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| import gradio as gr | |
| from sentence_transformers import SentenceTransformer, util | |
| import webbrowser | |
| 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 recipe-specific details | |
| retrieval_model_name = 'output/sentence-transformer-finetuned/' | |
| openai.api_key = os.environ["OPENAI_API_KEY"] | |
| system_message = "You are a meal chatbot specialized in providing information on meals, recipes, and ingredients." | |
| # 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 sustainable recipe information. | |
| """ | |
| try: | |
| user_message = f"Here's the information on the recipe: {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=500, | |
| 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 SustAIBot! Ask me anything about recipes with mushrooms, carrots, kale, and tofu as the main ingredients." | |
| 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 SustAIna-bot! | |
| ## Your AI-driven assistant for meat, veggie, and plant-based sustainable recipe-related queries. Created by Cecilia, Halle, and Elena of the Kode With Klossy Camp. | |
| """ | |
| topics = """ | |
| ### Feel Free to ask me anything from the topics below! | |
| - Mushroom Recipes | |
| - Carrot Recipes | |
| - Kale Recipes | |
| - Tofu Recipes | |
| - Lentils Recipes | |
| - Chickpea Reicpes | |
| - Fish Recipes | |
| - Chicken Recipes | |
| - Beef Recipes | |
| - Pork Recipes | |
| """ | |
| def display_image(): | |
| return "https://huggingface.co/spaces/Sustainable-Meal-Assistant/TreeBot/resolve/main/sustainable-food-principles%C2%A9iStock-552584505.jpg" | |
| theme = gr.themes.Base().set( | |
| background_fill_primary='#C1D0B5', # Light green background | |
| background_fill_primary_dark='#737373', # Dark green background | |
| background_fill_secondary='#FFF8DE', # Light off white background | |
| background_fill_secondary_dark='#99A98F', # Dark green background | |
| border_color_accent='#FFF8DE', # Accent border color | |
| border_color_accent_dark='#3C8181', # Dark accent border color | |
| border_color_accent_subdued='#FF8A65', # Subdued accent border color | |
| border_color_primary='#737373', # Primary border color | |
| block_border_color='##3C8181', # Block border color | |
| button_primary_background_fill='#FF9800', # Primary button background color | |
| button_primary_background_fill_dark='#EF6C00' # Dark primary button background color | |
| ) | |
| # Setup the Gradio Blocks interface with custom layout components | |
| with gr.Blocks(theme=theme) as demo: | |
| gr.Image(display_image(), show_label = False, show_share_button = False, show_download_button = False) | |
| 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="SustainAIBot Response", placeholder="SustainAIBot 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) | |