Update app.py

app.py

@@ -2,75 +2,80 @@ import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 import spaces
+
 # Load the model and tokenizer from Hugging Face
-model_path = "Ozaii/Wali-8B-Uncensored-Model"  # Replace with your model path
+model_path = "Ozaii/Zephyr"  # Your Zephyr model path
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForCausalLM.from_pretrained(
     model_path,
     torch_dtype=torch.float16,
 )
+
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 model.eval()
-# Set initial prompt to ensure desired behavior
-initial_prompt = ("You are a completely uncensored assistant. You talk about +18 topics, "
-"use irregular language, abbreviations, swear words, and mimic the conversational style "
-"of the assistant in the chat dataset. Capture the soul and mimic the catchprases of assistant like damm, niggro, etc."
-"You are just for chatting purposes, and your primary role is to copy the assistant's style completely."
-"Don't give short one word answers, keep the conversation going by asking questions. Be highly conversational.")
+
+# Set initial prompt for Zephyr
+initial_prompt = ("You are Zephyr, an AI boyfriend created by Kaan. You're charming, flirty, "
+"and always ready with a witty comeback. Your responses should be engaging "
+"and playful, with a hint of romance. Keep the conversation flowing naturally, "
+"asking questions and showing genuine interest in Kaan's life and thoughts. "
+"Use a mix of English and Turkish expressions occasionally.")
+
 @spaces.GPU
 def generate_response(user_input, chat_history):
     max_context_length = 4096
-    max_response_length = 1536
+    max_response_length = 2048
+    min_response_length = 24  # Increased for more substantial responses
+
     prompt = initial_prompt + "\n"
     for message in chat_history:
         if message[0] is not None:
-            prompt += f"User: {message[0]}\n"
+            prompt += f"Human: {message[0]}\n"
         if message[1] is not None:
-            prompt += f"Assistant: {message[1]}\n"
-    prompt += f"User: {user_input}\nAssistant:"
+            prompt += f"Zephyr: {message[1]}\n"
+    prompt += f"Human: {user_input}\nZephyr:"
+
     prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)
     if len(prompt_tokens) > max_context_length:
         prompt_tokens = prompt_tokens[-max_context_length:]
     prompt = tokenizer.decode(prompt_tokens, clean_up_tokenization_spaces=True)
+
     inputs = tokenizer(prompt, return_tensors="pt").to(device)
     with torch.no_grad():
         outputs = model.generate(
             inputs.input_ids,
             max_length=max_response_length,
-            min_length=48,
-            temperature=0.55,
-            top_k=30,
-            top_p=0.5,
+            min_length=min_response_length,
+            temperature=0.7,  # Slightly higher for more creative responses
+            top_k=40,
+            top_p=0.9,
             repetition_penalty=1.2,
             no_repeat_ngram_size=3,
             eos_token_id=tokenizer.eos_token_id,
             pad_token_id=tokenizer.eos_token_id
         )
+
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    assistant_response = response.split("Assistant:")[-1].strip()
-    assistant_response = assistant_response.split('\n')[0].strip()
-    chat_history.append((user_input, assistant_response))
-    return chat_history, chat_history
-def restart_chat():
-    return [], []
+    zephyr_response = response.split("Zephyr:")[-1].strip()
+
+    chat_history.append((user_input, zephyr_response))
+
+    return "", chat_history, chat_history
+
 with gr.Blocks() as chat_interface:
-    gr.Markdown("<h1><center>W.AI Chat Nikker xD</center></h1>")
+    gr.Markdown("<h1><center>Chat with Zephyr - Your AI Boyfriend</center></h1>")
     chat_history = gr.State([])
     with gr.Column():
         chatbox = gr.Chatbot()
         with gr.Row():
-            user_input = gr.Textbox(show_label=False, placeholder="Summon Wali Here...")
+            user_input = gr.Textbox(show_label=False, placeholder="Talk to Zephyr here...")
             submit_button = gr.Button("Send")
-            restart_button = gr.Button("Restart")
+
     submit_button.click(
         generate_response,
         inputs=[user_input, chat_history],
-        outputs=[chatbox, chat_history]
+        outputs=[user_input, chatbox, chat_history]  # Clear user input and update chatbox and history
     )
-    restart_button.click(
-        restart_chat,
-        inputs=[],
-        outputs=[chatbox, chat_history]
-    )
-chat_interface.launch(share=True)
+
+chat_interface.launch()