app.py

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 college chatbot specialized in providing information on college,scholarships, and mentors."
# 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):
    try:
        user_message = f"Here's what I found about scholarships: {relevant_segment}"
        messages.append({"role": "user", "content": user_message})
        
        response = openai.ChatCompletion.create(
            model="gpt-4o", 
            messages=messages,
            max_tokens=500, # can try increasing this if responses are cut off
            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 "This is ScholarSage! Ask me anything about college or scholarships!"
    relevant_segment = find_relevant_segment(question, segments)
    if not relevant_segment:
        return "Sorry, that's not a spell I know of D: I couldn't find the 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 ScholarSage! 🧙‍♀️ 

## An AI-driven wizard for all college-related queries! Created by Sadia, Jinny, and Kristy of the 2024 Kode With Klossy NYC Camp. 
"""

topics = """
### Feel Free to ask me anything from the topics below! Reminder that I can only summon info about NY colleges and CS majors. Sorry!
- College
- Scholarships
"""

subtopics = """
### Focus questions on these subtopics:
- List of Colleges in NYS
   1. best colleges for CS
   2. private
   3. public
   4. ivy leagues
- List of Scholarships
   1. low income student friendly
   2. specific to a certain college
   3. national scholarships


"""

def display_image():
    return "https://huggingface.co/spaces/scholar-sage/Scholar-Sage/resolve/main/Screenshot%202024-08-01%20at%203.04.19%E2%80%AFPM.png"

theme = gr.themes.Soft(
    primary_hue="amber",
    secondary_hue="rose",
    neutral_hue="rose",
).set(
    body_text_color='*neutral_500',
    background_fill_primary='*primary_50',
    border_color_primary='*secondary_400',
    block_background_fill='*background_fill_primary',
    block_border_width='1px',
    block_border_width_dark='1px',
    block_label_background_fill='*background_fill_primary',
    block_label_background_fill_dark='*background_fill_secondary',
    block_label_text_color='*neutral_500',
    block_label_text_color_dark='*neutral_200',
    block_label_margin='0',
    block_label_padding='*spacing_sm *spacing_lg',
    block_label_radius='calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px) 0',
    block_label_text_size='*text_sm',
    block_label_text_weight='400',
    block_title_background_fill='none',
    block_title_background_fill_dark='none',
    block_title_text_color='*neutral_500',
    block_title_text_color_dark='*neutral_200',
    block_title_padding='0',
    block_title_radius='none',
    block_title_text_weight='400',
    panel_border_width='0',
    panel_border_width_dark='0',
    input_background_fill='*neutral_100',
    input_border_color='*border_color_primary',
    input_shadow='none',
    input_shadow_dark='none',
    input_shadow_focus='*input_shadow',
    input_shadow_focus_dark='*input_shadow',
    slider_color='#2563eb',
    slider_color_dark='#2563eb',
    button_shadow='none',
    button_shadow_active='none',
    button_shadow_hover='none',
    button_primary_background_fill='*primary_200',
    button_primary_background_fill_hover='*button_primary_background_fill',
    button_primary_background_fill_hover_dark='*button_primary_background_fill',
    button_primary_text_color='*primary_600',
    button_secondary_background_fill='*neutral_200',
    button_secondary_background_fill_hover='*button_secondary_background_fill',
    button_secondary_background_fill_hover_dark='*button_secondary_background_fill',
    button_secondary_text_color='*neutral_700',
    button_cancel_background_fill_hover='*button_cancel_background_fill',
    button_cancel_background_fill_hover_dark='*button_cancel_background_fill'
)


# Setup the Gradio Blocks interface with custom layout components
with gr.Blocks(theme=theme) as demo:
    gr.Image(display_image(), container = False, show_share_button = False, show_download_button = False, label="output", show_label=True, elem_id="output_image")
    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.Markdown(subtopics)
        with gr.Row():
            with gr.Column():
                question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
                answer = gr.Textbox(label="ScholarSage Response", placeholder="ScholarSage will respond here...", interactive=False, lines=10)
                submit_button = gr.Button("Submit")
                submit_button.click(fn=query_model, inputs=question, outputs=answer)


demo.launch()                
    

# Launch the Gradio app to allow user interaction
demo.launch(share=True)