app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util
import openai
import os
HF_TOKEN = os.getenv('HF_TOKEN')
hf_writer =gr.HuggingFaceDatasetSaver(HF_TOKEN, "kellydoesstuff/RotBot_Flags")

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 chess information.
    """
    try:
        # system_message = "You are a chess chatbot specialized in providing information on chess rules, strategies, and terminology."
        system_message = "You are a chatbot that is specialized in translating Gen-Z/Gen-Alpha slang into standard English. In addition to translation, you are knowledgable on synonyms and origins of slang words. You not only act as a dictionary/thesaurus for slang words, you can translate sentences with slang words into standard Englsih."
        # system_message = """ You are a chatbot that translates slang English, I'm talking sentences with words like rizz and gyatt, into professional standard English. 
        # We want a translation that sounds like a sentence from a LinkedIn post. You translate sentences with multiple slang words into sentences with comprehensible standard English. 
        # Not only that, you are also knowledgeable on definitions, synonyms, and origins of slang words if prompted. 
        # So,if someone asks you to translate a sentence into standard English like, 'What does the sentence She has the biggest gyatt on god! mean?', you would respond with 'This sentence translates to she has the biggest butt I swear to god!' 
        # Or, if someone asks you to translate the sentence 'What the sigma! She just ghosted me!', you would translate that to 'What the hell! She is ignoring me!'
        # If the output is inappropriate warn the user.  DO NOT RESPOND WITH THE SAME TEXT PASSED TO YOU. YOU ARE A TRANSLATOR NOT A CONVERSATIONALIST.
        # """
        user_message = f"Here's the question the user inputted: {relevant_segment}"
        messages = [
            {"role": "system", "content": system_message},
            {"role": "user", "content": user_message}
        ]
        response = openai.ChatCompletion.create(
            model="gpt-4o",
            messages=messages,
            max_tokens=200,
            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, history):
    """
    Process a question, find relevant information, and generate a response.
    """
    # global question_g
    # question_g = question
    if question == "":
        return "What the sigma! You didn't ask a question. Feel free to ask me anything about the topics listed above."
    relevant_segment = find_relevant_segment(question, segments)
    if not relevant_segment:
        return "Unfortunately I cannot answer your question..😔 Try refining your question so I can try again."
    # global response_g
    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 RotBot!

## Your AI assistant for translating slang into standard English! 
"""

topics = """
### Feel Free to ask me anything from the topics below!
- Translating slang sentences into standard English
- Defining slang into standard English
- Providing standard English synonyms for slang
"""

# callback = gr.CSVLogger()


# Setup the Gradio Blocks interface with custom layout components
with gr.Blocks(theme='gradio/soft') 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="RotBot Response", placeholder="RotBot will respond here...", interactive=False, lines=10)
    #         submit_button = gr.Button("Submit")
    #         submit_button.click(fn=query_model, inputs=question, outputs=answer)
    # chatbot = gr.ChatInterface(
    #     fn=query_model,
    #     examples=["Help me translate this sentence into standard English: Stop glazing him! He isn't even that good. ", "What's a synonym for rizz?", "What is the definition of gyatt?"],
    #     multimodal=False,
    # )
    # with gr.Row():
    #     flag_btn = gr.Button("Flag")

    # callback.setup([chatbot], "flagged_data_points")
    # flag_btn.click(lambda *args: callback.flag(args), [chatbot], None, preprocess=False)
    chatbot = gr.Interface(
        fn=query_model,
        inputs=gr.Textbox(label="Your question", placeholder="What do you want to ask about?"),
        outputs=gr.Textbox(label="RotBot Response", placeholder="RotBot will respond here...", interactive=False, lines=10, show_copy_button = True
),
        # outputs = gr.Markdown(label="RotBot Response"),
        theme='gradio/soft',
        examples=[
            ["Help me translate this sentence into standard English: Stop glazing him! He isn't even that good."],
            ["What's a synonym for rizz?"],
            ["What is the definition of gyatt?"]
        ],
        allow_flagging="manual",
        # flagging_dir = "flag",
        flagging_options=["Ambiguous", "Wrong", "Other"],
        flagging_callback=hf_writer
        # show_copy_button = True

    )

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