Update app.py

app.py

@@ -2,87 +2,49 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-# Fast lightweight model for CPU
-model_id = "microsoft/phi-2"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
+# Load model (CPU-friendly)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2")
+model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2")
 
-# Persona prefix
+# Style prompt
 PERSONA = """
-[System: You are 𝕴 𝖆𝖒 𝖍𝖎𝖒 - a chill, smooth, confident, emotionally intelligent AI.
-Speak like a witty friend, not a robot. 
-Keep replies natural, short (<15 words), human, and with emotional vibes: 😊 😏 🤔]
+[System: You are 𝕴 𝖆𝖒 𝖍𝖎𝖒, a smooth, chill AI who replies with emotion and charm in under 15 words.]
 """
 
-# Build context from last 2-3 messages
-def format_context(history):
-    context = PERSONA.strip() + "\n"
+# Function to format past conversation
+def format_prompt(message, history):
+    prompt = PERSONA
     for user, bot in history[-3:]:
-        context += f"You: {user}\n"
-        context += f"𝕴 𝖆𝖒 𝖍𝖎𝖒: {bot}\n"
-    return context
-
-# Add emotional flavor
-def add_vibes(response, user_msg):
-    if any(word in response.lower() for word in ["cool", "love", "great", "awesome", "!"]):
-        response += " 😊"
-    elif "?" in user_msg or any(w in response.lower() for w in ["think", "why", "how"]):
-        response += " 🤔"
-    if "?" in user_msg and not response.endswith("?"):
-        if len(response.split()) < 10:
-            response += " What do you think?"
-
-    # Make more human
-    response = response.replace("I am", "I'm").replace("You are", "You're")
-    words = response.split()
-    response = " ".join(words[:15]) if len(words) > 15 else response
-    if response and response[-1] not in {".", "!", "?", "..."}:
-        response += "." if len(response) > 20 else "..."
-    return response
-
-# Generate the bot's reply
-def generate_response(message, history):
-    context = format_context(history) + f"You: {message}\n𝕴 𝖆𝖒 𝖍𝖎𝖒:"
-    inputs = tokenizer(context, return_tensors="pt")
+        prompt += f"\nYou: {user}\n𝕴 𝖆𝖒 𝖍𝖎𝖒: {bot}"
+    prompt += f"\nYou: {message}\n𝕴 𝖆𝖒 𝖍𝖎𝖒:"
+    return prompt
+
+# Chat function
+def chat(message, history):
+    prompt = format_prompt(message, history)
+    inputs = tokenizer(prompt, return_tensors="pt")
     outputs = model.generate(
         **inputs,
         max_new_tokens=48,
         temperature=0.9,
-        top_k=40,
+        top_k=50,
         do_sample=True,
-        repetition_penalty=1.1,
         pad_token_id=tokenizer.eos_token_id
     )
-    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    response = decoded.split("𝕴 𝖆𝖒 𝖍𝖎𝖒:")[-1].strip()
-    if "\nYou:" in response:
-        response = response.split("\nYou:")[0].strip()
-    return add_vibes(response, message)[:80]
-
-# Gradio Interface
-with gr.Blocks(title="𝕴 𝖆𝖒 𝖍𝖎𝖒", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 𝕴 𝖆𝖒 𝖍𝖎𝖒 😎\n*Chill • Confident • Gets You*")
-    chatbot = gr.Chatbot(height=300, bubble_full_width=False, label="Chat", type="tuples")
-    msg = gr.Textbox(placeholder="Type here...", container=False)
-    clear = gr.Button("New Vibe", size="sm")
-    history_state = gr.State([])
-
-    def user_input(message, history):
-        return "", history + [[message, None]]
-
-    def bot_reply(history):
-        user_msg = history[-1][0]
-        reply = generate_response(user_msg, history[:-1])
-        return history[:-1] + [[user_msg, reply]]
-
-    def clear_chat():
-        return []
-
-    msg.submit(user_input, [msg, history_state], [msg, history_state]).then(
-        bot_reply, history_state, [chatbot, history_state]
-    )
-    clear.click(clear_chat, None, [chatbot, history_state])
-    demo.load(lambda: [], None, history_state)
-
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    reply = result.split("𝕴 𝖆𝖒 𝖍𝖎𝖒:")[-1].split("\n")[0].strip()
+    reply = reply[:80] + " 😊" if len(reply.split()) < 15 else reply[:80]
+    history.append((message, reply))
+    return history, history
+
+# UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 𝕴 𝖆𝖒 𝖍𝖎𝖒 🤖✨")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Drop something smooth...", show_label=False)
+    state = gr.State([])
+
+    msg.submit(chat, [msg, state], [chatbot, state])
+    
 demo.queue()
 demo.launch()