test argilla

app.py

@@ -0,0 +1,51 @@
+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

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