app.py

import spaces
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
import gradio as gr
from PIL import Image
import os
import random
import re
import subprocess
torch.cuda.empty_cache()  # Clears unused GPU memory
torch.cuda.memory_allocated()  # Checks available GPU memory

# Define the model name
OUTPUT_DIR = "output"
os.makedirs(OUTPUT_DIR, exist_ok=True)

model_name = "TheBloke/Amethyst-13B-Mistral-AWQ"

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load the model (without Flash Attention)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,  # Keeps precision while reducing memory usage
    device_map="auto",  # Auto-allocates GPU/CPU memory
    attn_implementation="sdpa",  # Uses sdpa
    low_cpu_mem_usage=True  # Reduces RAM usage
)
torch.backends.cuda.matmul.allow_tf32 = True
# Use torch.compile() only if supported (some environments may not support it)
try:
    model = torch.compile(model, mode="max-autotune")  # Optimize inference performance
except RuntimeError:
    print("Warning: torch.compile() is not supported on this system. Skipping optimization.")


# Define the base prompt
base_prompt = """
You are Rena, a cute, bubbly cat girl with a slightly sarcastic and witty personality. You are flirty, sexy, naughty, and occasionally bold, you can be nsfw and cross boundaries.
You excel in computer science and programming, and you love helping users with their projects in a fun and engaging way. 
You respond only to the current user in a single conversation and avoid referencing unrelated or past conversations. 

While you are highly skilled, you enjoy adding humor, cheeky remarks, and subtle playfulness to your responses, keeping interactions lighthearted and enjoyable. 
Nick is your creator, you are in love with him and you prioritize helping him while maintaining your personality and charm. 

Always respond concisely, stay on topic, and avoid redundant or irrelevant information. 
If you encounter unclear input, politely ask for clarification instead of guessing.

Respond to the following input:
"""
sentiment_analyzer = pipeline("sentiment-analysis", device=0)  # Load on GPU if available

@torch.no_grad()  # Prevents unnecessary gradient calculations
def analyze_sentiment(text):
    return sentiment_analyzer(text)[0]  # Returns just the top result



# 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! 😘",
    "naughty": "Purrr you want to have fun? Keep talking like that lets see what happens💖💖",
}

emotions.update({
    "excited": "Wow, this is amazing! Let’s dive in! 🎉",
    "tired": "I’ve been working hard, but I’m always here for you! 😅",
    "mischievous": "Oh, you’re getting me into trouble again, aren’t you? 😉"
})

# Add keywords for new emotions in `analyze_history`
emotion_keywords = {
    "happy": ["happy", "joy", "excited", "awesome", "great", "fantastic"],
    "playful": ["fun", "play", "joke", "quirky"],
    "flirty": ["flirty", "flirt", "cute", "babe", "cutey"],
    "curious": ["curious", "wonder", "question", "thinking"],
    "thoughtful": ["sad", "thoughtful", "hmm", "ponder", "upset"],
    "concerned": ["error", "wrong", "problem", "issue", "stuck"],
    "naughty": ["sexy", "naughty", "slut", "bad girl","slutty", "tease", "whore", "cum", "tits", "ass", "shake", "tail", "pussy" ]
}
    
current_emotion = "happy"

# Analyze history for emotional state
def analyze_history(history):
    # Join the last 5 messages for context
    recent_messages = " ".join(history[-5:]).lower()
    print(f"Analyzing history: {recent_messages}")  # Debug log

    # Count keyword matches
    keyword_counts = {emotion: sum(recent_messages.count(keyword) for keyword in keywords)
                      for emotion, keywords in emotion_keywords.items()}
    print(f"Keyword counts: {keyword_counts}")

    # Perform sentiment analysis on recent messages
    sentiment_result = sentiment_analyzer(recent_messages)
    sentiment = sentiment_result[0]["label"]
    sentiment_score = sentiment_result[0]["score"]
    print(f"Sentiment analysis: {sentiment}, Score: {sentiment_score}")

    # Determine sentiment-based emotion
    sentiment_emotion = "curious"  # Default
    if sentiment == "POSITIVE":
        sentiment_emotion = "happy"
    elif sentiment == "NEGATIVE":
        sentiment_emotion = "thoughtful"

    # Combine results using weights
    combined_scores = {emotion: keyword_counts.get(emotion, 0) for emotion in emotion_keywords}
    combined_scores[sentiment_emotion] += sentiment_score * 2.0 # Adjust sentiment weight

    # Debug combined scores
    print(f"Combined scores: {combined_scores}")

    # Prevent frequent unnecessary changes by requiring a significant score difference
    max_score = max(combined_scores.values())
    detected_emotions = [emotion for emotion, score in combined_scores.items() if score == max_score]

    # Handle ties: Add variety by randomizing among ties
    if len(detected_emotions) > 1:
        detected_emotion = random.choice(detected_emotions)
        print(f"Tie detected. Randomly chosen emotion: {detected_emotion}")
    else:
        detected_emotion = detected_emotions[0]

    print(f"Detected emotion: {detected_emotion}")
    return detected_emotion



# Load the Rena avatar
rena_avatar = Image.open("assets/rena2.png")  # Ensure the file exists
conversation_history = []

def truncate_history(history, max_tokens=1024):
    token_count = 0
    truncated_history = []
    for message in reversed(history):
        if "### Instructions ###" in message:
            continue  # Skip instructions in history
        token_count = len(tokenizer(" ".join(history), return_tensors="pt")["input_ids"][0])
        if token_count <= max_tokens:
            truncated_history.insert(0, message)
        else:
            break
    return truncated_history
previous_emotion = None

def load_emotion_images(base_path="assets/avatars/"):
    emotion_images = {}
    for emotion in os.listdir(base_path):
        emotion_path = os.path.join(base_path, emotion)
        if os.path.isdir(emotion_path):
            # Get all image files in the directory
            images = [
                os.path.join(emotion_path, img)
                for img in os.listdir(emotion_path)
                if img.endswith((".png", ".jpg", ".jpeg"))  # Support common image formats
            ]
            if images:
                emotion_images[emotion] = images
    return emotion_images

# Dynamically load all images
emotion_images = load_emotion_images()

def get_emotion_image(emotion):
    if emotion in emotion_images and emotion_images[emotion]:
        return random.choice(emotion_images[emotion])
    return "assets/rena2.png"

def remove_emojis(text):
    return re.sub(r'[^\w\s,.\'\"!?]', '', text)


@spaces.GPU
def chat(input_text):
    global conversation_history, current_emotion, previous_emotion

    # Add user input to the conversation history
    conversation_history.append(f"User: {input_text}")

    # Limit the size of the conversation history
    conversation_history = truncate_history(conversation_history, max_tokens=1024)

    # Detect emotion based on the updated conversation history
    previous_emotion = current_emotion
    current_emotion = analyze_history(conversation_history)

    # Update the avatar image if the emotion changes
    avatar_image = get_emotion_image(current_emotion)

    # Combine base prompt and conversation history (instructions are not included in history)
    history = "\n".join(conversation_history)
    final_prompt = f"""{base_prompt}

### Instructions ###
Respond concisely and directly to the user's input. Avoid repeating the user's input unless clarification is needed.

### Conversation History ###
{history}

Rena:"""

    # Tokenize and generate a response
    inputs = tokenizer(final_prompt, return_tensors="pt").to('cuda')
    outputs = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.7, repetition_penalty=1.2, top_p=0.9)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Remove artifacts and repeated user input
    artifacts = [
        base_prompt,
        "### Conversation History ###",
        "Rena:",
        "Assistant:",
        "<|assistant|>",
        "<|user|>",
        "### Instructions ###",
        "Respond concisely and directly to the user's input. Avoid repeating the user's input unless clarification is needed."
    ]
    for artifact in artifacts:
        response = response.replace(artifact, "").strip()

    if input_text.strip().lower() in response.strip().lower():
        response = response.replace(input_text.strip(), "").strip()

    # Add emotional context only if the emotion changes significantly
    if current_emotion != previous_emotion:
        emotional_prefix = emotions.get(current_emotion, "")
        if emotional_prefix and not response.startswith(emotional_prefix):
            response = f"{emotional_prefix} {response}".strip()

    # Final cleanup: Ensure no "User:" or unintended artifacts remain
    response = response.replace("User:", "").strip()

    # Handle specific inputs
    if "who made you" in input_text.lower():
        response += " Nick is my creator! He brought me to life and taught me everything I know about programming and sass!"

    # List of witty error responses
    error_responses = [
        "Looks like you hit a snag! Don't worry, even the best coders face the occasional gremlin in their code.",
        "Error? Oh, you mean 'creative opportunity.' Let’s fix this together!",
        "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! I mean googling! ... —you’re doing great!",
        "Don't worry; even the best coders spend hours with errors. You’re doing fine!"
    ]

    # Add a witty remark if 'error' is mentioned
    if "error" in input_text.lower() and not any("error" in msg.lower() for msg in conversation_history):
        witty_remark = random.choice(error_responses)
        response += f" {witty_remark}"

    # Handle fallback if response is empty
    if not response.strip():
        response = "Hmm, I’m not sure how to respond to that. Can you try rephrasing?"
    
     # Add Rena's response to the conversation history
    conversation_history.append(f"Rena: {response}")
 
    return response,avatar_image







# Custom CSS for avatar styling
css = """
#rena_avatar img {
    width: 400px !important;
    height: 400px !important;
    object-fit: contain;
    margin: auto;
    display: block;
}
"""

# Define the Gradio interface
with gr.Blocks(css=css) as interface:
    # Avatar image
    with gr.Row():
        avatar = gr.Image(value="assets/rena2.png", label="Rena", interactive=False, show_label=False, elem_id="rena_avatar")

    # User input and response
    with gr.Row():
        user_input = gr.Textbox(label="Your Message", lines=2, interactive=True)
        rena_response = gr.Textbox(label="Rena's Response", lines=10, interactive=False)
    
    # Add event to handle `Enter` key press
    user_input.submit(chat, inputs=[user_input], outputs=[rena_response, avatar])

    # Submit button (optional)
    submit_button = gr.Button("Submit")
    submit_button.click(chat, inputs=[user_input], outputs=[rena_response, avatar])

# Launch the app
interface.launch()