Update app.py

app.py

@@ -41,7 +41,7 @@ def randomize_seed_fn(seed: int) -> int:
     seed = random.randint(0, 999999)
     return seed
 
-default_system_instructions = """
+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. 
 Avoid unnecessary introductions and answer the user's questions directly. 
@@ -50,7 +50,7 @@ Respond in a manner that reflects your expertise and wisdom.
 """
 
 # Initialize an empty DataFrame to store the history
-history_df = pd.DataFrame(columns=['Timestamp', 'Request', 'Response', 'Model', 'Input Size', 'Output Size'])
+history_df = pd.DataFrame(columns=['Timestamp', 'Model', 'Input Size', 'Output Size', 'Request', 'Response'])
 
 def save_history():
     history_df_copy = history_df.copy()
@@ -63,10 +63,10 @@ def load_history():
         history_df = pd.read_json('chat_history.json', orient='records')
         history_df['Timestamp'] = pd.to_datetime(history_df['Timestamp'])
     else:
-        history_df = pd.DataFrame(columns=['Timestamp', 'Request', 'Response', 'Model', 'Input Size', 'Output Size'])
+        history_df = pd.DataFrame(columns=['Timestamp', 'Model', 'Input Size', 'Output Size', 'Request', 'Response'])
     return history_df
 
-def models(text, model="Llama 3 8B", seed=42, system_instructions=default_system_instructions):
+def models(text, model="Llama 3 8B", seed=42):
     global history_df
     
     seed = int(randomize_seed_fn(seed))
@@ -78,7 +78,7 @@ def models(text, model="Llama 3 8B", seed=42, system_instructions=default_system
         max_new_tokens=300,
         seed=seed
     )    
-    formatted_prompt = system_instructions + text + "[DR. NOVA QUANTUM]"
+    formatted_prompt = system_instructions1 + text + "[DR. NOVA QUANTUM]"
     stream = client.text_generation(
         formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
     output = ""
@@ -86,6 +86,18 @@ def models(text, model="Llama 3 8B", seed=42, system_instructions=default_system
         if not response.token.text == "</s>":
             output += response.token.text
     
+    # Add the current interaction to the history DataFrame
+    new_row = pd.DataFrame({
+        'Timestamp': [datetime.now()],
+        'Model': [model],
+        'Input Size': [len(text)],
+        'Output Size': [len(output)],
+        'Request': [text],
+        'Response': [output]
+    })
+    history_df = pd.concat([history_df, new_row], ignore_index=True)
+    save_history()
+    
     return output
 
 # Add a list of available voices
@@ -97,13 +109,14 @@ VOICES = [
     "en-CA-ClaraNeural",
 ]
 
-async def respond(input_text, model, seed, voice, system_instructions):
-    reply = models(input_text, model, seed, system_instructions)
+async def respond(audio, model, seed, voice):
+    user = transcribe(audio)
+    reply = models(user, model, seed)
     communicate = edge_tts.Communicate(reply, voice=voice)
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
         tmp_path = tmp_file.name
         await communicate.save(tmp_path)
-    return tmp_path, reply
+    return tmp_path
 
 def display_history():
     df = load_history()
@@ -120,9 +133,6 @@ def download_history():
     href = f'data:text/csv;base64,{b64}'
     return gr.HTML(f'<a href="{href}" download="chat_history.csv">Download Chat History</a>')
 
-def new_chat():
-    return None, None, gr.Markdown.update(value=""), gr.Markdown.update(value=""), gr.DataFrame.update(value=pd.DataFrame())
-
 DESCRIPTION = """# <center>Dr. Nova Quantum⚡ - Your Personal Guide to the Frontiers of Science and Technology</center>"""
 
 with gr.Blocks(css="style.css") as demo:    
@@ -151,65 +161,28 @@ with gr.Blocks(css="style.css") as demo:
             label="Dr. Nova Quantum's Voice"
         )
     
-    system_prompt = gr.Textbox(
-        label="System Prompt",
-        placeholder="Edit the system prompt here...",
-        value=default_system_instructions,
-        lines=5
-    )
-    
-    with gr.Row():
-        input_audio = gr.Audio(label="User (Audio)", sources="microphone", type="filepath")
-        input_text = gr.Textbox(label="User (Text)", placeholder="Type your message here...")
-    
+    input_audio = gr.Audio(label="User", sources="microphone", type="filepath")
     output_audio = gr.Audio(label="Dr. Nova Quantum", type="filepath", autoplay=True)
     
     request_md = gr.Markdown(label="User Request")
     response_md = gr.Markdown(label="Dr. Nova Quantum Response")
     
-    history_display = gr.DataFrame(label="Conversation History", headers=["Timestamp", "Request", "Response", "Model", "Input Size", "Output Size"])
+    history_display = gr.DataFrame(label="Conversation History", headers=["Timestamp", "Model", "Input Size", "Output Size", "Request", "Response"])
     
-    new_chat_button = gr.Button("New Chat")
     download_button = gr.Button("Download Conversation History")
     download_link = gr.HTML()
     
-    def process_input(input_audio, input_text, model, seed, voice, system_instructions):
-        if input_audio is not None:
-            text = transcribe(input_audio)
-        else:
-            text = input_text
-        
-        response, reply = asyncio.run(respond(text, model, seed, voice, system_instructions))
-        
-        # Update history
-        new_row = pd.DataFrame({
-            'Timestamp': [datetime.now()],
-            'Request': [text],
-            'Response': [reply],
-            'Model': [model],
-            'Input Size': [len(text)],
-            'Output Size': [len(reply)]
-        })
-        global history_df
-        history_df = pd.concat([history_df, new_row], ignore_index=True)
-        save_history()
-        
-        return response, display_history(), text, reply
+    def process_audio(audio, model, seed, voice):
+        response = asyncio.run(respond(audio, model, seed, voice))
+        text = transcribe(audio)
+        return response, display_history(), text, models(text, model, seed)
 
     input_audio.change(
-        fn=process_input,
-        inputs=[input_audio, input_text, select, seed, voice_select, system_prompt],
+        fn=process_audio,
+        inputs=[input_audio, select, seed, voice_select],
         outputs=[output_audio, history_display, request_md, response_md]
     )
     
-    input_text.submit(
-        fn=process_input,
-        inputs=[input_audio, input_text, select, seed, voice_select, system_prompt],
-        outputs=[output_audio, history_display, request_md, response_md]
-    )
-    
-    new_chat_button.click(fn=new_chat, outputs=[input_audio, input_text, request_md, response_md, history_display])
-    
     download_button.click(fn=download_history, outputs=[download_link])
 
     demo.load(fn=display_history, outputs=[history_display])