Update app.py

app.py

@@ -2,15 +2,15 @@ import gradio as gr
 import pandas as pd
 from load_data import *
 import os
-os.system("pip install git+https://github.com/openai/whisper.git")
-import whisper
+#os.system("pip install git+https://github.com/openai/whisper.git")
+#import whisper
 
-model = whisper.load_model("small")
-current_size = 'small'
+#model = whisper.load_model("small")
+#current_size = 'small'
 
 
 hf_writer = gr.HuggingFaceDatasetSaver('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', "Pavankalyan/chitti_data")
-
+'''
 def inference(audio):
     audio = whisper.load_audio(audio)
     audio = whisper.pad_or_trim(audio)
@@ -21,8 +21,9 @@ def inference(audio):
     
     options = whisper.DecodingOptions(fp16 = False)
     result = whisper.decode(model, mel, options)
-    
-    query = result.text
+    return result.text
+    '''
+def chitti(query):
     re_table = search(query)
     answers_re_table = [re_table[i][0] for i in range(0,5)]
     answer_links = [re_table[i][3] for i in range(0,5)]
@@ -40,8 +41,9 @@ def inference(audio):
     #return [re_table[0][0],re_table[0][3],re_table[1][0],re_table[1][3]]
 
 demo = gr.Interface(
-    fn=inference,
-    inputs=[gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio")],
+    fn=chitti,
+    inputs=["text"]
+    #inputs=[gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio")],
     outputs=["text","text","text","text"],
     allow_flagging = "manual",
     flagging_options = ["0","1","None"],