app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util
import openai
import os

# potential color customization

import gradio as gr
from transformers import pipeline

# Load the Hugging Face pipeline
# summarizer = pipeline("summarization")

# # Define the function to be used in the Gradio interface
# def summarize_text(text):
#     summary = summarizer(text)[0]['summary_text']
#     return summary

# Define custom CSS
# custom_css = ""
# body {
#     background-color: #a9e3cb;
# }
# .gradio-title {
   


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

# Initialize paths and model identifiers for easy configuration and maintenance
filename = "output_chess_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 in providing information about St. Louis events.
    """
    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=150,
            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):
    """
# Process a question, find relevant information, and generate a response.
    """
    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

# Define the welcome message and specific topics the chatbot can provide information about
welcome_message = """
# 🌎 Welcome to GloBot! 🌻

## 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
-  Enviormental Activism
-  Health & Wellness Events
- How to Support Local Businesses
"""


# Display function

def display_image():
    return "globot-logo.jpg"

# with gr.Blocks(theme=theme) as demo:
#     theme = gr.themes.Monochrome(
#     primary_hue="amber",
#     secondary_hue="rose",
# ).set(
#     background_fill_primary='*primary_200',
#     background_fill_primary_dark='*primary_200',
#     background_fill_secondary='*secondary_300',
#     background_fill_secondary_dark='*secondary_300',
#     border_color_accent='*secondary_200',
#     border_color_accent_dark='*secondary_600',
#     border_color_accent_subdued='*secondary_200',
#     border_color_primary='*secondary_300',
#     block_border_color='*secondary_200',
#     button_primary_background_fill='*secondary_300',
#     button_primary_background_fill_dark='*secondary_300'
# )

# Setup the Gradio Blocks interface with custom layout components
with gr.Blocks(theme='gstaff/xkcd') as demo:
    gr.Image(display_image(), width = 200, 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
    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)