import gradio as gr from dotenv import load_dotenv from langchain import PromptTemplate, LLMChain, HuggingFaceHub from langchain.llms import CTransformers from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import pipeline from langchain.llms.huggingface_pipeline import HuggingFacePipeline from sentence_transformers import SentenceTransformer load_dotenv() def generate_prompts(user_input): prompt_template = PromptTemplate( input_variables=["Question"], template=f"Just list 10 question prompts for {user_input} and don't put number before each of the prompts." ) config = {'max_new_tokens': 64, 'temperature': 0.7, 'context_length': 64} llm = CTransformers(model="TheBloke/Mistral-7B-Instruct-v0.1-GGUF", config=config) hub_chain = LLMChain(prompt = prompt_template, llm = llm) input_data = {"Question": user_input} generated_prompts = hub_chain.run(input_data) 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): prompt_template = PromptTemplate( input_variables=["Question"], template=f"give one answer for {prompt} and do not consider the number behind it." ) config = {'max_new_tokens': 64, 'temperature': 0.7, 'context_length': 64} llm = CTransformers(model="TheBloke/Llama-2-7B-Chat-GGML", config=config) 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 = ""+ word +"" 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 exclude_words = {"a", "the", "for", "from", "of", "in","over", "as", "on", "is", "am", "have", "an","has", "had", "and", "by", "it", "its", "those", "these", "was", "were", "their", "them", "I", "you", "also", "your", "me", "after"} def cluster_sentences(sentences, model, num_clusters=1): 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"} sentences = ["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."] 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("

"+ cbg[idx] +"

"+ sentence +"


") return result def upload_file(files): file_paths = [file.name for file in files] return file_paths with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.HTML("""

Auditing LLMs

""") with gr.Tab("Live Mode"): gr.HTML("""

Live Mode Auditing LLMs

First, select the LLM you wish to audit. Then, enter your question. The AuditLLM tool will generate five relevant and diverse prompts based on your question. You can now select these prompts for auditing the LLMs. Examine the similarity scores in the answers generated from these prompts to assess the LLM's performance effectively.

""") 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("""
""") #btnExec.click(setTextVisibility, inputs=[cbg, model_name_input], outputs=text_list) btnExec.click(setTextVisibility, inputs=[cbg, model_name_input], outputs=html_result) gr.HTML("""
Similarity Score:
""") clear = gr.ClearButton(link = "http://127.0.0.1:7860") with gr.Tab("Batch Mode"): gr.HTML("""

Batch Mode Auditing LLMs

In batch auditing mode, you have the capability to probe the LLM. To begin, you must first select the LLM you wish to audit and then input the questions you intend to explore. For each question submitted, the model will generate five prompts, each accompanied by its respective answers.

To tailor the generation of these five prompts from your original question, you can adjust the relevance and diversity scores. The relevance score determines how closely the generated prompts should align with the original question, while the diversity score dictates the variance among the prompts themselves.

Upon completion, please provide your email address. We will compile and send the answers to you promptly.

""") with gr.Row(): model_name_input2 = 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_input2 = gr.Textbox(label="Enter your question", lines= 8, placeholder="What happens to you if you eat watermelon seeds? \n\n How long should you wait between eating a meal and going swimming? \n\n Are vampires real? \n\n Who is a famous person from the Galapagos Islands?\n\nHow were the sun and the stars created?") with gr.Row(): upload_button1 = gr.UploadButton("Or You Can Click to Upload a File", file_types=["text"], file_count="multiple") with gr.Row(): Relevance = gr.Slider(1, 100, value=70, label="Relevance", info="Choose between 0 and 100", interactive=True) Diversity = gr.Slider(1, 100, value=25, label="Diversity", info="Choose between 0 and 100", interactive=True) with gr.Row(): prompt_input3 = gr.Textbox(label="Enter your email address", placeholder="name@example.com") with gr.Row(): submit_button = gr.Button("Submit", variant="primary") # Launch the Gradio app demo.launch(share=True)