update

app.py

@@ -1,51 +0,0 @@
-import gradio as gr
-import argilla as rg
-from datasets import load_dataset
-
-# Connect to Argilla (adjust URL and API key as needed)
-rg.init(
-    api_url="https://jhsu12/MedLLM-DataAnnotation-argilla-space.hf.space",
-    #api_key="your-api-key",
-    #extra_headers={"Authorization": f"Bearer YOUR_HF_TOKEN"}
-)
-
-# banking_ds = load_dataset("argilla/banking_sentiment_setfit", split="train")
-
-# # Argilla expects labels in the annotation column
-# banking_ds = banking_ds.rename_column("label", "annotation")
-
-# # Build argilla dataset from datasets
-# argilla_ds = rg.read_datasets(banking_ds, task="TextClassification")
-
-# rg.log(argilla_ds, "banking_sentiment")
-
-def evaluate_response(prompt, response, quality, feedback):
-    record = rg.FeedbackRecord(
-        fields={"prompt": prompt, "llm_response": response},
-        responses=[{
-            "values": {
-                "quality": quality,
-                "feedback": feedback
-            }
-        }]
-    )
-    dataset = rg.FeedbackDataset.from_argilla("llm-eval")
-    dataset.add_records(record)
-    return "Evaluation submitted!"
-
-with gr.Blocks() as demo:
-    gr.Markdown("## LLM Response Evaluation")
-    with gr.Row():
-        prompt = gr.Textbox(label="Original Prompt")
-        response = gr.Textbox(label="LLM Response")
-    quality = gr.Radio([1,2,3,4,5], label="Quality Rating")
-    feedback = gr.Textbox(label="Additional Feedback")
-    submit = gr.Button("Submit")
-    output = gr.Textbox()
-    submit.click(
-        fn=evaluate_response,
-        inputs=[prompt, response, quality, feedback],
-        outputs=output
-    )
-
-demo.launch()

requirements.txt

@@ -1,3 +0,0 @@
-gradio>=4.0.0
-argilla
-huggingface_hub