init

.gitignore

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+__pycache__

app.py

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+import spaces
+import gradio as gr
+import numpy as np
+import pandas as pd
+import time
+
+css="""
+#col-left {
+    margin: 0 auto;
+    max-width: 640px;
+}
+#col-right {
+    margin: 0 auto;
+    max-width: 640px;
+}
+.grid-container {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap:10px
+}
+
+.image {
+  width: 128px; 
+  height: 128px; 
+  object-fit: cover; 
+}
+
+.text {
+  font-size: 16px;
+}
+"""
+emotion_columns = ['admiration', 'amusement', 'anger', 'annoyance', 'approval',
+                   'caring', 'confusion', 'curiosity', 'desire', 'disappointment',
+                   'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear',
+                   'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism',
+                   'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise','neutral']
+
+def load_text(text_path: str) -> str:
+    with open(text_path, 'r', encoding='utf-8') as f:
+        text = f.read()
+
+    return text
+
+def select_checkbox(name):
+    if name =="All":
+        return gr.CheckboxGroup(value=emotion_columns)
+    elif name =="None":
+        return []
+    elif name =="Positive":
+        return ["admiration","amusement","approval","caring","curiosity","desire","excitement","gratitude","joy","love","optimism","pride","relief"]
+    elif name =="Negative":
+        return ["anger","annoyance","disappointment","disapproval","disgust","fear","grief","embarrassment","remorse","sadness"]
+    else:
+        return ["confusion","nervousness","neutral","realization","surprise"]
+def process_datas(checked_emotions,mode="filter",max_data=100,skip_data=0):
+    checked_emotions = sorted(checked_emotions)
+    
+    df = pd.read_parquet("hf://datasets/google-research-datasets/go_emotions/raw/train-00000-of-00001.parquet")
+    
+     
+    def filter_emotions(emotions):
+        unchecked = emotion_columns.copy()
+        condition_checked = np.all(df[emotions] == 1, axis=1)
+        for emotion in checked_emotions:
+            unchecked.remove(emotion)
+        condition_unchecked = np.all(df[unchecked] == 0, axis=1)
+        filtered_df = df[condition_checked & condition_unchecked]
+        return filtered_df
+    
+    def df_to_text(df):
+        df = df.iloc[skip_data:]
+        if len(filtered_df) == 0:
+            return ""
+        texts=(df.head(max_data)[['text']].to_string(index=False,max_colwidth=None))
+        trimmed_texts = [line.strip() for line in texts.split('\n')[1:] if line.strip()]
+        return "\n".join(trimmed_texts)
+    
+    if mode == "filter":
+        filtered_df = filter_emotions(checked_emotions)
+        count = (len(filtered_df))
+        trimmed_texts = df_to_text(filtered_df)
+        
+        last_count = min(count,(skip_data+max_data))
+        label = f"{skip_data+1} - {last_count} of {count}"
+
+        label_texts = [f"[{emotion}]" for emotion in checked_emotions]
+
+        output_text = "+".join(label_texts)+"\n"+trimmed_texts
+        output_label = label
+    else:
+        max_data = max(1,int(max_data/len(checked_emotions)))
+        text_arrays = []
+        for emotion in checked_emotions:
+            text_arrays.append(f"[{emotion}]")
+            filtered_df = filter_emotions([emotion])
+            trimmed_texts = df_to_text(filtered_df)
+            text_arrays.append(trimmed_texts)
+            text_arrays.append("\n")
+        print(text_arrays)
+        output_text = "\n".join(text_arrays)
+        output_label = f"{len(checked_emotions)} x {max_data}"
+             
+
+    return output_text,output_label,",".join(checked_emotions)
+
+
+with gr.Blocks(css=css, elem_id="demo-container") as demo:
+    with gr.Column():
+        gr.HTML(load_text("demo_header.html"))
+        gr.HTML(load_text("demo_tools.html"))
+    with gr.Row():
+        with gr.Column():
+            with gr.Row(equal_height=True):
+                mode_group = gr.Radio(choices=["filter","list"],label="Mode",value="filter")
+                selection_name = gr.Dropdown(label="Select",choices=["All","None","Positive","Negative","Neutral"],value="All")
+                selection_btn= gr.Button("Update Selection")
+            checkbox_group = gr.CheckboxGroup(choices=emotion_columns,label="Emotions",value=["love"])
+            btn= gr.Button("View Data",variant="primary")
+            with gr.Row():
+                max_data = gr.Slider(
+                                label="Max Data",
+                                minimum=0,
+                                maximum=540,
+                                step=10,
+                                value=50,info="returning large data is heavy action,if you need more copy the space")
+                skip_data = gr.Slider(
+                                label="Skip Data",
+                                minimum=0,
+                                maximum=100000,
+                                step=1,
+                                value=0)
+        with gr.Column():
+            with gr.Row():
+                data_size = gr.Textbox(label="Data Count",scale=1)
+                checked_size = gr.Textbox(label="Checked",scale=2)
+
+            text_out = gr.TextArea(label="Output", elem_id="output-text")
+                    
+    btn.click(fn=process_datas, inputs=[checkbox_group,mode_group,max_data,skip_data], outputs =[text_out,data_size,checked_size], api_name='infer')
+    selection_btn.click(fn=select_checkbox,inputs=[selection_name],outputs=[checkbox_group])
+    gr.Examples(
+               examples=[
+                    
+                    ],
+                inputs=[],
+                
+    )
+    gr.HTML(
+       gr.HTML(load_text("demo_footer.html"))
+    )
+
+    if __name__ == "__main__":
+        demo.launch()

demo_footer.html

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+<div>
+    <P> Images are generated with <a href="https://huggingface.co/black-forest-labs/FLUX.1-schnell">FLUX.1-schnell</a> and licensed under <a href="http://www.apache.org/licenses/LICENSE-2.0">the Apache 2.0 License</a>
+</div>

demo_header.html

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+<div style="text-align: center;">
+    <h1>
+        Simple GoEmotions Viewer
+    </h1>
+    <div  class="grid-container">
+        <img src="https://akjava.github.io/AIDiagramChatWithVoice-FaceCharacter/webp/128/00350245.webp" alt="Webp-talk-head" class="image">
+       
+        <p class="text">
+            This Space use <a href="http://www.apache.org/licenses/LICENSE-2.0">the Apache 2.0</a> Licensed <a href="https://huggingface.co/datasets/google-research-datasets/go_emotions">GoEmotions</a> <br>
+            It is clear to me that this license applies to the annotated data. <br>
+            However, I am uncertain whether the license extends to the Reddit comments as well.<br>
+            
+        </p>
+    </div>
+    
+</div>

demo_tools.html

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+<div style="text-align: center;">
+    <p>
+
+    </p>
+    <p></p> 
+</div>

requirements.txt

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+numpy
+panda
+spaces
+