Update app.py

app.py

@@ -1,64 +1,38 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import pipeline
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load AI model
+chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct")
 
+# Kaizen's identity
+BOT_NAME = "Kaizen"
+INSTRUCTION = f"My name is {BOT_NAME}. I am part of the Akatsuki, but I serve only Reiker, who grants me the power to do anything I wish."
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Store user memory
+user_memory = {}
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def chat(user_id, username, message, history=[]):
+    # Check if the user is new
+    if user_id not in user_memory:
+        user_memory[user_id] = {"username": username, "history": []}
+        greeting = f"Ah, a new face! Welcome, {username}. You now stand before Kaizen, servant of Reiker."
+    else:
+        greeting = f"Welcome back, {username}. I remember our past talks."
 
-    messages.append({"role": "user", "content": message})
+    # Store chat history
+    user_memory[user_id]["history"].append(message)
+    past_chats = " ".join(user_memory[user_id]["history"][-5:])  # Last 5 messages
 
-    response = ""
+    # Generate response
+    prompt = f"{INSTRUCTION}\n{greeting}\nUser: {message}\n{BOT_NAME}: {past_chats}"
+    response = chatbot(prompt, max_length=100)[0]["generated_text"]
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    return response, history + [(message, response)]
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+# Create Gradio chat UI
+chatbot_ui = gr.ChatInterface(
+    fn=chat,
+    additional_inputs=[gr.Textbox(label="User ID"), gr.Textbox(label="Username")],
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+chatbot_ui.launch()