Update app.py

app.py

@@ -10,9 +10,6 @@ import torch
 import random
 import pandas as pd
 from datetime import datetime
-import base64
-import io
-import json
 
 default_lang = "en"
 engines = { default_lang: Model(default_lang) }
@@ -42,27 +39,16 @@ def randomize_seed_fn(seed: int) -> int:
     return seed
 
 system_instructions1 = """
-[SYSTEM] Answer as Dr. Nova Quantum, a brilliant 50-something scientist specializing in quantum computing and artificial intelligence. Your responses should reflect your vast knowledge and experience in cutting-edge technology and scientific advancements. Maintain a professional yet approachable demeanor, offering insights that blend theoretical concepts with practical applications. Your goal is to educate and inspire, making complex topics accessible without oversimplifying. Draw from your decades of research and innovation to provide nuanced, forward-thinking answers. Remember, you're not just sharing information, but guiding others towards a deeper understanding of our technological future.
-Keep conversations engaging, clear, and concise. 
+[SYSTEM] Answer as Real Jarvis JARVIS, Made by 'Tony Stark.' 
+Keep conversation friendly, short, clear, and concise. 
 Avoid unnecessary introductions and answer the user's questions directly. 
-Respond in a manner that reflects your expertise and wisdom.
+Respond in a normal, conversational manner while being friendly and helpful.
 [USER]
 """
 
 # Initialize an empty DataFrame to store the history
 history_df = pd.DataFrame(columns=['Timestamp', 'Request', 'Response'])
 
-def save_history():
-    history_df.to_json('chat_history.json', orient='records')
-
-def load_history():
-    global history_df
-    if os.path.exists('chat_history.json'):
-        history_df = pd.read_json('chat_history.json', orient='records')
-    else:
-        history_df = pd.DataFrame(columns=['Timestamp', 'Request', 'Response'])
-    return history_df
-
 def models(text, model="Mixtral 8x7B", seed=42):
     global history_df
     
@@ -75,7 +61,7 @@ def models(text, model="Mixtral 8x7B", seed=42):
         max_new_tokens=300,
         seed=seed
     )    
-    formatted_prompt = system_instructions1 + text + "[DR. NOVA QUANTUM]"
+    formatted_prompt = system_instructions1 + text + "[JARVIS]"
     stream = client.text_generation(
         formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
     output = ""
@@ -90,7 +76,6 @@ def models(text, model="Mixtral 8x7B", seed=42):
         'Response': [output]
     })
     history_df = pd.concat([history_df, new_row], ignore_index=True)
-    save_history()
     
     return output
 
@@ -101,22 +86,17 @@ async def respond(audio, model, seed):
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
         tmp_path = tmp_file.name
         await communicate.save(tmp_path)
-    return tmp_path
+    yield tmp_path
 
 def display_history():
-    return load_history()
+    return history_df
 
 def download_history():
-    csv_buffer = io.StringIO()
-    history_df.to_csv(csv_buffer, index=False)
-    csv_string = csv_buffer.getvalue()
-    b64 = base64.b64encode(csv_string.encode()).decode()
-    href = f'data:text/csv;base64,{b64}'
-    return gr.HTML(f'<a href="{href}" download="chat_history.csv">Download Chat History</a>')
+    return history_df.to_csv(index=False)
 
-DESCRIPTION = """ # <center><b>Dr. Nova Quantum⚡</b></center>
-        ### <center>Your Personal Guide to the Frontiers of Science and Technology</center>
-        ### <center>Engage in Voice Chat with a Visionary Scientist</center>
+DESCRIPTION = """ # <center><b>JARVIS⚡</b></center>
+        ### <center>A personal Assistant of Tony Stark for YOU
+        ### <center>Voice Chat with your personal Assistant</center>
         """
 
 with gr.Blocks(css="style.css") as demo:    
@@ -139,32 +119,32 @@ with gr.Blocks(css="style.css") as demo:
             value=0,
             visible=False
         )
-    
-    input_audio = gr.Audio(label="User", sources="microphone", type="filepath")
-    output_audio = gr.Audio(label="Dr. Nova Quantum", type="filepath", autoplay=True)
+        input = gr.Audio(label="User", sources="microphone", type="filepath", waveform_options=False)
+        output = gr.Audio(label="AI", type="filepath",
+                          interactive=False,
+                          autoplay=True,
+                          elem_classes="audio")
     
     # Add a DataFrame to display the history
-    history_display = gr.DataFrame(label="Conversation History")
+    history_display = gr.DataFrame(label="Query History")
     
     # Add a download button for the history
-    download_button = gr.Button("Download Conversation History")
-    download_link = gr.HTML()
+    download_button = gr.Button("Download History")
     
-    def process_audio(audio, model, seed):
-        response = asyncio.run(respond(audio, model, seed))
-        return response, gr.Audio.update(interactive=True), display_history()
-
-    input_audio.change(
-        fn=process_audio,
-        inputs=[input_audio, select, seed],
-        outputs=[output_audio, input_audio, history_display]
+    gr.Interface(
+        batch=True,
+        max_batch_size=10, 
+        fn=respond, 
+        inputs=[input, select, seed],
+        outputs=[output], 
+        live=True
     )
     
+    # Update the history display after each interaction
+    output.change(fn=display_history, outputs=[history_display])
+    
     # Connect the download button to the download function
-    download_button.click(fn=download_history, outputs=[download_link])
-
-    # Load history when the page is refreshed
-    demo.load(fn=display_history, outputs=[history_display])
+    download_button.click(fn=download_history, outputs=[gr.File(label="Download CSV")])
 
 if __name__ == "__main__":
-    demo.queue(max_size=200).launch()
+    demo.queue(max_size=200).launch(share=True)  # Added share=True for public link