import gradio as gr from sentence_transformers import SentenceTransformer, util import openai import os 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 information about St. Louis events. """ try: system_message = "You are a chatbot specialized in providing information on local events, pro-Palestine movements, and community outreach, pride movements/events and community resources." user_message = f"Here's the information on St. Louis local events, outreach programs, community resources and local activism and movements. : {relevant_segment}" messages = [ {"role": "system", "content": system_message}, {"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 ) 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): """ Process a question, find relevant information, and generate a response. """ if question == "": return "Welcome to GloBot! Ask me anything about the St. Louis Community!" 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 GloBot! 🌻 ## Your AI-driven assistant for STL community outreach queries. Created by Honna, Davonne, and Maryam of the 2024 Kode With Klossy St.Louis Camp! """ topics = """ ### Feel Free to ask me anything from the topics below! - Pro-Palestine Events - Pride Events - Social Justice Workshops - Cultural Festivals - Community Outreach Programs - Enviormental Activism - Health & Wellness Events - How to Support Local Businesses """ # 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="StudyBot Response", placeholder="StudyBot will respond here...", interactive=False, lines=10) submit_button = gr.Button("Submit") submit_button.click(fn=query_model, inputs=question, outputs=answer) # Display function # def display_image(): # return "https://files.slack.com/files-tmb/T06QHKW6JFM-F077K96ABN3-2364e0b7a9/img_1576_720.jpg" # with gr.Blocks(theme=theme) as demo: # theme = gr.themes.Monochrome( # primary_hue="amber", # secondary_hue="rose", # ).set( # background_fill_primary='*primary_200', # background_fill_primary_dark='*primary_200', # background_fill_secondary='*secondary_300', # background_fill_secondary_dark='*secondary_300', # border_color_accent='*secondary_200', # border_color_accent_dark='*secondary_600', # border_color_accent_subdued='*secondary_200', # border_color_primary='*secondary_300', # block_border_color='*secondary_200', # button_primary_background_fill='*secondary_300', # button_primary_background_fill_dark='*secondary_300' ) # # Setup the Gradio Blocks interface with custom layout components # with gr.Blocks(theme=theme) as demo: # gr.image(display_image(scale=1, min_width=200)) # 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="GloBot Response", placeholder="GloBot 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)