Update app.py

app.py

@@ -1,83 +1,16 @@
 import os
-import warnings
-import gradio as gr
 
+HF_TOKEN = os.getenv('HF_TOKEN')
+hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-calculator-demo")
 
-
-
-# Initialize the speech recognition pipeline and transliterator
- 
-#p1 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-odia_v1")
-#p2 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-hindi_v1")
-
-HF_TOKEN = os.getenv('HW_TOKEN')
-hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "save_audio")
-
-
-cur_line=0
-
-def readFile():
-    f=open('prompt.txt')
-    line_num=0
-    lines=f.readlines()
-    line_num = len(lines)
-    return  line_num,lines
-
-totlines,file_content=readFile()
-
-callback = gr.CSVLogger()
-        
-def readPromt():
-    global cur_line
-    cur_line+=1
-    global file_content
-    print (cur_line)
-    return file_content[cur_line]
-    
-def readNext():
-    
-    global totlines
-    print(totlines)
-    global cur_line
-    if cur_line<totlines-1:
-        cur_line+=1
-        global file_content
-        print (cur_line)
-    return [file_content[cur_line],None]
-
-def readPrevious():
-    global cur_line
-    if cur_line>=0:
-        cur_line-=1
-    #cur_line=current_line
-        global file_content
-        print (cur_line)
-    return [file_content[cur_line],None]
-
-demo = gr.Blocks()
-
-with demo:
-    #dr=gr.Dropdown(["Hindi","Odiya"],value="Odiya",label="Select Language")
-    #audio_file = gr.Audio(sources=["microphone","upload"],type="filepath")
-    text = gr.Textbox(readPromt())
-    upfile =  gr.Audio(
-        sources=["microphone","upload"], type="filepath",  label="Record"
-    )
-    #upfile =  gr.inputs.Audio(source="upload", type="filepath", label="Upload")
-    
-    with gr.Row():
-        b1 = gr.Button("Save")
-        b2 = gr.Button("Next")
-        b3 = gr.Button("Previous")
-        #b4=gr.Button("Clear")
-        b2.click(readNext,inputs=None,outputs=[text,upfile])
-        b3.click(readPrevious,inputs=None,outputs=[text,upfile])
-        #b4.click(lambda: None, outputs=upfile)
-   # b1.click(sel_lng, inputs=[dr,mic,upfile], outputs=text)
-    #b2.click(text_to_sentiment, inputs=text, outputs=label)
-    #callback.setup([text, upfile], "flagged_data_points")
-    #callback.setup([text, upfile], hf_writer)
-    #b1.click(lambda *args: callback.flag(args), [text, upfile], None, preprocess=False)
+iface = gr.Interface(
+    calculator,
+    ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
+    "number",
+    description="Check out the crowd-sourced dataset at: [https://huggingface.co/datasets/aliabd/crowdsourced-calculator-demo](https://huggingface.co/datasets/aliabd/crowdsourced-calculator-demo)",
+    allow_flagging="manual",
+    flagging_options=["wrong sign", "off by one", "other"],
     flagging_callback=hf_writer
-    b1.click(lambda *args:  flagging_callback, [text, upfile], None, preprocess=False)
-demo.launch()
+)
+
+iface.launch()