test barplot

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter"
+    },
+    "python.formatting.provider": "none"
+}

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import pandas as pd
 import torch
 from transformers import AutoTokenizer, AutoModelForMaskedLM
 
@@ -9,12 +10,13 @@ tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
 model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)
 mask_token = tokenizer.mask_token
 
+
 def add_mask(target_word, text):
-     text_mask = text.replace(target_word, mask_token)
-     return text_mask
+    text_mask = text.replace(target_word, mask_token)
+    return text_mask
 
-def eval_prob(target_word, text):
 
+def eval_prob(target_word, text):
     text_mask = add_mask(target_word, text)
     # Get index of target_word
     idx = tokenizer.encode(target_word)[2]
@@ -22,11 +24,11 @@ def eval_prob(target_word, text):
     # Get logits
     inputs = tokenizer(text_mask, return_tensors="pt")
     token_logits = model(**inputs).logits
-    
+
     # Find the location of the MASK and extract its logits
     mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
     mask_token_logits = token_logits[0, mask_token_index, :]
-    
+
     # Convert logits to softmax probability
     logits = mask_token_logits[0].tolist()
     probs = torch.nn.functional.softmax(torch.tensor([logits]), dim=1)[0]
@@ -36,18 +38,34 @@ def eval_prob(target_word, text):
 
     return result
 
+
+# test barplot
+simple = pd.DataFrame(
+    {
+        "item": ["A", "B", "C", "D", "E", "F", "G", "H", "I"],
+        "inventory": [28, 55, 43, 91, 81, 53, 19, 87, 52],
+    }
+)
+
+css = (
+    "footer {display: none !important;} .gradio-container {min-height: 0px !important;}"
+)
+
+with gr.Blocks(css=css) as demo:
+    gr.BarPlot(value=simple, x="item", y="inventory", title="Simple Bar Plot").style(
+        container=False,
+    )
+demo.launch(share=True)
+
+
 gr.Interface(
     fn=eval_prob,
     inputs=[
-        gr.Textbox(
-            label="词语",
-            placeholder="夸大"),
-        gr.Textbox(
-            label="造句",
-            placeholder=f"我们使用生成式人工智能已经很长时间了，所以他们最近的媒体报道可能被夸大了。"),
+        gr.Textbox(label="词语", placeholder="夸大"),
+        gr.Textbox(label="造句", placeholder=f"我们使用生成式人工智能已经很长时间了，所以最近的媒体报道可能被夸大了。"),
     ],
     examples=[
-        ["夸大", "我们使用生成式人工智能已经很长时间了，所以他们最近的媒体报道可能被夸大了。"],
+        ["夸大", "我们使用生成式人工智能已经很长时间了，所以最近的媒体报道可能被夸大了。"],
     ],
     outputs="number",
     title="Chinese Sentence Grading",

requirements.txt

@@ -1,3 +1,4 @@
 --extra-index-url https://download.pytorch.org/whl/cu113
 torch
-transformers
+transformers
+pandas