import streamlit as st
import openai
import hashlib
import random

# Set the OpenAI API key from Streamlit secrets
openai.api_key = st.secrets["OPENAI_API_KEY"]

# Cache responses to avoid redundant API calls
@st.cache_data
def get_cached_response(key, response=None):
    if response:
        st.session_state[key] = response
    return st.session_state.get(key)

def create_prompt(prompt: str, template: str, tone: str) -> str:
    """
    Creates a custom prompt based on the user's selected template and tone.
    """
    templates = {
        "Formal": f"Please make this sound professional and polished:\n\n{prompt}",
        "Empathetic": f"Express warmth and empathy:\n\n{prompt}",
        "Friendly": f"Make it casual and friendly:\n\n{prompt}",
        "Concise": f"Summarize this as clearly as possible:\n\n{prompt}",
        "Creative": f"Add a fun, engaging twist:\n\n{prompt}",
        "General": f"Make this sound natural and conversational:\n\n{prompt}",
    }
    custom_prompt = templates.get(template, f"Make this sound natural and conversational:\n\n{prompt}")
    
    tones = {
        "Warm": "Use a warm, approachable tone.",
        "Confident": "Sound friendly but confident.",
        "Apologetic": "Make it sound honest and genuine.",
        "Neutral": "Keep it straightforward and clear.",
        "Optimistic": "Add a hopeful, upbeat tone.",
        "Excited": "Make it energetic and enthusiastic."
    }
    tone_instruction = tones.get(tone, "Keep it natural and relatable.")
    return f"{tone_instruction}\n\n{custom_prompt}"

def refine_text(text: str) -> str:
    """
    Apply additional transformations to simulate human-like writing.
    """
    # List of phrases to introduce conversational tones
    conversational_inserts = [
        "Honestly,", "Frankly speaking,", "In a nutshell,", "To put it simply,", "If I may add,"
    ]
    
    # Substitute some formal words with informal counterparts
    replacements = {
        "do not": "don't", "cannot": "can't", "will not": "won't",
        "it is": "it's", "let us": "let's", "for example": "like,"
    }
    for formal, casual in replacements.items():
        text = text.replace(formal, casual, 1)

    # Add conversational inserts randomly
    if random.random() > 0.5:
        insert = random.choice(conversational_inserts)
        sentences = text.split(".")
        if len(sentences) > 2:
            index = random.randint(1, len(sentences) - 2)
            sentences.insert(index, insert)
            text = ". ".join(sentences).replace("..", ".")
    
    return text.strip()
    
def generate_text(prompt: str, max_tokens: int, temperature: float) -> str:
    """
    Generates humanized text using OpenAI's API based on the prompt.
    """
    try:
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "Write as if you're a real person, natural and relatable."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=0.9,
            frequency_penalty=0.4,
            presence_penalty=0.8,
        )
        
        # Refine the response to add human-like nuances
        refined_text = refine_text(response.choices[0].message.content.strip())
        return refined_text
    except Exception as e:
        st.error(f"Error generating text: {e}")
        return None

def main():
    st.set_page_config(page_title="HumanizeIt", page_icon="📝", layout="wide")
    st.title("📝 HumanizeIt")
    st.write("Transform your text into something more conversational and human-like.")

    # Input fields
    st.markdown("### Enter text to humanize:")
    prompt = st.text_area("", height=150)

    with st.expander("Advanced Options"):
        col1, col2 = st.columns(2)
        with col1:
            template = st.selectbox("Choose a Template:", ["General", "Formal", "Friendly", "Empathetic", "Concise", "Creative"])
            max_tokens = st.slider("Max Tokens:", 50, 300, 150)
        with col2:
            tone = st.selectbox("Select a Tone:", ["Neutral", "Optimistic", "Confident", "Apologetic", "Warm", "Excited"])
            temperature = st.slider("Creativity Level:", 0.1, 1.0, 0.7)

    # Generate and display humanized text
    generate_button = st.button("✨ Generate Humanized Text")
    if generate_button:
        if prompt.strip():
            with st.spinner("Generating..."):
                user_prompt = create_prompt(prompt, template, tone)
                cache_key = hashlib.md5(user_prompt.encode()).hexdigest()
                cached_response = get_cached_response(cache_key)
                
                if cached_response:
                    st.write("Retrieved from cache.")
                    humanized_text = cached_response
                else:
                    humanized_text = generate_text(user_prompt, max_tokens, temperature)
                    if humanized_text:
                        get_cached_response(cache_key, humanized_text)

            # Display result
            if humanized_text:
                st.subheader("💡 Humanized Text:")
                st.write(humanized_text)
        else:
            st.warning("Please enter text to humanize.")

    # Feedback section
    st.markdown("---")
    st.subheader("Your Feedback Matters!")
    feedback = st.radio("Was this helpful?", ["👍 Yes", "👎 No", "😐 Neutral"], horizontal=True)
    additional_feedback = st.text_input("Any suggestions or comments?")

    if st.button("Submit Feedback"):
        st.success("Thank you for your feedback!")
        # Here, you can add code to save the feedback if needed

if __name__ == "__main__":
    main()