confidence.py

import os
os.environ["HF_HOME"] = "/tmp"
os.environ["TRANSFORMERS_CACHE"] = "/tmp"

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Initialize FastAPI app
app = FastAPI(title="Confidence Statement API", version="1.0")

# Load the fine-tuned model and tokenizer
model_name = "mjpsm/Confidence-Statement-Model-final"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Define input format
class InputText(BaseModel):
    statement: str

# Define prediction function
def predict_statement(statement: str):
    inputs = tokenizer(statement, return_tensors="pt", padding=True, truncation=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    probabilities = torch.nn.functional.softmax(logits, dim=-1)
    predicted_class = torch.argmax(probabilities, dim=-1).item()
    label_mapping = {0: "lack of self-confidence", 1: "self-confident"}
    return {
        "label": label_mapping[predicted_class],
        "confidence_score": round(probabilities[0][predicted_class].item(), 4)
    }

# Define root route
@app.get("/")
def read_root():
    return {"message": "Welcome to the Confidence Statement API!"}

# Define prediction route
@app.post("/predict")
def predict(input_text: InputText):
    return predict_statement(input_text.statement)