j

app.py

@@ -21,7 +21,6 @@ def extract_json(gen_text, n_shot_learning=0):
     return gen_text[start_index:end_index]
 
 def score_model(model_uri, databricks_token, prompt):
-  prompt = "Korea is a"
   ds_dict={
             "model": "debisoft/mpt-7b-awq-tester",
             "prompt": prompt,
@@ -128,5 +127,5 @@ Give a score for the product. Format your response as a JSON object with \
 #iface.launch()
 
 #iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Text to find entities", lines=2)], outputs=[gr.HighlightedText(label="Text with entities")], title="NER with dslim/bert-base-NER", description="Find entities using the `dslim/bert-base-NER` model under the hood!", allow_flagging="never", examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
-iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Elevator pitch", lines=3)], outputs="text")
+iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Elevator pitch", lines=3)], outputs="json")
 iface.launch()