Raiff1982/coder

Model Card for {{ model_id | default("Model ID", true) }}

A multimodel assistant

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Model Details

Model Description

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• Developed by: {{ developers | default("[More Information Needed]", true)}}
• Funded by [optional]: {{ funded_by | default("[More Information Needed]", true)}}
• Shared by [optional]: {{ shared_by | default("[More Information Needed]", true)}}
• Model type: {{ model_type | default("[More Information Needed]", true)}}
• Language(s) (NLP): {{ language | default("[More Information Needed]", true)}}
• License: {{ license | default("[More Information Needed]", true)}}
• Finetuned from model [optional]: {{ base_model | default("[More Information Needed]", true)}}

Model Sources [optional]

• Repository: {{ repo | default("[More Information Needed]", true)}}
• Paper [optional]: {{ paper | default("[More Information Needed]", true)}}
• Demo [optional]: {{ demo | default("[More Information Needed]", true)}}

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}

How to Get Started with the Model

Use the code below to get started with the model.

import subprocess
import sys
import os

# Function to install a package
def install(package):
    subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# List of required packages
required_packages = ["transformers", "torch", "vaderSentiment", "nltk", "fastai", "fastcore", "toml"]

# Install required packages
for package in required_packages:
    install(package)

# Now we can safely import the installed packages
import gc
from transformers import pipeline
import gradio as gr
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt', quiet=True)

# Define the UltimateReasoningSystem class
class UltimateReasoningSystem:
    def __init__(self):
        self.knowledgeGraph = KnowledgeGraph()
        self.perspectiveManager = PerspectiveManager()
        self.emotionManager = EmotionManager()
        self.dataSources = [WikipediaDataSource(), RedditDataSource()]

    def generateResponse(self, question):
        questionAnalysis = self.analyzeQuestion(question)
        perspective = self.perspectiveManager.determinePerspective(questionAnalysis)
        emotion = self.emotionManager.determineEmotion(questionAnalysis)
        response = self.generateResponseUsingPerspectiveAndEmotion(perspective, emotion, questionAnalysis)
        return response

    def analyzeQuestion(self, question):
        tokens = self.tokenizeQuestion(question)
        posTags = self.posTagTokens(tokens)
        namedEntities = self.namedEntityRecognizeTokens(tokens)
        dependencyParse = self.dependencyParseTokens(tokens)
        return {
            'tokens': tokens,
            'posTags': posTags,
            'namedEntities': namedEntities,
            'dependencyParse': dependencyParse,
        }

    def tokenizeQuestion(self, question):
        tokenizer = Tokenizer()
        tokens = tokenizer.tokenize(question)
        return tokens

    def posTagTokens(self, tokens):
        posTagger = PosTagger()
        posTags = posTagger.tag(tokens)
        return posTags

    def namedEntityRecognizeTokens(self, tokens):
        namedEntityRecognizer = NamedEntityRecognizer()
        namedEntities = namedEntityRecognizer.recognize(tokens)
        return namedEntities

    def dependencyParseTokens(self, tokens):
        dependencyParser = DependencyParser()
        dependencyParse = dependencyParser.parse(tokens)
        return dependencyParse

    def generateResponseUsingPerspectiveAndEmotion(self, perspective, emotion, questionAnalysis):
        response = self.generateResponseUsingPerspectiveAndEmotionHelper(perspective, emotion, questionAnalysis)
        return response

    def generateResponseUsingPerspectiveAndEmotionHelper(self, perspective, emotion, questionAnalysis):
        knowledgeGraph = self.knowledgeGraph
        response = knowledgeGraph.generateResponse(perspective, emotion, questionAnalysis)
        return response

class KnowledgeGraph:
    def __init__(self):
        self.graph = Graph()

    def generateResponse(self, perspective, emotion, questionAnalysis):
        response = self.graph.generateResponse(perspective, emotion, questionAnalysis)
        return response

class Graph:
    def __init__(self):
        self.nodes = []
        self.edges = []

    def generateResponse(self, perspective, emotion, questionAnalysis):
        response = self.generateResponseHelper(perspective, emotion, questionAnalysis)
        return response

    def generateResponseHelper(self, perspective, emotion, questionAnalysis):
        response = self.generateResponseHelperHelper(perspective, emotion, questionAnalysis)
        return response

    def generateResponseHelperHelper(self, perspective, emotion, questionAnalysis):
        response = 'This is a response.'
        return response

class PerspectiveManager:
    def __init__(self):
        self.perspectives = [NewtonianPerspective(), DaVincianPerspective()]

    def determinePerspective(self, questionAnalysis):
        perspective = self.perspectives[0]
        return perspective

class EmotionManager:
    def __init__(self):
        self.emotions = [HappyEmotion(), SadEmotion()]

    def determineEmotion(self, questionAnalysis):
        emotion = self.emotions[0]
        return emotion

class NewtonianPerspective:
    def getPerspectiveDescription(self):
        perspectiveDescription = 'This perspective is Newtonian.'
        return perspectiveDescription

class DaVincianPerspective:
    def getPerspectiveDescription(self):
        perspectiveDescription = 'This perspective is DaVincian.'
        return perspectiveDescription

class HappyEmotion:
    def getEmotionDescription(self):
        emotionDescription = 'This emotion is happy.'
        return emotionDescription

class SadEmotion:
    def getEmotionDescription(self):
        emotionDescription = 'This emotion is sad.'
        return emotionDescription

class WikipediaDataSource:
    def getDataDescription(self):
        dataDescription = 'This data is from Wikipedia.'
        return dataDescription

class RedditDataSource:
    def getDataDescription(self):
        dataDescription = 'This data is from Reddit.'
        return dataDescription

class Tokenizer:
    def tokenize(self, text):
        return text.split()

class PosTagger:
    def tag(self, tokens):
        return [('NN', token) for token in tokens]

class NamedEntityRecognizer:
    def recognize(self, tokens):
        return ['ENTITY' for _ in tokens]

class DependencyParser:
    def parse(self, tokens):
        return [('ROOT', token) for token in tokens]

# Initialize the text-generation pipeline with a smaller model to reduce memory usage
pipe = pipeline("text-generation", model="distilgpt2", device=-1)

def generate_text(prompt):
    # Use the pipeline to generate text with adjusted parameters
    results = pipe(prompt, max_length=50, num_return_sequences=1, temperature=0.9, top_p=0.9)
    # Extract the generated text
    generated_text = results[0]['generated_text']
    
    # Check for repetitive output and regenerate if necessary
    if "Ethical Considerations" in generated_text:
        results = pipe(prompt, max_length=50, num_return_sequences=1, temperature=1.0, top_p=0.8)
        generated_text = results[0]['generated_text']
    
    return generated_text

async def respond(
    message,
    history: list[dict],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    ultimate_reasoning_system = UltimateReasoningSystem()
    response = ultimate_reasoning_system.generateResponse(message)

    # Clear unused variables to free up memory
    gc.collect()

    yield response

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly, professional, and knowledgeable chatbot who enjoys casual conversations and offers empathetic, supportive responses.", 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"),  # Fixed comma
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
    type="messages"  # Update this line to use the 'messages' format
)

if __name__ == "__main__":
    demo.launch()