import streamlit as st
import openai
import pandas as pd
import textstat
import os
import asyncio
from textblob import TextBlob

# Initialize OpenAI client
client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# Function to fetch available OpenAI models
def get_models():
    try:
        models = client.models.list()
        return [model.id for model in models.data]
    except Exception as e:
        st.error(f"Error fetching models: {e}")
        return []

# Function to analyze text
def analyze_text(text):
    readability = textstat.flesch_reading_ease(text)
    sentiment = TextBlob(text).sentiment.polarity
    return readability, sentiment

# Function to generate AI-enhanced content
def generate_response(prompt, model, tone):
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "system", "content": f"Rewrite this in {tone} style: {prompt}"}]
    )
    return response.choices[0].message.content.strip()

# Function for batch processing asynchronously
async def process_bulk(prompts, model, tone):
    tasks = [
        client.chat.completions.acreate(
            model=model,
            messages=[{"role": "system", "content": f"Rewrite this in {tone} style: {p}"}]
        ) for p in prompts
    ]
    responses = await asyncio.gather(*tasks)
    return [response.choices[0].message.content.strip() for response in responses]

# UI Structure
st.title("🚀 AI Content Optimizer")
st.write("Enhance, analyze, and optimize your content with AI!")

# Select AI Provider
provider = st.selectbox("Choose AI Provider", ["OpenAI"])

# Fetch available models
display_models = get_models()
if display_models:
    model_choice = st.selectbox("Choose AI Model", display_models)
else:
    model_choice = "gpt-3.5-turbo"

# Prompt Customization
st.markdown("### **Content Customization**")
user_prompt = st.text_area("Enter your content:")
tone_choice = st.selectbox("Choose a Writing Tone", ["Formal", "Casual", "Technical", "Poetic", "Persuasive"])

if user_prompt:
    readability, sentiment = analyze_text(user_prompt)
    st.write(f"**Original Readability Score:** {readability:.2f}")
    st.write(f"**Sentiment Score:** {sentiment:.2f} (Positive: 1, Negative: -1)")

# Generate AI-enhanced content
if st.button("🔄 Optimize Content"):
    optimized_content = generate_response(user_prompt, model_choice, tone_choice)
    optimized_readability, optimized_sentiment = analyze_text(optimized_content)
    
    st.write("### ✨ Optimized Content")
    st.text_area("Optimized Content:", optimized_content, height=150)
    
    st.write(f"**Optimized Readability Score:** {optimized_readability:.2f}")
    st.write(f"**Optimized Sentiment Score:** {optimized_sentiment:.2f}")
    
    if "history" not in st.session_state:
        st.session_state["history"] = []
    st.session_state["history"].append({"Original": user_prompt, "Optimized": optimized_content})

# Batch Processing
st.markdown("### 📂 Bulk Optimization (CSV Upload)")
uploaded_file = st.file_uploader("Upload a CSV file with a column named 'Content'", type=["csv"])
if uploaded_file:
    df = pd.read_csv(uploaded_file)
    if "Content" in df.columns:
        prompts = df["Content"].tolist()
        optimized_prompts = asyncio.run(process_bulk(prompts, model_choice, tone_choice))
        df["Optimized_Content"] = optimized_prompts
        st.write(df)
        st.download_button("Download Optimized CSV", df.to_csv(index=False).encode('utf-8'), "optimized_content.csv", "text/csv")
    else:
        st.error("CSV must contain a column named 'Content'")

# Show Optimization History
st.markdown("### 🔹 Optimization History")
if "history" in st.session_state and st.session_state["history"]:
    for entry in st.session_state["history"][::-1]:
        st.write(f"🔹 **Original:** {entry['Original']}")
        st.write(f"✨ **Optimized:** {entry['Optimized']}")
        st.markdown("---")

st.success("🚀 AI Content Optimizer Ready!")