Update app.py

app.py

@@ -1,28 +1,76 @@
 import os
 import gradio as gr
 HF_TOKEN = os.getenv('HW_TOKEN')
-print (HF_TOKEN)
+#print (HF_TOKEN)
 hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "save_audio")
 
 
-def calculator(num1, operation, num2):
-    if operation == "add":
-        return num1 + num2
-    elif operation == "subtract":
-        return num1 - num2
-    elif operation == "multiply":
-        return num1 * num2
-    elif operation == "divide":
-        return num1 / num2
-
-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
-)
-
-iface.launch()
+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())
+    #allow_flagging="manual", 
+    #flagging_callback=hf_writer
+    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)
+    #flagging_callback=hf_writer
+    b1.click(lambda *args:  hf_writer, [text, upfile], None, preprocess=False)
+demo.launch()