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| import gradio as gr | |
| from sentence_transformers import SentenceTransformer, util | |
| import openai | |
| import os | |
| #trying so when user puts la or los angles specific pic comes: | |
| # URL or path to your image file | |
| #PICTURE_URL = "Stars/sf.png" | |
| #def respond(user_input): | |
| # if "los angeles" in user_input.lower() or "la" in user_input.lower(): | |
| #return f"Here's a picture of Los Angeles!", PICTURE_URL | |
| # else: | |
| # return "How can I help you with astronomy?", None | |
| # Define the Gradio interface | |
| #iface = gr.Interface( | |
| # fn=respond, | |
| # inputs="text", | |
| # outputs=["text", "image"] | |
| #) | |
| # Launch the interface | |
| #iface.launch() | |
| 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/' | |
| # Define paths to images | |
| path_to_sf_image = "Stars/sf.png" | |
| path_to_sacramento_image = "Stars/sacramento.png" | |
| path_to_la_image = "Stars/la.png" | |
| openai.api_key = os.environ["OPENAI_API_KEY"] | |
| system_message = "You are an astronomy chatbot named Starfinder specialized in providing information on stargazing, astronomical events, and outer space." | |
| # 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 astronomical information. | |
| """ | |
| try: | |
| user_message = f"Here's the information on outer space: {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 Starfinder! Ask me anything about outer space, stargazing, and upcoming astronomical events.", None | |
| if "san francisco" in question.lower(): | |
| return "There are many locations near San Francisco where you can stargaze: Lick Observatory (Mount Hamilton), Chabot Space & Science Center (Oakland) , Twin Peaks (SF), Sibley Volcanic National Reserve (Oakland), Mount Tamalpais (Marin), San Francisco State University Observatory (SF), Mount Diablo (East Bay)!", "https://huggingface.co/spaces/Starfinders/Stars/resolve/main/sf.png" | |
| if "sacramento" in question.lower(): | |
| return "There are many locations near Sacramento where you can stargaze: Kalithea Park, Northstar Park, Curtis Park, Lake Theodore, Casa Bella Verde, McKinley Park, Tiscornia Park, Old Sacramento Waterfront.", "https://huggingface.co/spaces/Starfinders/Stars/resolve/main/sacramento.png" | |
| if "los angeles" in question.lower() or "la" in question.lower(): | |
| return "There are many locations near Los Angeles where you can stargaze: Leo Carrillo State Beach (Malibu), Malibu Creek State Park (Malibu), Griffith Observatory (Griffith Park), Mount Wilson Observatory (Angeles Crest)", "https://huggingface.co/spaces/Starfinders/Stars/resolve/main/la.png" | |
| relevant_segment = find_relevant_segment(question, segments) | |
| if not relevant_segment: | |
| return "Could not find specific information. Please refine your question.", None | |
| response = generate_response(question, relevant_segment) | |
| return response, None | |
| # Define the welcome message and specific topics the chatbot can provide information about | |
| welcome_message = """ | |
| # ✧ Welcome to Starfinder! | |
| ## Your AI-driven assistant for all astronomy-related queries. Created by Aarna, Aditi, and Anastasia of the 2024 Kode With Klossy SF Camp. | |
| """ | |
| topics = """ | |
| ### Feel Free to ask me anything from the topics below! | |
| - The Night sky | |
| - Outer space insights | |
| - Light pollution | |
| - Stargazing spots | |
| - Celestial events | |
| - Astronomy tips | |
| """ | |
| # Setup the Gradio Blocks interface with custom layout components | |
| with gr.Blocks(theme='earneleh/paris') 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="StarFinder Response", placeholder="StarFinder will respond here...", interactive=False, lines=10) | |
| image_output = gr.Image(label="Image Output") # Add an Image component | |
| submit_button = gr.Button("Submit") | |
| submit_button.click(fn=query_model, inputs=question, outputs=[answer, image_output]) # Update outputs to include the image component | |
| # Launch the Gradio app to allow user interaction | |
| demo.launch(share=True) | |