Going back to streamlit

app.py

@@ -8,49 +8,50 @@ HuggingFace Spaces that:
 # https://huggingface.co/blog/streamlit-spaces
 # https://huggingface.co/docs/hub/en/spaces-sdks-streamlit
 
-# https://www.gradio.app/docs/interface
-# https://huggingface.co/spaces/docs-demos/roberta-base/blob/main/app.py
-
 """
-import gradio as gr
+
+import streamlit as st
+from transformers import pipeline
 from string import punctuation
+import pandas as pd
+# from huggingface_hub import InferenceClient
+# client = InferenceClient(model="bdsl/HanmunRoBERTa")
+
+# Load the pipeline with the HanmunRoBERTa model
+model_pipeline = pipeline(task="text-classification", model="bdsl/HanmunRoBERTa")
 
+# Streamlit app layout
 title = "HanmunRoBERTa Century Classifier"
-description = "Century classifier for classical Chinese and Korean texts"
+st.title(title)
+st.set_page_config(layout=layout, page_title=title, page_icon="📚")
 
-# Load the HanmunRoBERTa model
-hanmun_roberta = gr.load("huggingface/bdsl/HanmunRoBERTa")
+# Checkbox to remove punctuation
+remove_punct = st.checkbox(label="Remove punctuation", value=True)
 
-def strip_text(inputtext):
+# Text area for user input
+input_str = st.text_area("Input text", height=275)
+
+# Remove punctuation if checkbox is selected
+if remove_punct and input_str:
+    # Specify the characters to remove
     characters_to_remove = "○□()〔〕:\"。·, ?ㆍ" + punctuation
     translating = str.maketrans('', '', characters_to_remove)
-    return inputtext.translate(translating)
-
-def inference(inputtext, model, strip_text_flag):
-    if strip_text_flag:
-        inputtext = strip_text(inputtext)
-    if model == "HanmunRoBERTa":
-        outlabel = hanmun_roberta(inputtext)
-    return outlabel
-
-# Define some example inputs for your interface
-examples = [["Example text 1", "HanmunRoBERTa", True],
-            ["Example text 2", "HanmunRoBERTa", True]]
-
-# Set up the Gradio interface
-gr.Interface(
-    inference, 
-    [gr.inputs.Textbox(label="Input text", lines=10),
-     gr.inputs.Dropdown(choices=["HanmunRoBERTa"], 
-                        type="value", 
-                        default="HanmunRoBERTa", 
-                        label="Model"),
-     gr.inputs.Checkbox(label="Remove punctuation")], 
-    [gr.outputs.Label(label="Output")],
-    examples=examples,
-    title=title,
-    description=description).launch(enable_queue=True)
-
-
-
-
+    input_str = input_str.translate(translating)
+
+# Display the input text after processing
+st.write("Processed input:", input_str)
+
+# Predict and display the classification scores if input is provided
+if st.button("Classify"):
+    if input_str:
+        predictions = model_pipeline(input_str)
+        
+        # Prepare the data for plotting
+        labels = [prediction['label'] for prediction in predictions]
+        scores = [prediction['score'] for prediction in predictions]
+        data = pd.DataFrame({"Label": labels, "Score": scores})
+        
+        # Displaying predictions as a bar chart
+        st.bar_chart(data.set_index('Label'))
+    else:
+        st.write("Please enter some text to classify.")

requirements.txt

@@ -1 +1,4 @@
-gradio==3.50
+streamlit
+torch
+transformers
+pandas