update inputs keyword and type hints

app.py

@@ -3,6 +3,7 @@ from fixed_precision import FixedPrecision
 from fixed_recall import FixedRecall
 import evaluate
 import gradio as gr
+import pandas as pd
 
 title = "'Combine' multiple metrics with this 🤗 Evaluate 🪲 Fix!"
 
@@ -22,27 +23,27 @@ Try to use \t to write some code? \t or how does that work? </p>
 article = "<p style='text-align: center'> Check out the [original repo](https://github.com/johngrahamreynolds/FixedMetricsForHF) housing this code, and a quickly \
 trained [multilabel text classification model](https://github.com/johngrahamreynolds/RoBERTa-base-DReiFT/tree/main) that makes use of it during evaluation.</p>"
 
-def show_off(predictions=[0,1,2], references=[0,1,2], weighting_map={"f1":"weighted", "precision": "micro", "recall": "weighted"}): 
+def show_off(predictions: list[list]) -> str: 
 
-    f1 = FixedF1(average=weighting_map["f1"])
-    precision = FixedPrecision(average=weighting_map["precision"])
-    recall = FixedRecall(average=weighting_map["recall"])
+    # f1 = FixedF1(average=weighting_map["f1"])
+    # precision = FixedPrecision(average=weighting_map["precision"])
+    # recall = FixedRecall(average=weighting_map["recall"])
 
-    combined = evaluate.combine([f1, recall, precision])
-
-    combined.add_batch(prediction=predictions, reference=references)
-    outputs =  combined.compute()
+    # combined = evaluate.combine([f1, recall, precision])
 
+    # combined.add_batch(prediction=predictions, reference=references)
+    # outputs =  combined.compute()
+    outputs = predictions
 
     return "Your metrics are as follows: \n" + outputs
 
 
 gr.Interface(
     fn=show_off,
-    inputs="textbox",
+    inputs="dataset",
     outputs="text",
     title=title,
     description=description,
     article=article,
-    examples=[[[1, 0, 2, 0, 1], [1,0,0,0,1], {"f1":"weighted", "precision": "micro", "recall": "weighted"}]],
+    examples=[[1, 0, 2, 0, 1]],
 ).launch()