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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 college-specific details | |
| retrieval_model_name = 'output/sentence-transformer-finetuned/' | |
| openai.api_key = os.environ["OPENAI_API_KEY"] | |
| system_message = "You are a college consultant chatbot specialized, but not limited, in assisting high school students with learning more about colleges and the application process. Your main purpose is to create personalized lists of colleges that suit the user's financial situation, academic performance, and interests. You provide information on college demographics, admissions, tuition, campus life, programs offered, and locations to help students make informed decisions about their higher education options." | |
| # 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 college information. | |
| """ | |
| try: | |
| user_message = f"Here's the information on college: {relevant_segment}" | |
| # Append user's message to messages list | |
| messages.append({"role": "user", "content": user_message}) | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4o", | |
| messages=messages, | |
| max_tokens=600, | |
| temperature=0.7, | |
| top_p=1, | |
| frequency_penalty=0.5, | |
| presence_penalty=0.5, | |
| stop=None | |
| ) | |
| # 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 Collete! Ask me anything about college admissions, college fit or college testing." | |
| 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 Collete! | |
| ## Your AI-driven assistant for all college-related queries. Created by Abigail, Reet, and Sora of the 2024 Kode With Klossy DC Camp. | |
| """ | |
| topics = """ | |
| ### | |
| Collete wants to know what state you live in and what max tuition you would pay for college and will give you some colleges that are a good fit for you. | |
| A sample input is: I live in Virginia and I am looking for a college that has a max tution of 50k. | |
| You can also ask Collete about these topics concerning college and college admissions: | |
| - Applications | |
| - College life | |
| - Testing for college | |
| - College fit | |
| """ | |
| # 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="What do you want to ask about?") | |
| answer = gr.Textbox(label="Collete's Response", placeholder="Collete 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) |