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| import gradio as gr | |
| from sentence_transformers import SentenceTransformer, util | |
| import transformers | |
| from transformers import pipeline | |
| import webbrowser | |
| import openai | |
| import os | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| # Initialize paths and model identifiers for easy configuration and maintenance | |
| filename = "output_composting_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 to provide information related to composting food. | |
| """ | |
| try: | |
| system_message = "You are a chatbot specialized in providing information about food composting tips, tricks, and basics." | |
| user_message = f"Here's the information on composting: {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=0.5, | |
| top_p=1, | |
| frequency_penalty=0.5, | |
| presence_penalty=0.5 | |
| ) | |
| 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 CompBot! Ask me anything about composting tips, tricks, and basics!" | |
| 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 = """ | |
| <span style="color:#836953; font-size:24px; font-family:Roboto;">🌱Welcome to CompBot!</span> | |
| """""" | |
| ## Your AI-driven assistant for all composting-related queries. | |
| """ | |
| topics = """ | |
| ### Feel free to ask me anything from the topics below! | |
| - Components of composting | |
| - Green and brown materials | |
| - The composting process | |
| - Common strategies | |
| - Uses of compost | |
| - Tips for successful composting | |
| - Sustainability | |
| """ | |
| # Define the HTML iframe content | |
| podcast_iframe = ''' | |
| <div style="height:10px;"></div> | |
| <iframe style="border-radius:12px" | |
| src="https://open.spotify.com/embed/episode/1Emjgqf8PfwD42kvyKvtfW?utm_source=generator&theme=0" | |
| width="100%" height="152" frameBorder="0" | |
| allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe> | |
| <div style="height:20px;"></div> | |
| <iframe style="border-radius:12px" | |
| src="https://open.spotify.com/embed/episode/6m83iwiAwCOu5yaW8LOT1v?utm_source=generator&theme=0" | |
| width="100%" height="152" frameBorder="0" allowfullscreen="" | |
| allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe> | |
| ''' | |
| youtube_iframe = ''' | |
| <iframe width="560" height="315" src="https://www.youtube.com/embed/MryNKPPvFbk" frameborder="0" | |
| allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> | |
| ''' | |
| def display_image(): | |
| return "https://huggingface.co/spaces/dogutcu/composting-how-tos/resolve/main/compbot.jpeg" | |
| custom_css = """ | |
| <style> | |
| .textbox-question { | |
| background-color: #E8F0FE !important; /* Light blue background */ | |
| } | |
| .textbox-answer { | |
| background-color: #F1F8E9 !important; /* Light green background */ | |
| } | |
| </style> | |
| """ | |
| theme = gr.themes.Base().set( | |
| background_fill_primary='#AFC9AD', # Light cyan background | |
| background_fill_primary_dark='#AFC9AD', # Dark teal background | |
| background_fill_secondary='#ffccbc', # Light orange background | |
| background_fill_secondary_dark='#d84315', # Dark orange background | |
| border_color_accent='#ffab40', # Accent border color | |
| border_color_accent_dark='#ff6d00', # Dark accent border color | |
| border_color_accent_subdued='#ff8a65', # Subdued accent border color | |
| border_color_primary='#2a2a2a', # Primary border color | |
| block_border_color='#2a2a2a', # Block border color | |
| button_primary_background_fill='#2a2a2a', # Primary button background color | |
| button_primary_background_fill_dark='#2a2a2a' # Dark primary button background color | |
| ) | |
| # Setup the Gradio Blocks interface with custom layout components | |
| with gr.Blocks(theme=theme) as demo: | |
| gr.HTML(custom_css) | |
| gr.Image(display_image(), show_label = False, show_share_button = False, show_download_button = False, width=300, height=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 | |
| gr.HTML(youtube_iframe) # Embed the iframe on the left side | |
| with gr.Row(): | |
| with gr.Column(): | |
| question = gr.Textbox(label="Your question", placeholder="What would you like to know?") | |
| answer = gr.Textbox(label="CompBot Response", placeholder="CompBot will respond here...", interactive=False, lines=16) | |
| 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) | |