Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# This is the key line:
+# It loads your private model from your *other* repository.
+pipe = pipeline(
+    "text-classification",
+    model="kxshrx/infrnce-bert-classifier"
+)
+
+def classify_log(log_text):
+    # This function runs the classification.
+    results = pipe(log_text, top_k=None)
+    # We format the result into a simple dictionary.
+    return {item['label']: item['score'] for item in results[0]}
+
+# This creates a simple web UI for testing and, more importantly,
+# an API endpoint that we can call.
+gr.Interface(
+    fn=classify_log,
+    inputs=gr.Textbox(lines=5, label="Log Entry"),
+    outputs=gr.Label(num_top_classes=6),
+    title="Infrnce Private Log Classifier API"
+).launch()