Upload app.py

app.py

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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()
+