Delete app.py

app.py

@@ -1,61 +0,0 @@
-'''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()
-