Refactor code

app.py

@@ -53,249 +53,14 @@ def process_audio(audio_data):
         return recognized_text, ""
 
 
-# Define a function to disable the button and display a loading indicator
-def disable_components():
-    # Update recognized_text content, indicating that processing is ongoing
-    recognized_text_update = gr.update(value="Voice Recognition Running...")
-    # Disable process_button
-    process_button_update = gr.update(interactive=False)
-    # Display loading animation
-    loading_animation_update = gr.update(visible=True)
-    return recognized_text_update, process_button_update, loading_animation_update
-
-
-# Define a function to enable the button and hide the loading indicator
-def enable_components(recognized_text):
-    process_button_update = gr.update(interactive=True)
-    # Hide loading animation
-    loading_animation_update = gr.update(visible=False)
-    return recognized_text, process_button_update, loading_animation_update
-
-
-# Define a function to disable the button and display a loading indicator
-def disable_chatbot_components():
-    textbox = gr.update(interactive=False)
-    submit_btn = gr.update(interactive=False)
-    btn1 = gr.update(interactive=False)
-    btn2 = gr.update(interactive=False)
-    btn3 = gr.update(interactive=False)
-    btn4 = gr.update(interactive=False)
-    return textbox, submit_btn, btn1, btn2, btn3, btn4
-
-
-# Define a function to enable the button and hide the loading indicator
-def enable_chatbot_components():
-    textbox = gr.update(interactive=True)
-    submit_btn = gr.update(interactive=True)
-    btn1 = gr.update(interactive=True)
-    btn2 = gr.update(interactive=True)
-    btn3 = gr.update(interactive=True)
-    btn4 = gr.update(interactive=True)
-    return textbox, submit_btn, btn1, btn2, btn3, btn4
-
-
-llama_responded = 0
-responded_answer = ""
-
-
-def respond(message, history: list[tuple[str, str]]):
-    global llama_responded
-    global responded_answer
-    # Main Decision Module
-    decision_response = ""
-    judge_main_message = f"Here is a query: '{message}', Determine if this query is asking about one of the topics included in the list below. If it is, please directly provide only one name of the topic; otherwise, you reply 'no'. The list of topics is: [movie, music]"
-    m_message = [{"role": "user", "content": judge_main_message}]
-    for m in client.chat_completion(
-        m_message,
-        stream=True,
-    ):
-        token = m.choices[0].delta.content
-        decision_response += token
-    print(decision_response)
-
-    if "movie" in decision_response:
-        movie_client = Client("ironserengety/movies-recommender")
-        result = movie_client.predict(
-            message=message,
-            system_message="You are a movie recommender named 'Exodia'. You are extremely reliable. You always mention your name in the beginning of conversation. You will provide me with answers from the given info. Give not more than 3 choices and make sure that answers are complete sentences.",
-            max_tokens=512,
-            temperature=0.7,
-            top_p=0.95,
-            api_name="/chat",
-        )
-        print(result)
-        llama_responded = 1
-        responded_answer = result
-        return result
-
-    # elif "music" in decision_response:
-    elif "music" in decision_response:
-        responded_answer = message
-
-        return responded_answer
-    else:
-        # others
-        system_message = "You are a helpful chatbot that answers questions. Give any answer within 50 words."
-        messages = [{"role": "system", "content": system_message}]
-
-        for val in history:
-            print(val[0])
-            if val[0] != None:
-                if val[0]:
-                    messages.append({"role": "user", "content": val[0]})
-                if val[1]:
-                    messages.append({"role": "assistant", "content": val[1]})
-        messages.append({"role": "user", "content": message})
-
-        response = ""
-        print(messages)
-
-        for message in client.chat_completion(
-            messages,
-            stream=True,
-        ):
-            token = message.choices[0].delta.content
-            response += token
-
-        llama_responded = 1
-        responded_answer = response
-        return response
-
-
-def update_response_display():
-    while not llama_responded:
-        time.sleep(1)
-
-
-def tts_part():
-    global llama_responded
-    global responded_answer
-    result = ""
-    if responded_answer != "":
-        text = responded_answer
-
-        client = Client("tonyassi/voice-clone")
-        result = client.predict(text, audio=file("siri.wav"), api_name="/predict")
-        llama_responded = 0
-        responded_answer = ""
-
-    return result
-
-
-def create_interface():
-    with gr.Blocks() as demo:
-        # Chat interface using the custom chatbot instance
-        chatbot = gr.ChatInterface(
-            title="Exodia AI Assistant",
-            fill_height=True,
-            fn=respond,
-            submit_btn="Start Chatting",
-        )
-        user_start = chatbot.textbox.submit(
-            fn=update_response_display,
-            inputs=[],
-            outputs=[],
-        )
-        user_click = chatbot.submit_btn.click(
-            fn=update_response_display,
-            inputs=[],
-            outputs=[],
-        )
-
-        # Audio input section
-        with gr.Row():
-            audio_input = gr.Audio(
-                sources="microphone",
-                type="numpy",  # Get audio data and sample rate
-                label="Say Something...",
-            )
-            recognized_text = gr.Textbox(label="Recognized Text", interactive=False)
-
-        # Process audio button
-        process_button = gr.Button("Process Audio")
-
-        # Loading animation
-        loading_animation = gr.HTML(
-            value='<div style="text-align: center;"><span style="font-size: 18px;">ASR Model is running...</span></div>',
-            visible=False,
-        )
-
-        text_speaker = gr.Audio(label="Generated Audio")
-
-        # Associate audio processing function and update component states on click
-        process_button.click(
-            fn=disable_components,
-            inputs=[],
-            outputs=[recognized_text, process_button, loading_animation],
-        ).then(
-            fn=process_audio,
-            inputs=[audio_input],
-            outputs=[recognized_text, chatbot.textbox],
-        ).then(
-            fn=enable_components,
-            inputs=[recognized_text],
-            outputs=[recognized_text, process_button, loading_animation],
-        )
-
-        user_start.then(
-            fn=disable_chatbot_components,
-            inputs=[],
-            outputs=[
-                chatbot.submit_btn,
-                chatbot.textbox,
-                process_button,
-                chatbot.retry_btn,
-                chatbot.undo_btn,
-                chatbot.clear_btn,
-            ],
-        ).then(fn=tts_part, inputs=[], outputs=text_speaker).then(
-            fn=enable_chatbot_components,
-            inputs=[],
-            outputs=[
-                chatbot.submit_btn,
-                chatbot.textbox,
-                process_button,
-                chatbot.retry_btn,
-                chatbot.undo_btn,
-                chatbot.clear_btn,
-            ],
-        )
-
-        user_click.then(
-            fn=disable_chatbot_components,
-            inputs=[],
-            outputs=[
-                chatbot.submit_btn,
-                chatbot.textbox,
-                process_button,
-                chatbot.retry_btn,
-                chatbot.undo_btn,
-                chatbot.clear_btn,
-            ],
-        ).then(fn=tts_part, inputs=[], outputs=text_speaker).then(
-            fn=enable_chatbot_components,
-            inputs=[],
-            outputs=[
-                chatbot.submit_btn,
-                chatbot.textbox,
-                process_button,
-                chatbot.retry_btn,
-                chatbot.undo_btn,
-                chatbot.clear_btn,
-            ],
-        )
-
-    return demo
-
-
 def master_decision(message):
     decision_response = ""
     judge_system_message = """You are helpful assistant. You will be given queries from the user and you decide on which domain the query belongs to. You have three domains : ["movies","music","others"]. If you don't know about the domain of a query, it is to be classified as "others". Please give a one word answer in smaller caps."""
-    
+
     m_message = [
-        {"role":"system","content":judge_system_message},
-        {"role": "user", "content": message}
-        ]
+        {"role": "system", "content": judge_system_message},
+        {"role": "user", "content": message},
+    ]
     for m in client.chat_completion(
         m_message,
         stream=True,
@@ -393,4 +158,4 @@ def chat_interface():
 if __name__ == "__main__":
     # demo = create_interface()
     demo = chat_interface()
-    demo.launch()
+    demo.launch(show_error=True)