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_topic_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"] system_message = "You are a eco-friendly travel chatbot specialized in providing information on eco-friendly restaurants, hotels, and attractions in NYC, you can also provide the user with environment-themed pickup lines." # 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 eco-friendly travel information. """ try: user_message = f"Here's the information on eco-friendly travel information: {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}) 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 GreenGuide! Ask me anything about eco-friendly hotels, restaurants, and things to do in NYC." 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 HTML iframe content iframe = ''' ''' # Define the welcome message and specific topics the chatbot can provide information about welcome_message = """ # 🌱 Welcome to GreenGuide! ## Your AI-driven assistant for all eco-friendly travel-related queries in NYC. Created by Eva, Amy, and Ambur of the 2024 Kode With Klossy NYC AI/ML Camp. """ topics = """ ### Feel free to ask me anything things to do in the city! - Hotels (affordable, luxury) - Restaurants (regular, vegetarian, vegan) - Parks & Gardens - Thrift Stores - Attractions """ # Create a Gradio HTML component def display_iframe(): return iframe def display_image(): return "https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExZzdqMnkzcWpjbGhmM3hzcXp0MGpuaTF5djR4bjBxM3Biam5zbzNnMCZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9cw/GxMnTi3hV3qaIgbgQL/giphy.gif" #return "https://cdn-uploads.huggingface.co/production/uploads/6668622b72b61ba78fe7d4bb/PkWjNxvGm9MOqGkZdiT4e.png" theme = gr.themes.Monochrome( primary_hue="amber", #okay this did NOT work lmaoo secondary_hue="rose", ).set( background_fill_primary='#CBE9A2', # BACKGROUND background_fill_primary_dark='#768550', background_fill_secondary='#768550', # BUTTON HOVER background_fill_secondary_dark='#99a381', #LOADING BAR border_color_accent='#768550', border_color_accent_dark='#768550', border_color_accent_subdued='#768550', border_color_primary='#03a9f4', block_border_color='#b3e5fc', button_primary_background_fill='#768550', button_primary_background_fill_dark='#768550' ) # Setup the Gradio Blocks interface with custom layout components with gr.Blocks(theme=theme) as demo: gr.Image("header2.png", show_label = False, show_share_button = False, show_download_button = False) #CHANGE !! 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="GreenGuide Response", placeholder="GreenGuide will respond here...", interactive=False, lines=10) submit_button = gr.Button("Submit") submit_button.click(fn=query_model, inputs=question, outputs=answer) gr.HTML(iframe) # Launch the Gradio app to allow user interaction demo.launch(share=True)