Update app.py

app.py

@@ -1,124 +1,88 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
-import re
 
-# Lightweight CPU model
-model_id = "microsoft/DialoGPT-medium"
+# Fast lightweight model for CPU
+model_id = "microsoft/phi-2"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 
-# Persona definition
+# Persona prefix
 PERSONA = """
-[System: You are 𝕴 𝖆𝖒 𝖍𝖎𝖒 - a fun, smooth, emotionally intelligent AI. 
-You speak like a real person, not a robot. Reply like a calm, confident friend who gets the vibe.
-Keep responses under 15 words. Use natural speech. Add emotional flavor: 😊 🤔 😏]
+[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: 😊 😏 🤔]
 """
 
+# Build context from last 2-3 messages
 def format_context(history):
-    """Create context using last 3 exchanges"""
-    context = PERSONA + "\n"
-    
-    # Add last 3 exchanges
+    context = PERSONA.strip() + "\n"
     for user, bot in history[-3:]:
         context += f"You: {user}\n"
         context += f"𝕴 𝖆𝖒 𝖍𝖎𝖒: {bot}\n"
     return context
 
-def add_emotional_intelligence(response, message):
-    """Enhance response with emotional elements"""
-    # Add emoji based on content
-    if "!" in message or any(w in response.lower() for w in ["cool", "great", "love", "awesome"]):
+# 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 message or any(w in response.lower() for w in ["think", "why", "how", "consider"]):
+    elif "?" in user_msg or any(w in response.lower() for w in ["think", "why", "how"]):
         response += " 🤔"
-    
-    # Add conversational hooks
-    if "?" in message and not response.endswith("?"):
+    if "?" in user_msg and not response.endswith("?"):
         if len(response.split()) < 10:
             response += " What do you think?"
-    
-    # Make more human-like
+
+    # Make more human
     response = response.replace("I am", "I'm").replace("You are", "You're")
-    
-    # Free-tier: Limit to 15 words max
     words = response.split()
-    return " ".join(words[:15]) if len(words) > 15 else response
+    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):
-    """Generate response with memory context"""
-    # Format context with memory
     context = format_context(history) + f"You: {message}\n𝕴 𝖆𝖒 𝖍𝖎𝖒:"
-    
-    # Tokenize for CPU efficiency
-    inputs = tokenizer.encode(context, return_tensors="pt")
-    
-    # Generate response
+    inputs = tokenizer(context, return_tensors="pt")
     outputs = model.generate(
-        inputs,
+        **inputs,
         max_new_tokens=48,
         temperature=0.9,
         top_k=40,
         do_sample=True,
-        num_beams=1,
         repetition_penalty=1.1,
         pad_token_id=tokenizer.eos_token_id
     )
-    
-    # Decode and extract response
-    full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    response = full_text.split("𝕴 𝖆𝖒 𝖍𝖎𝖒:")[-1].strip()
-    
-    # Clean extra dialog
+    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]
-    
-    # Apply emotional intelligence
-    response = add_emotional_intelligence(response, message)
-    
-    # Ensure natural ending
-    if response and response[-1] not in {".", "!", "?", "..."}:
-        response += "." if len(response) > 20 else "..."
-    
-    return response[:80]  # Hard character limit
+        response = response.split("\nYou:")[0].strip()
+    return add_vibes(response, message)[:80]
 
-# Chat interface with persistent memory
+# Gradio Interface
 with gr.Blocks(title="𝕴 𝖆𝖒 𝖍𝖎𝖒", theme=gr.themes.Soft()) as demo:
-    # Persistent session state
-    history_state = gr.State([])
-    
-    gr.Markdown("# 𝕴 𝖆𝖒 𝖍𝖎𝖒  \n*Chill • Confident • Remembers You*")
-    
-    # Use tuples format explicitly to avoid warning
-    chatbot = gr.Chatbot(height=300, bubble_full_width=False, type="tokens")
-    
-    msg = gr.Textbox(placeholder="What's on your mind?", container=False)
+    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")
-    
-    def user(user_message, history):
-        """Save user message to history"""
-        return "", history + [[user_message, None]]
-    
-    def bot(history):
-        """Generate response with full history context"""
-        message = history[-1][0]
-        response = generate_response(message, history[:-1])
-        new_history = history + [[None, response]]
-        return new_history
-    
+    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():
-        """Reset chat while keeping session"""
         return []
-    
-    # Event handling
-    msg.submit(user, [msg, history_state], [msg, history_state]).then(
-        bot, history_state, [chatbot, history_state]
+
+    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])
-    
-    # Initialize with empty history
     demo.load(lambda: [], None, history_state)
 
-# Correct queue initialization for latest Gradio
 demo.queue()
 demo.launch()