app.py

import gradio as gr

# Define the theme with custom colors and styles, including larger text sizes
theme = gr.themes.Default(
    primary_hue=gr.themes.Color(
        c100="#ffedd5", c200="#fed7aa", c300="#ffe09e", c400="#c2814c",
        c50="#fff8f0", c500="#f97316", c600="#ea580c", c700="#c2410c",
        c800="#9a3412", c900="#7c2d12", c950="#611f00"
    ),
    secondary_hue="red",
    neutral_hue="slate",
    font=[gr.themes.GoogleFont('jack armstrong'), 'ui-sans-serif', 'system-ui', 'sans-serif'],
    font_mono=[gr.themes.GoogleFont('xkcd'), 'ui-monospace', 'Consolas', 'monospace'],
).set(
    body_text_color='*primary_950',
    body_text_color_dark='*secondary_50',
    body_text_size='26px',  # Increase body text size
    body_text_color_subdued='*primary_400',
    body_text_weight='500',
    background_fill_primary='*primary_300',
    background_fill_primary_dark='*primary_800',
    background_fill_secondary='*primary_50',
    background_fill_secondary_dark='*primary_600',
    border_color_accent='*secondary_950',
    border_color_accent_dark='*body_text_color',
    border_color_accent_subdued='*border_color_accent',
    link_text_color='*secondary_800',
    code_background_fill='*neutral_200',
    code_background_fill_dark='*neutral_100',
    block_shadow='none',
    block_shadow_dark='none',
    form_gap_width='0px',
    checkbox_label_background_fill='*button_secondary_background_fill',
    checkbox_label_background_fill_dark='*button_secondary_background_fill',
    checkbox_label_background_fill_hover='*button_secondary_background_fill_hover',
    checkbox_label_background_fill_hover_dark='*button_secondary_background_fill_hover',
    checkbox_label_shadow='none',
    error_background_fill_dark='*background_fill_primary',
    input_background_fill='*neutral_100',
    input_background_fill_dark='*neutral_700',
    input_border_width='0px',
    input_border_width_dark='0px',
    input_shadow='none',
    input_shadow_dark='none',
    input_shadow_focus='*input_shadow',
    input_shadow_focus_dark='*input_shadow',
    stat_background_fill='*primary_300',
    stat_background_fill_dark='*primary_500',
    button_shadow='none',
    button_shadow_active='none',
    button_shadow_hover='none',
    button_transition='background-color 0.2s ease',
    button_primary_background_fill='*primary_200',
    button_primary_background_fill_dark='*primary_700',
    button_primary_background_fill_hover='*button_primary_background_fill',
    button_primary_background_fill_hover_dark='*button_primary_background_fill',
    button_primary_border_color_dark='*primary_600',
    button_secondary_background_fill='*neutral_200',
    button_secondary_background_fill_dark='*neutral_600',
    button_secondary_background_fill_hover='*button_secondary_background_fill',
    button_secondary_background_fill_hover_dark='*button_secondary_background_fill',
    button_cancel_background_fill='*button_secondary_background_fill',
    button_cancel_background_fill_dark='*button_secondary_background_fill',
    button_cancel_background_fill_hover='*button_cancel_background_fill',
    button_cancel_background_fill_hover_dark='*button_cancel_background_fill',
    button_cancel_border_color='*button_secondary_border_color',
    button_cancel_border_color_dark='*button_secondary_border_color',
    button_cancel_text_color='*button_secondary_text_color',
    button_cancel_text_color_dark='*button_secondary_text_color'
)

from sentence_transformers import SentenceTransformer, util
import openai
import os

os.environ["TOKENIZERS_PARALLELISM"] = "false"

filename = "output_chess_details.txt"
retrieval_model_name = 'output/sentence-transformer-finetuned/'

openai.api_key = os.environ["OPENAI_API_KEY"]

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):
    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):
    try:
        lower_query = user_query.lower()
        query_embedding = retrieval_model.encode(lower_query)
        segment_embeddings = retrieval_model.encode(segments)
        similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0]
        best_idx = similarities.argmax()
        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:
        system_message = "You are a chatbot specialized in providing information on local events, pro-Palestine movements, and community outreach, pride movements/events and community resources."
        user_message = f"Here's the information on St. Louis local events, outreach programs, community resources and local activism and movements: {relevant_segment}"
        messages = [
            {"role": "system", "content": system_message},
            {"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
        )
        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):
    if question == "":
        return "Welcome to GloBot! Ask me anything about the St. Louis Community!"
    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

welcome_message = """
## Your AI-driven assistant for STL community outreach queries. Created by Honna, Davonne, and Maryam of the 2024 Kode With Klossy St.Louis Camp! 
"""

topics = """
### Feel free to ask me anything from the topics below!
-  Pro-Palestine Events
-  Pride Events
-  Social Justice Workshops
-  Cultural Festivals
-  Community Outreach Programs
-  Environmental Activism
-  Health & Wellness Events
- How to Support Local Businesses
"""

def display_image():
    return "Globot_Logo3.jpg"

# Setup the Gradio Blocks interface with custom layout components
with gr.Blocks(theme=theme) as demo:
    gr.Image(display_image(), width=2000, height=600)
    gr.Markdown(welcome_message)
    with gr.Row():
        with gr.Column():
            gr.Markdown(topics)
    with gr.Row():
        with gr.Column():
            question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
            answer = gr.Textbox(label="GloBot Response", placeholder="GloBot 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)