Update app.py

app.py

@@ -1,3 +1,38 @@
-import gradio as gr
+import streamlit as st
+from transformers import pipeline
 
-gr.load("models/dejanseo/CTR-ZD").launch()
+# Load the model from Hugging Face
+model_name = "dejanseo/CTR-ZD"
+model_pipeline = pipeline("text-classification", model=model_name)
+
+# Mapping for the labels
+labels_mapping = {
+    "LABEL_0": "Neutral impact",
+    "LABEL_1": "Positive impact",
+    "LABEL_2": "Negative impact"
+}
+
+def predict_title_impact(query, title):
+    # Concatenate query and title
+    input_text = f"{query} {title}"
+    predictions = model_pipeline(input_text)
+    return predictions
+
+# Streamlit app
+st.title("HTML Title CTR Prediction")
+st.write("Predict the likelihood of a HTML title having an impact on CTR (click-through rate).")
+
+# Input for the query and HTML title
+query = st.text_input("Enter the Query:")
+title = st.text_input("Enter the HTML Title:")
+
+# Predict button
+if st.button("Predict"):
+    if query and title:
+        predictions = predict_title_impact(query, title)
+        for prediction in predictions:
+            label = labels_mapping.get(prediction['label'], "Unknown")
+            st.write(f"Prediction: {label}")
+            st.write(f"Confidence: {prediction['score']:.2f}")
+    else:
+        st.write("Please enter both a query and an HTML title.")