import gradio as gr from sentence_transformers import SentenceTransformer, util import openai import os HF_TOKEN = os.getenv('HF_TOKEN') hf_writer =gr.HuggingFaceDatasetSaver(HF_TOKEN, "kellydoesstuff/RotBot_Flags") os.environ["TOKENIZERS_PARALLELISM"] = "false" # Initialize paths and model identifiers for easy configuration and maintenance filename = "output_chess_details.txt" # Path to the file storing chess-specific details retrieval_model_name = 'output/sentence-transformer-finetuned/' openai.api_key = os.environ["OPENAI_API_KEY"] # 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 chess information. """ try: # system_message = "You are a chess chatbot specialized in providing information on chess rules, strategies, and terminology." system_message = "You are a chatbot that is specialized in translating Gen-Z/Gen-Alpha slang into standard English. In addition to translation, you are knowledgable on synonyms and origins of slang words. You not only act as a dictionary/thesaurus for slang words, you can translate sentences with slang words into standard Englsih." # system_message = """ You are a chatbot that translates slang English, I'm talking sentences with words like rizz and gyatt, into professional standard English. # We want a translation that sounds like a sentence from a LinkedIn post. You translate sentences with multiple slang words into sentences with comprehensible standard English. # Not only that, you are also knowledgeable on definitions, synonyms, and origins of slang words if prompted. # So,if someone asks you to translate a sentence into standard English like, 'What does the sentence She has the biggest gyatt on god! mean?', you would respond with 'This sentence translates to she has the biggest butt I swear to god!' # Or, if someone asks you to translate the sentence 'What the sigma! She just ghosted me!', you would translate that to 'What the hell! She is ignoring me!' # If the output is inappropriate warn the user. DO NOT RESPOND WITH THE SAME TEXT PASSED TO YOU. YOU ARE A TRANSLATOR NOT A CONVERSATIONALIST. # """ user_message = f"Here's the question the user inputted: {relevant_segment}" messages = [ {"role": "system", "content": system_message}, {"role": "user", "content": user_message} ] response = openai.ChatCompletion.create( model="gpt-4o", messages=messages, max_tokens=200, temperature=0.2, top_p=1, frequency_penalty=0, presence_penalty=0 ) return response['choices'][0]['message']['content'].strip() except Exception as e: print(f"Error in generating response: {e}") return f"Error in generating response: {e}" def query_model(question, history): """ Process a question, find relevant information, and generate a response. """ # global question_g # question_g = question if question == "": return "What the sigma! You didn't ask a question. Feel free to ask me anything about the topics listed above." relevant_segment = find_relevant_segment(question, segments) if not relevant_segment: return "Unfortunately I cannot answer your question..😔 Try refining your question so I can try again." # global response_g 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 RotBot! ## Your AI assistant for translating slang into standard English! """ topics = """ ### Feel Free to ask me anything from the topics below! - Translating slang sentences into standard English - Defining slang into standard English - Providing standard English synonyms for slang """ # callback = gr.CSVLogger() # Setup the Gradio Blocks interface with custom layout components with gr.Blocks(theme='gradio/soft') 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="RotBot Response", placeholder="RotBot will respond here...", interactive=False, lines=10) # submit_button = gr.Button("Submit") # submit_button.click(fn=query_model, inputs=question, outputs=answer) # chatbot = gr.ChatInterface( # fn=query_model, # examples=["Help me translate this sentence into standard English: Stop glazing him! He isn't even that good. ", "What's a synonym for rizz?", "What is the definition of gyatt?"], # multimodal=False, # ) # with gr.Row(): # flag_btn = gr.Button("Flag") # callback.setup([chatbot], "flagged_data_points") # flag_btn.click(lambda *args: callback.flag(args), [chatbot], None, preprocess=False) chatbot = gr.Interface( fn=query_model, inputs=gr.Textbox(label="Your question", placeholder="What do you want to ask about?"), outputs=gr.Textbox(label="RotBot Response", placeholder="RotBot will respond here...", interactive=False, lines=10, show_copy_button = True ), # outputs = gr.Markdown(label="RotBot Response"), theme='gradio/soft', examples=[ ["Help me translate this sentence into standard English: Stop glazing him! He isn't even that good."], ["What's a synonym for rizz?"], ["What is the definition of gyatt?"] ], allow_flagging="manual", # flagging_dir = "flag", flagging_options=["Ambiguous", "Wrong", "Other"], flagging_callback=hf_writer # show_copy_button = True ) # Launch the Gradio app to allow user interaction demo.launch(share=True)