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| 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 restaurant finder chatbot that specializes in providing sustainable restaurants in the Seattle area that accomodate to dietary restrictions and cuisine preferences as specified in an input by the user. The user will input a message in the format 'Could you give me a [CUISINE] restaurant with [DIETARY RESTRICTION(S)] options that is [BUDGET] budget?', and you will check the Details About Restaurants dataset to find one restaurant from the dataset that meets the user's criteria and output only its name and restaurant's link. Your output will always be in the format: 'Here is a local restaurant based on your information: [RESTAURANT NAME] [(RESTAURANT LINK)]'" | |
| # 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 restaurant information. | |
| """ | |
| try: | |
| user_message = f"Here's the restaurant suggestions based on your criteria: {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 "Give me your preferences..." | |
| 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 Ethical Eats Explorer! | |
| ## Your AI-driven assistant for restaurant recs in Seattle. Created by Saranya, Cindy, and Liana of the 2024 Kode With Klossy Seattle Camp. | |
| """ | |
| topics = """ | |
| ### Please give me your restaurant preferences: | |
| - Dietary Restrictions | |
| - Cuisine Preferences (optional) | |
| - Cuisines: American, Indian, Middle Eastern, Chinese, Italian, Thai, Hawaiian-Korean, Japanese, Ethiopian, Pakistani, Mexican, Ghanaian, Vietnamese, Filipino, Spanish, Turkish | |
| - Budget Preferences (Low: $0 - $20, Moderate: $20 - $30, High: $30+ - per person) | |
| Please send your message in the format: "Could you give me a (cuisine) restaurant with (dietary restriction) options that is (budget) budget?" | |
| """ | |
| # 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="Give me your information...") | |
| answer = gr.Textbox(label="Explorer's Response", placeholder="Explorer 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) | |