Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,62 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# ====================== APP CONFIG ======================
+st.set_page_config(
+    page_title="AI Text Detector",
+    page_icon="🤖",
+    layout="centered"
+)
+
+st.title("🧠 AI Text Detector (DeBERTa-v3-large)")
+st.markdown("""
+This tool detects whether the given text is **Human-written** or **AI-generated**  
+using a fine-tuned `microsoft/deberta-v3-large` model.
+""")
+
+# ====================== LOAD MODEL ======================
+@st.cache_resource
+def load_model():
+    model_name = "your-username/ai-text-detector-deberta-v3-large"  # Replace with your HF model repo
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# ====================== TEXT INPUT ======================
+user_text = st.text_area(
+    "Enter text to analyze:",
+    placeholder="Paste or write any text here...",
+    height=200
+)
+
+if st.button("🔍 Analyze Text", type="primary"):
+    if not user_text.strip():
+        st.warning("⚠️ Please enter some text.")
+    else:
+        with st.spinner("Analyzing..."):
+            inputs = tokenizer(user_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+            with torch.no_grad():
+                outputs = model(**inputs)
+                probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0]
+                confidence, prediction = torch.max(probs, dim=0)
+
+            label = "🤖 AI-generated" if prediction.item() == 1 else "🧍 Human-written"
+            confidence_percent = confidence.item() * 100
+
+        st.success(f"**Prediction:** {label}")
+        st.progress(confidence.item())
+        st.write(f"**Confidence:** {confidence_percent:.2f}%")
+
+        # Detailed Probabilities
+        st.markdown("### 📊 Detailed Probabilities")
+        st.write({
+            "Human (0)": f"{probs[0].item() * 100:.2f}%",
+            "AI (1)": f"{probs[1].item() * 100:.2f}%"
+        })
+
+# ====================== FOOTER ======================
+st.markdown("---")
+st.caption("Built with ❤️ using [Streamlit](https://streamlit.io) and [Hugging Face Transformers](https://huggingface.co/transformers).")