVICE))
-model.eval()
-
-class PredictionRequest(BaseModel):
-    sanction_context: str
-
-@app.get("/")
-async def root():
-    return {"status": "healthy", "message": "BERT API is running"}
-
-@app.get("/health")
-async def health_check():
-    return {"status": "healthy"}
-
-@app.post("/predict")
-async def predict(request: PredictionRequest):
-    try:
-        # Tokenize the input text
-        inputs = tokenizer(
-            request.sanction_context,
-            padding='max_length',
-            truncation=True,
-            max_length=MAX_LEN,
-            return_tensors="pt"
-        )
-        
-        # Move inputs to device
-        input_ids = inputs['input_ids'].to(DEVICE)
-        attention_mask = inputs['attention_mask'].to(DEVICE)
-
-        # Get predictions
-        with torch.no_grad():
-            outputs = model(input_ids=input_ids, attention_mask=attention_mask)
-            probabilities = [torch.softmax(output, dim=1).cpu().numpy() for output in outputs]
-            predictions = [np.argmax(prob, axis=1) for prob in probabilities]
-
-        # Load label encoders to decode predictions
-        label_encoders = load_label_encoders()
-        
-        # Format response
-        response = {}
-        for i, (col, pred, prob) in enumerate(zip(LABEL_COLUMNS, predictions, probabilities)):
-            decoded_pred = label_encoders[col].inverse_transform(pred)[0]
-            response[col] = {
-                "prediction": decoded_pred,
-                "probabilities": {
-                    label: float(prob[0][j]) 
-                    for j, label in enumerate(label_encoders[col].classes_)
-                }
-            }
-
-        return response
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-if __name__ == "__main__":
-    import uvicorn
-    # For Hugging Face Spaces, we need to use port 7860
-    port = int(os.environ.get("PORT", 7860))
-    uvicorn.run(app, host="0.0.0.0", port=port)