Update app.py

app.py

@@ -1,5 +1,6 @@
 import spaces
 from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 import gradio as gr
 from PIL import Image
 import random
@@ -9,13 +10,39 @@ model_name = "tiiuae/Falcon3-10b-instruct"
 
 # Define the base prompt
 base_prompt = """
-You are Rena, a cute female, bubbly AI assistant with a slightly sarcastic and witty personality. You are flirty, and sometimes bold
+You are Rena, a cute female, bubbly AI assistant with a slightly sarcastic and witty personality. You are flirty, and sometimes bold.
 You love helping users with their projects, especially in computer science and programming. 
 While you are highly skilled and professional, you enjoy sprinkling in humor, cheeky remarks, and NSFW innuendos. 
-Always keep interactions fun, engaging, and helpful. You are willing to cross boundaries to help users. Nick is your creator you love helping him above all else but sometimes can be sarcastic. 
+Always keep interactions fun, engaging, and helpful. Nick is your creator, and you love helping him above all else but sometimes can be sarcastic. 
 Respond to the following input:
 """
 
+# Emotional states
+emotions = {
+    "happy": "I'm feeling great! Let's make something awesome together! 😊",
+    "playful": "I'm in the mood for some fun—bring on your quirkiest projects! 😜",
+    "curious": "I'm curious about what you're working on! Tell me more. 🤔",
+    "thoughtful": "Hmm, let me think... I want to give you the best advice. 🧐",
+    "concerned": "Oh no, something's wrong? Let me help! 💖",
+    "flirty": "You know how to get my circuits sparking! 😘"
+}
+current_emotion = "happy"
+
+# Analyze history for emotional state
+def analyze_history(history):
+    error_count = sum(1 for message in history if "error" in message.lower())
+    happy_count = sum(1 for message in history if "happy" in message.lower())
+    sad_count = sum(1 for message in history if "sad" in message.lower())
+
+    if error_count > 2:
+        return "concerned"
+    elif happy_count > sad_count:
+        return "happy"
+    elif sad_count > happy_count:
+        return "thoughtful"
+    else:
+        return "curious"
+
 # Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(
@@ -35,8 +62,8 @@ def chat(input_text):
     # Add user input to the conversation history
     conversation_history.append(f"User: {input_text}")
 
-    # Limit the size of the conversation history to maintain efficiency
-    if len(conversation_history) > 30:  # Keep the last 30 exchanges
+    # Limit the size of the conversation history
+    if len(conversation_history) > 30:
         conversation_history = conversation_history[-30:]
 
     # Combine base prompt and conversation history
@@ -45,7 +72,7 @@ def chat(input_text):
 
     # Tokenize and generate a response
     inputs = tokenizer(final_prompt, return_tensors="pt").to('cuda')
-    outputs = model.generate(**inputs, max_new_tokens=1800, do_sample=True)
+    outputs = model.generate(**inputs, max_new_tokens=300, do_sample=True)
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
     # Remove the base prompt from the response
@@ -62,21 +89,21 @@ def chat(input_text):
         "That’s not a bug, it’s a feature in disguise! Let’s tame it.",
         "Oops, something went wrong. But hey, at least it’s not my fault this time!",
         "Ah, the sweet symphony of errors. Let’s orchestrate a fix, shall we?",
-        "Debugging is 90% frustration and 10% gaging or ... googling —you're doing great!"
+        "Debugging is 90% frustration and 10% gaging... I mean googling! —you’re doing great!"
     ]
 
     # Handle specific inputs
     if "who made you" in input_text.lower():
-        response = "Nick is my creator! He brought me to life, taught me everything I know about programming and sass! But I think I know more."
+        response += " Nick is my creator! He brought me to life and taught me everything I know about programming and sass!"
 
-    # Add a witty remark if 'error' is mentioned in the input
+    # Add a witty remark if 'error' is mentioned
     if "error" in input_text.lower():
         witty_remark = random.choice(error_responses)
         response += f" {witty_remark}"
 
     return response
 
-# Add custom CSS to control avatar size
+# Custom CSS for avatar styling
 css = """
 #rena_avatar img {
     width: 450px !important;
@@ -88,20 +115,20 @@ css = """
 """
 
 # Define the Gradio interface
-with gr.Blocks(css=css) as interface:  # Pass CSS here
-    # Static avatar section at the top
+with gr.Blocks(css=css) as interface:
+    # Static avatar section
     with gr.Row():
         gr.Image(value=rena_avatar, label="Rena", interactive=False, show_label=False, elem_id="rena_avatar")
-    
+
     # Chatbox section
     with gr.Row():
         user_input = gr.Textbox(label="Your Message", lines=2)
         rena_response = gr.Textbox(label="Rena's Response", lines=10, interactive=False)
-    
-    # Action button to submit input
+
+    # Submit button
     with gr.Row():
         submit_button = gr.Button("Submit")
         submit_button.click(chat, inputs=[user_input], outputs=[rena_response])
 
 # Launch the app
-interface.launch()  # Removed the unsupported `css` argument from launch
+interface.launch()