create app.py

app.py

@@ -0,0 +1,46 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load the pre-trained model for inference
+model_name = "devaprobs/amharic-hate-speech-detection"
+classifier = pipeline("text-classification", model=model_name)
+
+# Configure the Streamlit page
+st.set_page_config(page_title="Amharic Hate Speech Detector", page_icon="🕵️‍♂️", layout="centered")
+st.markdown("<style> body { background-color: #f0f2f6; } </style>", unsafe_allow_html=True)
+
+# Add a stylish header with a logo
+st.markdown(
+    """
+    <div style="text-align:center">
+        <h1 style="color:#1F618D;">Amharic Hate Speech Detector 🕵️‍♂️</h1>
+        <p style="font-size:20px; color:#555;">Type an Amharic sentence and let our model analyze it!</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# Input text box for user to enter Amharic text
+user_input = st.text_area("Enter Amharic text here:", height=150, placeholder="ምሳሌ: እናንተ እንደማንም አይሆኑም...")
+
+# Submit button for classification
+if st.button("Analyze Text 🚀"):
+    if user_input:
+        # Get the classification result
+        result = classifier(user_input)
+        label = result[0]['label']
+        score = result[0]['score']
+
+        # Map label to understandable output
+        if label == "LABEL_0":
+            prediction = "Normal Text 🟢"
+            color = "#28a745"
+        else:
+            prediction = "Hate Speech 🔴"
+            color = "#dc3545"
+
+        # Display the result with styled message
+        st.markdown(f"<h2 style='text-align: center; color: {color};'>{prediction}</h2>", unsafe_allow_html=True)
+        st.write(f"Confidence: {score * 100:.2f}%")
+    else:
+        st.warning("Please enter some text to analyze.")