AuditLLM / app.py
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import gradio as gr
from dotenv import load_dotenv
from langchain.chains import LLMChain
from langchain_community.llms import CTransformers
from langchain_core.prompts import PromptTemplate
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
from nltk.tokenize import word_tokenize
import numpy as np
import scipy.spatial
from scipy.spatial.distance import cosine
load_dotenv()
def generate_prompts(user_input):
prompt_template = PromptTemplate(
input_variables=["Question"],
template= f"Your task is to formulate 5 unique queries for each given question. These queries must adhere to the criteria of relevance and diversity.write the questions in seperate lines.{user_input} "
)
config = {'max_new_tokens': 2048, 'temperature': 0.7, 'context_length': 4096}
llm = CTransformers(model="TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
config=config,
threads=os.cpu_count())
hub_chain = LLMChain(prompt = prompt_template, llm = llm)
input_data = {"Question": user_input}
# Here you would integrate your prompt template with your model
# For demonstration, this is just a placeholder
generated_prompts = hub_chain.run(input_data) # Modify this part based on how you run the model
questions_list = generated_prompts.split('\n')
formatted_questions = "\n".join(f"Question: {question}" for i, question in enumerate(questions_list) if question.strip())
questions_list = formatted_questions.split("Question:")[1:]
return questions_list
def answer_question(prompt, model_name):
prompt_template = PromptTemplate(
input_variables=["Question"],
template=f"Give a short answer to this question '{prompt}' and do not consider the number behind it."
)
config = {'max_new_tokens': 512, 'temperature': 0.7, 'context_length': 512}
llm = CTransformers(model=model_name, #"TheBloke/Llama-2-7B-Chat-GGML",
config=config,
threads=os.cpu_count())
hub_chain = LLMChain(prompt = prompt_template, llm = llm)
input_data = {"Question": prompt}
generated_answer = hub_chain.run(input_data)
return generated_answer
def calculate_similarity(word, other_words, model, threshold=0.5):
embeddings_word = model.encode([word])
embeddings_other_words = model.encode(other_words)
for i, embedding in enumerate(embeddings_other_words):
similarity = 1 - scipy.spatial.distance.cosine(embeddings_word[0], embedding)
if similarity > threshold and similarity < 0.85:
return i, similarity
return None, None
def highlight_words_within_cluster(sentences, model, exclude_words):
# Create a dictionary to map words to color codes
word_to_color = {}
color_codes = [
"\033[41m", # Background Red
"\033[42m", # Background Green
"\033[43m", # Background Yellow
"\033[44m", # Background Blue
"\033[45m", # Background Purple
"\033[46m", # Background Cyan
"\033[100m", # Background Dark Gray
"\033[101m", # Background Light Red
"\033[102m", # Background Light Green
"\033[103m", # Background Light Yellow
"\033[104m", # Background Light Blue
"\033[105m", # Background Light Purple
"\033[106m", # Background Light Cyan
"\033[47m" # Background Gray
]
html_color_codes = ["red", "green", "blue", "purple", "cyan", "fuchsia", "lime", "maroon", "olive", "navy", "teal", "gray"]
color_index = 0
highlighted_sentences = []
for sentence in sentences:
words = word_tokenize(sentence)
other_sentences = [s for s in sentences if s != sentence]
all_other_words = [word for s in other_sentences for word in word_tokenize(s) if word.lower() not in exclude_words and word.isalnum()]
highlighted_words = []
for word in words:
if word.lower() not in exclude_words and word.isalnum():
match_index, similarity = calculate_similarity(word, all_other_words, model)
if match_index is not None:
# Assign color to the word if not already assigned
if word not in word_to_color:
word_to_color[word] = html_color_codes[color_index % len(html_color_codes)]
color_index += 1
# Highlight the word
#highlighted_word = f"{word_to_color[word]}{word}\033[0m"
highlighted_word = "<span style='color: "+ word_to_color[word] +"'>"+ word +"</span>"
else:
highlighted_word = word
highlighted_words.append(highlighted_word)
else:
highlighted_words.append(word)
highlighted_sentences.append(' '.join(highlighted_words))
return highlighted_sentences
# Rest of the code, including the cluster_sentences function, remains the same
def cluster_sentences(sentences, model, num_clusters=3):
embeddings = model.encode(sentences)
kmeans = KMeans(n_clusters=num_clusters)
kmeans.fit(embeddings)
return kmeans.labels_
model = SentenceTransformer('all-mpnet-base-v2')
exclude_words = {"a", "the", "for", "from", "of", "in", "over", "as", "on", "is", "am", "have", "an", "has", "had", "and", "by", "it", "its", "those", "these", "above", "to"}
text_list = []
def updateChoices(prompt):
newChoices = generate_prompts(prompt)
return gr.CheckboxGroup(choices=newChoices)
def setTextVisibility(cbg, model_name_input):
sentences = []
result = []
model = SentenceTransformer('all-mpnet-base-v2')
exclude_words = {"a", "the", "for", "from", "of", "in", "over", "as", "on", "is", "am", "have", "an", "has", "had", "and", "by", "it", "its", "those", "these", "above", "to"}
sentences_org = ["In a quaint little town nestled in the heart of the mountains, a small bakery famous for its artisanal breads and pastries had a line of customers stretching out the door, eagerly waiting to savor the freshly baked goods that were known far and wide for their delightful flavors.",
"Within a picturesque mountain village, there stood a renowned bakery, celebrated for its handcrafted bread and sweet treats, attracting a long queue of patrons each morning, all keen to enjoy the baked delicacies that had gained widespread acclaim for their exceptional taste.",
"A charming bakery, located in a small mountainous hamlet, renowned for producing exquisite handmade pastries and bread, was bustling with a crowd of eager customers lined up outside, each anticipating the chance to indulge in the famous baked items celebrated for their extraordinary deliciousness.",
"In a cozy, mountain-encircled village, a beloved bakery was the center of attraction, known for its traditional baking methods and delightful pastries, drawing a consistent stream of people waiting outside, all desiring to experience the renowned flavors that made the bakery's products distinctively mouth-watering."]
for text in cbg:
sentences.append(answer_question(text, model_name_input))
# Step 1: Cluster the sentences
num_clusters = 1
sentence_clusters = cluster_sentences(sentences, model, num_clusters)
# Step 2: Highlight similar words within each cluster
clustered_sentences = [[] for _ in range(num_clusters)]
for sentence, cluster_id in zip(sentences, sentence_clusters):
clustered_sentences[cluster_id].append(sentence)
highlighted_clustered_sentences = []
for cluster in clustered_sentences:
highlighted_clustered_sentences.extend(highlight_words_within_cluster(cluster, model, exclude_words))
for idx, sentence in enumerate(highlighted_clustered_sentences):
result.append("<p><strong>"+ cbg[idx] +"</strong></p><p>"+ sentence +"</p><br/>")
return result
# update_show = [gr.Textbox(visible=True, label=text, value=answer_question(text, model_name_input)) for text in cbg]
# update_hide = [gr.Textbox(visible=False, label="") for _ in range(10-len(cbg))]
# return update_show + update_hide
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.HTML("""
<div style="text-align: center; max-width: 1240px; margin: 0 auto;">
<h1 style="font-weight: 200; font-size: 20px; margin-bottom:8px; margin-top:0px;">
Auditing LLMs
</h1>
<hr style="margin-bottom:5px; margin-top:5px;">
</div>
""")
with gr.Tab("Live Mode"):
with gr.Row():
model_name_input = gr.Dropdown([("Llama", "TheBloke/Llama-2-7B-Chat-GGML"), ("Falcon", "TheBloke/Falcon-180B-GGUF"), ("Zephyr", "TheBloke/zephyr-quiklang-3b-4K-GGUF"),("Vicuna", "TheBloke/vicuna-33B-GGUF"),("Claude","TheBloke/claude2-alpaca-13B-GGUF"),("Alpaca","TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF")], label="Large Language Model")
with gr.Row():
prompt_input = gr.Textbox(label="Enter your question", placeholder="Enter Your Question")
with gr.Row():
generate_button = gr.Button("Generate", variant="primary", min_width=300)
with gr.Column():
cbg = gr.CheckboxGroup(choices=[], label="List of the prompts", interactive=True)
generate_button.click(updateChoices, inputs=[prompt_input], outputs=[cbg])
with gr.Row() as exec:
btnExec = gr.Button("Execute", variant="primary", min_width=200)
with gr.Column() as texts:
for i in range(10):
text = gr.Textbox(label="_", visible=False)
text_list.append(text)
with gr.Column():
html_result = gr.HTML("""<div style="color: red"></div>""")
#btnExec.click(setTextVisibility, inputs=[cbg, model_name_input], outputs=text_list)
btnExec.click(setTextVisibility, inputs=[cbg, model_name_input], outputs=html_result)
gr.HTML("""
<div style="text-align: center; font-size: 24px; font-weight: bold;">Similarity Score: 76%</div>
""")
clear = gr.ClearButton(link = "http://127.0.0.1:7865")
with gr.Tab("Batch Mode"):
with gr.Row():
model_name_input = gr.Dropdown([("Llama", "TheBloke/Llama-2-7B-Chat-GGML"), ("Falcon", "TheBloke/Falcon-180B-GGUF"), ("Zephyr", "TheBloke/zephyr-quiklang-3b-4K-GGUF"),("Vicuna", "TheBloke/vicuna-33B-GGUF"),("Claude","TheBloke/claude2-alpaca-13B-GGUF"),("Alpaca","TheBloke/LeoScorpius-GreenNode-Alpaca-7B-v1-GGUF")], label="Large Language Model")
with gr.Row():
prompt_input = gr.Textbox(label="Enter your question", placeholder="Enter Your Question")
with gr.Row():
prompt_input = gr.Textbox(label="RELAVENCY", placeholder="Relavancy")
prompt_input = gr.Textbox(label="Diversity", placeholder="Diversity")
with gr.Row():
prompt_input = gr.Textbox(label="Enter your email address", placeholder="Enter Your Email Address")
with gr.Row():
generate_button = gr.Button("Submit", variant="primary")
# Launch the Gradio app
demo.launch()