m

app.py

@@ -1,36 +1,7 @@
 from fastapi import FastAPI
-from pydantic import BaseModel
-from typing import List
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 app = FastAPI()
 
-# Load model and tokenizer once at startup
-checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
-
-class TextData(BaseModel):
-    texts: List[str]
-
 @app.get("/")
-def home():
-    return {"message": "Welcome to the sentiment analysis API!"}
-
-@app.post("/predict")
-def predict(data: TextData):
-    # Tokenize the input texts
-    tokens = tokenizer(data.texts, padding=True, truncation=True, return_tensors="pt")
-    
-    # Perform inference without gradient tracking
-    with torch.no_grad():
-        outputs = model(**tokens)
-    
-    # Calculate softmax probabilities and determine the predictions
-    predictions = torch.argmax(outputs.logits, dim=-1).tolist()
-    
-    # Prepare the response with texts and their corresponding predictions
-    results = [{"text": text, "prediction": prediction} for text, prediction in zip(data.texts, predictions)]
-    
-    return {"results": results}
+def greet_json():
+    return {"Hello": "World!"}

requirements.txt

@@ -1,6 +1,2 @@
 fastapi
 uvicorn[standard]
-pydantic
-typing
-torch
-transformers