import gradio as gr from sentence_transformers import SentenceTransformer, util import openai import os import random os.environ["TOKENIZERS_PARALLELISM"] = "false" # Initialize paths and model identifiers for easy configuration and maintenance filename = "output_topic_details.txt" # Path to the file storing AI-specific details retrieval_model_name = 'output/sentence-transformer-finetuned/' openai.api_key = os.environ["OPENAI_API_KEY"] system_message = "You are an AI chatbot specialized in providing information on AI usage, helpful tools, and teaching users about AI." # Initial system message to set the behavior of the assistant messages = [{"role": "system", "content": system_message}] # Attempt to load the necessary models and provide feedback on success or failure try: retrieval_model = SentenceTransformer(retrieval_model_name) print("Models loaded successfully.") except Exception as e: print(f"Failed to load models: {e}") def load_and_preprocess_text(filename): """ Load and preprocess text from a file, removing empty lines and stripping whitespace. """ try: with open(filename, 'r', encoding='utf-8') as file: segments = [line.strip() for line in file if line.strip()] print("Text loaded and preprocessed successfully.") return segments except Exception as e: print(f"Failed to load or preprocess text: {e}") return [] segments = load_and_preprocess_text(filename) def find_relevant_segment(user_query, segments): """ Find the most relevant text segment for a user's query using cosine similarity among sentence embeddings. This version finds the best match based on the content of the query. """ try: # Lowercase the query for better matching lower_query = user_query.lower() # Encode the query and the segments query_embedding = retrieval_model.encode(lower_query) segment_embeddings = retrieval_model.encode(segments) # Compute cosine similarities between the query and the segments similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0] # Find the index of the most similar segment best_idx = similarities.argmax() # Return the most relevant segment return segments[best_idx] except Exception as e: print(f"Error in finding relevant segment: {e}") return "" def generate_response(user_query, relevant_segment): """ Generate a response emphasizing the bot's capability in providing AI information. """ try: user_message = f"Here's the information on AI: {relevant_segment}" # Append user's message to messages list messages.append({"role": "user", "content": user_message}) response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages, max_tokens=150, temperature=0.2, top_p=1, frequency_penalty=0, presence_penalty=0 ) # Extract the response text output_text = response['choices'][0]['message']['content'].strip() # Append assistant's message to messages list for context messages.append({"role": "assistant", "content": output_text}) fun_int=random.randint(0,11) fun_facts=["Young Einstein didn't talk until much later in his childhood.","Einstein had larger-than-average perietal lobes.","Einstein was a talented violinist","Einstein's brain was preserved after his death!","Einstein started as a teacher, but couldn't find a job.","Einstein's famous equation E=mc² was announced in 1905.","Einstein won The Nobel Prize in Physics in 1921","Einstien did not wear socks!","Einstein loved sailing.",'Einstein once said -"If you can not explain it simply, you don not understand it well enough."','Einstein once said- "Logic will get you from A to B. Imagination will get you anywhere."'] output_text=output_text+"\n\n Here is a fun fact about Albert Einstein!: " + fun_facts[fun_int-1] ai_int=random.randint(0,10) ai_helpers=["https://chatgpt.com/ - An AI chatbot","https://www.grammarly.com/ - Help with grammar and writing!","https://www.any.do/ - Creates a to do list to help you get your tasks completed!","https://scheduler.ai/- AI optimizes your schedule and works around pre-scheduled deadlines","ChatGPT Data Analyst - Helps you visualize and analize your data","ChatGPT Logo creator - Helps to create professional logos for companies or brands","ScholarGPT - Enhances your reaserch capabilities","ChatGPT's Math solver","Tutor Me by Khan Academy","Travel Guide by capchair - helps find destinations, plan trips, and manage budgets"] output_text=output_text+"\n\n Here is a helpful chatbot tool for you!: "+ ai_helpers[ai_int-1] return output_text except Exception as e: print(f"Error in generating response: {e}") return f"Error in generating response: {e}" def query_model(question): """ Process a question, find relevant information, and generate a response. """ if question == "": return "Welcome to AI-nstein! Ask me anything about AI ML, and helpful tools you may want to use!" relevant_segment = find_relevant_segment(question, segments) if not relevant_segment: return "Could not find specific information. Please refine your question." response = generate_response(question, relevant_segment) return response # Define the welcome message and specific topics the chatbot can provide information about welcome_message = """ # Welcome to AI-nstein! ## Your AI-driven assistant for all artificial intelligence-related queries. Created by Sophie Cheng, Ariel Datikash, and K Barnes of the 2024 Kode With Klossy CITY Camp. """ topics = """ ### Feel Free to ask me anything from the topics below! - AI Usage - AI Safety - AI Helpers - How AI Works - Basics of AI - Fun Facts about AI - Examples of AI """ # Setup the Gradio Blocks interface with custom layout components with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo: gr.Markdown(welcome_message) # Display the formatted welcome message with gr.Row(): with gr.Column(): gr.Markdown(topics) # Show the topics on the left side with gr.Row(): with gr.Column(): question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?") answer = gr.Textbox(label="AI-nstein Response", placeholder="AI-nstein will respond here...", interactive=False, lines=10) submit_button = gr.Button("Submit") submit_button.click(fn=query_model, inputs=question, outputs=answer) # Launch the Gradio app to allow user interaction demo.launch(share=True)