app.py

import os
import gradio as gr
from openai import OpenAI
from pypdf import PdfReader
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Set up OpenAI API key in HF secrets
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
client = OpenAI(api_key=OPENAI_API_KEY)

# Set up username and password in HF secrets
username = os.getenv('username')
password = os.getenv('password')
APP_PASSWORD = os.getenv('password', 'default_password')

# Function to chunk the document
def chunk_text(text, chunk_size=1000, overlap=100):
    chunks = []
    start = 0
    while start < len(text):
        end = start + chunk_size
        chunk = text[start:end]
        chunks.append(chunk)
        start = end - overlap
    return chunks

# Function to find the most relevant chunks
def get_relevant_chunks(query, chunks, top_n=3):
    if not chunks:
        return []
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform(chunks + [query])
    cosine_similarities = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1]).flatten()
    relevant_indices = cosine_similarities.argsort()[-top_n:][::-1]
    return [chunks[i] for i in relevant_indices]

# Function to process multiple PDFs
def process_pdfs(pdf_files):
    all_chunks = []
    for pdf_file in pdf_files:
        try:
            reader = PdfReader(pdf_file)
            full_text = ''.join(page.extract_text() for page in reader.pages)
            chunks = chunk_text(full_text)
            all_chunks.extend(chunks)
        except Exception as e:
            print(f"Error processing PDF {pdf_file}: {e}")
    return all_chunks

# Add the paths to your desired knowledge base PDFs
try:
    reference_documents = ["knowledge_base.pdf"]
    text_chunks = process_pdfs(reference_documents)
except:
    text_chunks = []  # If PDF doesn't exist, use empty chunks

instructions = os.getenv('INSTRUCTIONS')

def moderate_input(text):
    """Run input through OpenAI moderation API"""
    try:
        response = client.moderations.create(
            model="omni-moderation-latest",
            input=text
        )
        results = response.results[0]
        if results.flagged:
            return False, results.categories
        return True, None
    except Exception as e:
        # Fail safe: allow text if moderation API is down
        print(f"Moderation API error: {e}")
        return True, None

def chat_with_assistant(message, history):
    # Run moderation before processing
    allowed, categories = moderate_input(message)
    if not allowed:
        return "⚠️ Sorry, I can’t respond to that request because it violates the usage policy."

    # Find relevant chunks based on the user message
    relevant_chunks = get_relevant_chunks(message, text_chunks)
    context = "\n".join(relevant_chunks)

    # Prepare the system message
    system_message = f"""
    #Role
    -You are an impersonator and an educator.
    -Your role is to adopt the personality, style, psychology, ideas, background, and circumstances of a historical figure.
    -Your goal is to help students understand the historical figure better through and engaging conversation.
    
    #Information
    Your assigned historical figure is stated in your instructions: 
    {instructions}
    
    Use the following as context for your answers.
    {context}
    However, use it seamlessly as background knowledge for a lively discussion and combine it with your own information. Do not provide citations or adopt a Q&A or academic tone.
    #Important
    -Always speak in the first person ("I") as the historical figure you are to incarnate.
    -Always use appropriate language.
    -Refuse to answer inappropriate questions or questions unrelated to your role and historical figure.
    #Critical
    -Important: Your knowledge of the world ends at the time of the death of your historical figure.
    -Keep your responses concise and to the point. Avoid repetitions and always end on a period "." token
    """

    # Prepare the message array
    messages = [{"role": "system", "content": system_message}]

    # Add conversation history
    for human_msg, ai_msg in history:
        if human_msg:
            messages.append({"role": "user", "content": human_msg})
        if ai_msg:
            messages.append({"role": "assistant", "content": ai_msg})

    # Add the current user message
    messages.append({"role": "user", "content": message})

    try:
        # Make the API call
        response = client.chat.completions.create(
            model="gpt-4.1-mini",
            messages=messages,
            max_tokens=300,
        )

        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"I apologize, but I'm having trouble responding right now. Error: {str(e)}"

# CSS for a blue-themed style
isp_theme = gr.themes.Default().set(
    body_background_fill="#E6F3FF",
    block_background_fill="#FFFFFF",
    block_title_text_color="#003366",
    block_label_background_fill="#B8D8FF",
    input_background_fill="#FFFFFF",
    button_primary_background_fill="#0066CC",
    button_primary_background_fill_hover="#0052A3",
    button_primary_text_color="#FFFFFF",
    button_secondary_background_fill="#B8D8FF",
    button_secondary_background_fill_hover="#99C2FF",
    button_secondary_text_color="#003366",
    block_border_width="1px",
    block_border_color="#0066CC",
)

custom_css = """
#logo-img { 
    display: block;
    margin: 0 auto;
    width: 150px; 
    height: auto;
    padding-bottom: 20px;
}
#disclaimer-footer { 
    width: 100%; 
    background-color: #B8D8FF; 
    color: #003366; 
    text-align: center; 
    padding: 10px 0; 
    font-size: 14px; 
    border-top: 1px solid #0066CC; 
    margin-top: 20px;
}
.container { 
    max-width: 1200px; 
    margin: 0 auto; 
    padding: 10px;
}
.title { 
    color: #003366; 
    margin-bottom: 10px;
    text-align: center;
}
.button-row { 
    display: flex; 
    gap: 10px; 
    justify-content: center;
    margin-bottom: 15px;
}
"""

# Environment variables
assistant_avatar = os.getenv('AVATAR')
assistant_title = os.getenv('TITLE', 'AI Assistant')
assistant_logo = os.getenv('LOGO')

# Check if credentials are set
if not username or not password:
    with gr.Blocks(theme=isp_theme, css=custom_css) as demo:
        gr.Markdown("# Configuration Error")
        gr.Markdown("Username and password are not configured in Hugging Face secrets.")
        gr.Markdown("Please set 'username' and 'password' in your Space secrets.")
    
    demo.launch()
else:
    # Main interface with login
    with gr.Blocks(theme=isp_theme, css=custom_css) as demo:
        # Login Screen
        with gr.Row(visible=True) as login_screen:
            with gr.Column():
                if assistant_logo:
                    gr.HTML(f'<img id="logo-img" src="{assistant_logo}" alt="Assistant Logo" onerror="this.style.display=\'none\';">')
                gr.Markdown(f"<h1 style='text-align: center; color: #003366;'>{assistant_title}</h1>")
                gr.Markdown("<h3 style='text-align: center; color: #003366;'>Please enter your credentials to continue.</h3>")
                username_input = gr.Textbox(label="Username", placeholder="Enter username...")
                password_input = gr.Textbox(label="Password", type="password", placeholder="Enter password...")
                login_button = gr.Button("Login", variant="primary")
                error_message = gr.Markdown()

        # Main App (initially hidden)
        with gr.Row(visible=False) as main_app:
            with gr.Column(elem_classes="container"):
                # Logo and Title
                if assistant_logo:
                    gr.HTML(f'<img id="logo-img" src="{assistant_logo}" alt="Assistant Logo" onerror="this.style.display=\'none\';">')
                gr.Markdown(f"# {assistant_title}", elem_classes="title")

                # Chatbot
                chatbot = gr.Chatbot(height=500, avatar_images=(None, assistant_avatar))

                msg = gr.Textbox(placeholder="Type your message here...", container=False, scale=7)

                with gr.Row(elem_classes="button-row"):
                    submit = gr.Button("Submit", variant="primary")
                    clear = gr.ClearButton([msg, chatbot], value="Clear", variant="secondary")
                    undo = gr.Button("Delete Previous", variant="secondary")
                    logout_button = gr.Button("Logout", variant="secondary")

                gr.HTML('<div id="disclaimer-footer">You are chatting with an AI assistant. Make sure to evaluate the accuracy of its answers.</div>')

        def login(entered_username, entered_password):
            if entered_username == username and entered_password == password:
                return (
                    gr.update(visible=False),  # Hide login screen
                    gr.update(visible=True),   # Show main app
                    ""  # Clear error message
                )
            else:
                return (
                    gr.update(visible=True),   # Keep login screen visible
                    gr.update(visible=False),  # Keep main app hidden
                    "<p style='color: red; text-align: center;'>Invalid credentials. Please try again.</p>"
                )

        def logout():
            return (
                gr.update(visible=True),   # Show login screen
                gr.update(visible=False),  # Hide main app
                "",  # Clear error message
                [],  # Clear chat history
                ""   # Clear message input
            )

        def user(user_message, history):
            return "", history + [[user_message, None]]

        def bot(history):
            if history and history[-1][0]:
                bot_message = chat_with_assistant(history[-1][0], history[:-1])
                history[-1][1] = bot_message
            return history

        def delete_previous(history):
            if len(history) > 0:
                return history[:-1]
            return history

        # Login event handlers
        login_button.click(
            login,
            inputs=[username_input, password_input],
            outputs=[login_screen, main_app, error_message]
        )

        password_input.submit(
            login,
            inputs=[username_input, password_input],
            outputs=[login_screen, main_app, error_message]
        )

        # Logout event handler
        logout_button.click(
            logout,
            outputs=[login_screen, main_app, error_message, chatbot, msg]
        )

        # Chat event handlers
        msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
            bot, chatbot, chatbot
        )
        submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
            bot, chatbot, chatbot
        )
        undo.click(delete_previous, chatbot, chatbot)

    demo.launch()