Upload app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import pandas as pd
+
+with gr.Blocks() as demo:
+    dataset_df = {}
+    state = gr.State(value=0)
+    with gr.Row():
+        gr.Markdown("# Distributed Evaluation Parallel 😎")
+    with gr.Row():
+        upload = gr.UploadButton(label="Upload a file")
+        prev = gr.Button(value="Previous")
+        next = gr.Button(value="Next")
+        download = gr.Button(value="Download")
+    with gr.Row():
+        with gr.Column():
+            question = gr.Textbox(label="Question")
+        with gr.Column():
+            ground_truth = gr.Textbox(label="GT")
+        with gr.Column():
+            prediction = gr.Textbox(label="Prediction")
+            score = gr.Radio(["Incorrect", "Correct"], label="Score")
+    with gr.Row():
+        todos = gr.DataFrame()
+        done = gr.DataFrame()
+    
+
+    def csv2df(file):
+        df = pd.read_csv(file.name)
+        dataset_df.update(dict(df=df))
+        return update()
+
+    def prev_func():
+        state.value = max(state.value - 1, 0)
+        return update()
+    
+    def next_func():
+        state.value = min(state.value + 1, len(dataset_df['df']) - 1)
+        return update()
+
+    def update():
+        q = dataset_df['df'].question.to_list()[state.value]
+        g = dataset_df['df'].answer.to_list()[state.value]
+        p = dataset_df['df'].prediction.to_list()[state.value]
+        return q, g, p, dataset_df['df'], dataset_df['df']
+    upload.upload(csv2df, upload, [question, ground_truth, prediction, todos, done])
+    prev.click(prev_func, None, [question, ground_truth, prediction, todos, done])
+    next.click(next_func, None, [question, ground_truth, prediction, todos, done])
+
+demo.queue()
+demo.launch(share=True)