Update app.py

app.py

@@ -2,37 +2,79 @@ import os
 import warnings
 import gradio as gr
 import re
-import numpy as np
+
 
 HF_TOKEN = os.getenv('HW_TOKEN')
 hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "save_audio")
 
-def sepia(input_img, strength):
-    sepia_filter = strength * np.array(
-        [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]
-    ) + (1-strength) * np.identity(3)
-    sepia_img = input_img.dot(sepia_filter.T)
-    sepia_img /= sepia_img.max()
-    return sepia_img
 
-callback = hf_writer
+cur_line=-1
 
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            img_input = gr.Image()
-            strength = gr.Slider(0, 1, 0.5)
-        img_output = gr.Image()
-    with gr.Row():
-        btn = gr.Button("Flag")
-        
-    # This needs to be called at some point prior to the first call to callback.flag()
-    callback.setup([img_input, strength, img_output], "flagged_data_points")
+def readFile():
+    f=open('prompt.txt')
+    line_num=0
+    lines=f.readlines()
+    line_num = len(lines)
+    return  line_num,lines
+
+totlines,file_content=readFile()
 
-    img_input.change(sepia, [img_input, strength], img_output)
-    strength.change(sepia, [img_input, strength], img_output)
+callback = hf_writer
+        
+def readPromt():
+    global cur_line
+    cur_line+=1
+    global file_content
+    print (cur_line)
+    return file_content[cur_line]
+    
+def readNext():
     
-    # We can choose which components to flag -- in this case, we'll flag all of them
-    btn.click(lambda *args: callback.flag(args), [img_input, strength, img_output], None, preprocess=False)
+    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.launch()
+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)
+    #b1.click(lambda *args:  hf_writer, [text, upfile])
+demo.launch()