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'''import requests
import streamlit as st
# Replace with the actual URL of your deployed FastAPI backend
API_URL = "http://127.0.0.1:8000/predict"
def main():
text_input = st.text_input("Enter text to score:")
if st.button("Score Text"):
response = requests.post(API_URL, json={"text": text_input})
data = response.json()
st.write(f"Score: {data['score']}")
st.write(f"Message: {data['message']}")
if __name__ == "__main__":
main()'''
import streamlit as st
import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification
# Load the tokenizer
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
# Load the model
model_path = "model_ai_detection"
model = RobertaForSequenceClassification.from_pretrained(model_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
def predict(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
ai_prob = probs[0][1].item() * 100 # Probability of the text being AI-generated
message = "The text is likely generated by AI." if ai_prob > 50 else "The text is likely generated by a human."
return {
"score": ai_prob,
"message": message
}
def main():
st.title("AI Text Detector")
text_input = st.text_area("Enter text to score:")
if st.button("Score Text"):
if text_input:
result = predict(text_input)
st.write(f"Score: {result['score']:.2f}%")
st.write(f"Message: {result['message']}")
else:
st.write("Please enter some text to score.")
if __name__ == "__main__":
main()
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