Update app.py

app.py

@@ -13,7 +13,85 @@ from datetime import datetime
 import base64
 import io
 
-# ... (previous imports and functions remain the same)
+default_lang = "en"
+engines = { default_lang: Model(default_lang) }
+
+def transcribe(audio):
+    lang = "en"
+    model = engines[lang]
+    text = model.stt_file(audio)[0]
+    return text
+
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+def client_fn(model):
+    if "Mixtral" in model:
+        return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+    elif "Llama" in model:
+        return InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
+    elif "Mistral" in model:
+        return InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
+    elif "Phi" in model:
+        return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
+    else: 
+        return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
+
+def randomize_seed_fn(seed: int) -> int:
+    seed = random.randint(0, 999999)
+    return seed
+
+system_instructions1 = """
+[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 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 models(text, model="Mixtral 8x7B", seed=42):
+    global history_df
+    
+    seed = int(randomize_seed_fn(seed))
+    generator = torch.Generator().manual_seed(seed)  
+    
+    client = client_fn(model)
+    
+    generate_kwargs = dict(
+        max_new_tokens=300,
+        seed=seed
+    )    
+    formatted_prompt = system_instructions1 + text + "[JARVIS]"
+    stream = client.text_generation(
+        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+    for response in stream:
+        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().strftime("%Y-%m-%d %H:%M:%S")],  # Convert to string
+        'Request': [text],
+        'Response': [output]
+    })
+    history_df = pd.concat([history_df, new_row], ignore_index=True)
+    
+    return output
+
+async def respond(audio, model, seed):
+    user = transcribe(audio)
+    reply = models(user, model, seed)
+    communicate = edge_tts.Communicate(reply)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+        tmp_path = tmp_file.name
+        await communicate.save(tmp_path)
+    return tmp_path
+
+def display_history():
+    return history_df
 
 def download_history():
     csv_buffer = io.StringIO()
@@ -21,7 +99,7 @@ def download_history():
     csv_string = csv_buffer.getvalue()
     b64 = base64.b64encode(csv_string.encode()).decode()
     href = f'data:text/csv;base64,{b64}'
-    return href
+    return gr.HTML(f'<a href="{href}" download="chat_history.csv">Download Chat History</a>')
 
 DESCRIPTION = """ # <center><b>JARVIS⚡</b></center>
         ### <center>A personal Assistant of Tony Stark for YOU
@@ -59,12 +137,10 @@ with gr.Blocks(css="style.css") as demo:
     download_button = gr.Button("Download History")
     download_link = gr.HTML()
     
-    demo.load(fn=lambda: gr.update(visible=True), outputs=[download_button])
-    
     def process_audio(audio, model, seed):
         response = asyncio.run(respond(audio, model, seed))
-        return next(response)
-    
+        return response
+
     input_audio.change(
         fn=process_audio,
         inputs=[input_audio, select, seed],