Initial commit for Gradio app

app.py

@@ -1,14 +1,63 @@
 import gradio as gr
 
+# VARIABLES: will eventually be loaded with JSON from a dataset 
 
+question_text = """
+    ### Bar Question
+    What is the answer to this question?"""
+answers_text = ["A", "B", "C", "D"]
 
-def greet(name):
-    return "Hello " + name + "!!"
+# BLOCKS: main user interface
 
-demo = gr.Interface(fn = greet, 
-                    inputs = "text", 
-                    outputs = "text",
-                    title = "Legal Retrieval Study",
-                    description = "Based on the questions, how helpful are these responses?")
+with gr.Blocks() as user_eval:
+    # Title text introducing study
+    gr.Markdown("""
+    # Legal Retriever Evaluation Study
+    Thank you for your participation! Here are some basic instructions on how to complete the legal study.
+    """)
 
-demo.launch()
+    # Passages and user evaluations thereof
+    with gr.Row(equal_height = False, visible = False) as evals:
+        with gr.Column(scale = 2) as passages:
+            passage_display = gr.Markdown("""
+            ### Relevant Passages
+                - Dataset 1
+                - Dataset 2
+                - More text
+                - More text
+                - More text
+                - More text
+            ### Auto-Generated Summary
+            This is a summary of the above legal passages, which imitates how a RAG system might \
+            encorporate retrieved data into its context to give a better response to a certain query.
+            """)
+        with gr.Column(scale = 1) as scores:
+            desc_1 = gr.Markdown("How **relevant** are these passages to our query?")
+            eval_1 = gr.Slider(1, 5)
+            desc_2 = gr.Markdown("How **novel** are these passages compared to the previous passages?")
+            eval_2 = gr.Slider(1, 5)
+            btn = gr.Button("Next")
+    # btn.click(fn=update, inputs=inp, outputs=eval_1)
+
+    # Question and answering dynamics
+    with gr.Row() as question:
+        with gr.Column():
+            gr.Markdown(question_text)
+            a = gr.Button(answers_text[0])
+            b = gr.Button(answers_text[1])
+            c = gr.Button(answers_text[2])
+            d = gr.Button(answers_text[3])
+
+            def answer():
+                return {
+                    question: gr.Row(visible = False),
+                    evals: gr.Row(visible = True)
+                }
+            
+            a.click(fn = answer, outputs = [question, evals])
+            b.click(fn = answer, outputs = [question, evals])
+            c.click(fn = answer, outputs = [question, evals])
+            d.click(fn = answer, outputs = [question, evals])
+
+# Starts on question, switches to evaluation after the user answers
+user_eval.launch()