Create app.py

# initializing the blueprint of the whole app

uploading the skeleton structure of the app with the placeholders to check how the app would like once uploaded.

app.py

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+import gradio as gr
+import pandas as pd
+
+def upload_file(files): # the actual translation should happen here
+    """
+    takes the file that comes from the UI and converts it to the respective
+    format to be sent to the model for transcription
+    """
+    # Define the column names
+    columns = ["audio_id", "transcribed_text( in org lang )", "transcribed_text( in eng )", "language"]
+
+    # Define the data as lists. Here the data would be sent in the form of single data fro mthe data
+
+    # data = [ ["ca_1.wav", "बिना किसी पूर्व सूचना के विलंबित या रद्द की गई ट्रिनिक", "without any prior information or any delay or delay in the train journey", "hindi"]]
+
+
+    # Create the DataFrameoutputs
+    df = pd.DataFrame(data, columns=columns)
+
+    return df
+
+
+with gr.Blocks() as demo:
+  gr.Markdown( "# V2T Engine"  )
+  with gr.Accordion("Steps to run the App"):
+    gr.Markdown("1. Click the \"Click to Upload a File\" to get the dialog box to browse your local path." )
+    gr.Markdown("2. The audio will get uploaded, transcribed into the ")
+    gr.Markdown("3. ")
+
+  upload_button = gr.UploadButton("Click to Upload a File", file_types=["audio"], file_count="single")
+  df_output = gr.Dataframe(
+            headers=["audio_id", "transcribed_text( in eng )", "transcribed_text( in org lang )", "language"],
+            datatype=["str", "str", "str"],
+            row_count=1,
+            col_count=(4, "fixed"),
+    )
+  upload_button.upload(upload_file, upload_button, df_output, show_progress = True) # upload the audio file and and sends to the upload function
+
+
+
+if __name__ == "__main__":
+    demo.launch()