Update app.py

app.py

@@ -1,4 +1,49 @@
 import gradio as gr
+from transformers import pipeline
+from helpers import load_model_file, load_wav_16k_mono_librosa, initialize_text_to_speech_model, load_label_mapping, predict_yamnet, classify, classify_realtime
+from helpers import interface, interface_realtime, updateHistory, clearHistory, clear, format_dictionary, format_json
+from helpers import generate_audio, TTS, TTS_ASR, TTS_chatbot, transcribe_speech, transcribe_speech_realtime, transcribe_realtime, translate_enpt
+from helpers import chatbot_response, add_text
+
+history = ""
+last_answer = ""
+
+examples_audio_classification = [
+    "content/crowd_laughing.mp3",
+    "content/nature-ambient-sound.mp3",
+    "content/talking-people.mp3",
+    "content/miaow_16k.wav",
+]
+
+examples_speech_recognition_en = [
+    "content/speech1-en.wav",
+    "content/speech2-en.wav",
+]
+examples_speech_recognition_ptbr = [
+    "content/speech1-ptbr.wav",
+    "content/speech2-ptbr.wav",
+    "content/speech3-ptbr.wav",
+]
+
+examples_chatbot_en = [
+    ['How does SocialEar assist people with hearing disabilities?'],
+    ['Give me suggestions on how to use SocialEar'],
+    ['How does SocialEar work?'],
+    ['Are SocialEar results accurate?'],
+    ['What accessibility features does SocialEar offer?'],
+    ['Does SocialEar collect personal data?'],
+    ['Can I use SocialEar to identify songs and artists from recorded audio?'],
+    ]
+
+examples_chatbot_ptbr = [
+    ['Como o SocialEar auxilia pessoas com deficiência auditiva?'],
+     ['Dê-me sugestões sobre como usar o SocialEar'],
+     ['Como funciona o SocialEar?'],
+     ['Os resultados do SocialEar são precisos?'],
+     ['Quais recursos de acessibilidade o SocialEar oferece?'],
+     ['O SocialEar coleta dados pessoais?'],
+     ['Posso usar o SocialEar para identificar músicas e artistas de áudio gravado?'],
+    ]
 
 def to_audioClassification():
     return {
@@ -7,7 +52,7 @@ def to_audioClassification():
       speech_recognition: gr.Row(visible=False),
       chatbot_qa: gr.Row(visible=False),
     }
-    
+
 def to_realtimeAudioClassification():
     return {
       audio_classification: gr.Row(visible=False),
@@ -39,10 +84,10 @@ with gr.Blocks() as demo:
       language = gr.Radio(["en-us", "pt-br"], label="Language", info="Choose the language to display the classification result and audio", value='en-us', interactive=True)
 
     with gr.Row():
-      btn0 = gr.Button("Audio Classification", scale=1,  size='lg')
-      btn1 = gr.Button("Realtime Audio Classification", scale=1,size='lg')
-      btn2 = gr.Button("Speech Recognition", scale=1, size='lg')
-      btn3 = gr.Button("Help", scale=1, size='lg')
+      btn0 = gr.Button("Audio Classification", scale=1, icon='content/Audio Classification.png', size='lg')
+      btn1 = gr.Button("Realtime Audio Classification", scale=1, icon='content/Realtime Audio Classification.png', size='lg')
+      btn2 = gr.Button("Speech Recognition", scale=1, icon='content/Speech Recognition.png', size='lg')
+      btn3 = gr.Button("Help", scale=1, icon='content/Chatbot.png', size='lg')
 
     with gr.Row(visible=False) as audio_classification:
           with gr.Column(min_width=700):
@@ -57,6 +102,12 @@ with gr.Blocks() as demo:
                 audioOutput = gr.Audio(label="Audio Output", interactive=False)
 
 
+          inputRecord.stop_recording(interface, [inputRecord, language], [output])
+          inputUpload.upload(interface, [inputUpload, language], [output])
+          btn.click(fn=TTS, inputs=[output, language], outputs=audioOutput)
+
+          examples = gr.Examples(fn=interface, examples=examples_audio_classification, inputs=[inputRecord], outputs=[output], run_on_click=True)
+
     with gr.Row(visible=False) as realtime_classification:
           with gr.Column(min_width=700):
                 input = gr.Audio(label="Audio Input", source="microphone", type="filepath",streaming=True, every=10)
@@ -65,6 +116,11 @@ with gr.Blocks() as demo:
           with gr.Column(min_width=700):
                 output = gr.Label(label="Audio Classification")
 
+          input.change(interface_realtime, [input, language], output)
+          input.change(updateHistory, None, historyOutput)
+          input.start_recording(clearHistory, None, historyOutput)
+
+
     with gr.Row(visible=False) as speech_recognition:
           with gr.Column(min_width=700):
                 with gr.Accordion("Record an Audio", open=True):
@@ -75,13 +131,19 @@ with gr.Blocks() as demo:
           with gr.Column(min_width=700):
                 output = gr.Label(label="Transcription")
 
-    
+
+          inputRecord.stop_recording(transcribe_speech, [inputRecord, language], [output])
+          inputUpload.upload(transcribe_speech, [inputUpload, language], [output])
+
+          examplesSpeechEn = gr.Examples(fn=transcribe_speech, examples=examples_speech_recognition_en, inputs=[inputRecord], outputs=[output], run_on_click=True, label="English Examples")
+          # examplesSpeechPtbr = gr.Examples(fn=transcribe_speech, examples=examples_speech_recognition_ptbr, inputs=[inputRecord], outputs=[output], run_on_click=True, label="Portuguese Examples")
+
     with gr.Row(visible=False) as chatbot_qa:
       chatbot = gr.Chatbot(
         [],
         elem_id="chatbot",
         bubble_full_width=False,
-        # avatar_images=(None, "/content/avatar-socialear.png"),
+        avatar_images=(None, "content/avatar-socialear.png"),
         min_width=2000
       )
       with gr.Row(min_width=2000):
@@ -92,7 +154,24 @@ with gr.Blocks() as demo:
               container=False,
               min_width=1000
           )
-        submit = gr.Button(value="", size='sm', scale=1)
+        submit = gr.Button(value="", size='sm', scale=1, icon='content/send-icon.png')
+
+
+        inputRecord = gr.Audio(label="Record a question", source="microphone", type="filepath", min_width=600)
+        btn = gr.Button(value="Listen the answer")
+        audioOutput = gr.Audio(interactive=False, min_width=600)
+
+        txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
+              chatbot_response, [chatbot, language], chatbot)
+        txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
+        submit.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
+              chatbot_response, [chatbot, language], chatbot).then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
+        inputRecord.stop_recording(transcribe_speech, [inputRecord, language], [txt])
+        btn.click(fn=TTS_chatbot, inputs=[language], outputs=audioOutput)
+
+        with gr.Row(min_width=2000):
+          examplesChatbotEn = gr.Examples(examples=examples_chatbot_en, inputs=[txt], label="English Examples")
+          examplesChatbotPtbr = gr.Examples(examples=examples_chatbot_ptbr, inputs=[txt], label="Portuguese Examples")
 
 
     btn0.click(fn=to_audioClassification, outputs=[audio_classification, realtime_classification, speech_recognition, chatbot_qa])