import streamlit as st
import requests
import json
import os
from dotenv import load_dotenv
from langchain_community.document_loaders import WebBaseLoader

# Load environment variables
load_dotenv()

# Get API key from .env
api_key = os.getenv('GROQ_API_KEY')
if not api_key:
    st.error("GROQ_API_KEY not found in .env file. Please add it to the .env file in your Hugging Face Space or local environment.")
    st.stop()

# Function to load the article/blog post from a URL
def load_text(url):
    """Load the article/blog post from a URL"""
    try:
        loader = WebBaseLoader(url)
        loader.requests_kwargs = {
            'headers': {'User-Agent': 'SummarizerBot/1.0 (https://your-site.com)'}
        }
        docs = loader.load()
        return docs[0].page_content if docs else None
    except Exception as e:
        st.error(f"Error loading URL: {e}")
        return None

# Function to summarize text using Llama 3 70B via Groq API
def summarize_text(url):
    """Summarize the content from the given URL using Llama 3 70B via Groq API"""
    text = load_text(url)
    if not text:
        return None

    # Define the prompt for summarization
    summary_prompt = f"""
You are an expert summarizer. Your task is to create a concise summary of the following text. The summary should be no more than 7-8 sentences long.

TEXT: {text}

SUMMARY:
"""

    try:
        # Make API request to Groq for summarization
        response = requests.post(
            url="https://api.groq.com/openai/v1/chat/completions",
            headers={
                "Authorization": f"Bearer {api_key}",
                "Content-Type": "application/json"
            },
            data=json.dumps({
                "model": "llama3-70b-8192",  # Working Llama model on Groq
                "messages": [
                    {
                        "role": "user",
                        "content": summary_prompt
                    }
                ],
                "max_tokens": 500,  # Limit output for concise summaries
                "temperature": 0.7  # Balanced creativity for summarization
            })
        )

        # Check if the request was successful
        if response.status_code == 200:
            result = response.json()
            summary = result['choices'][0]['message']['content']
            return summary.strip()
        else:
            st.error(f"API Error: {response.status_code} - {response.text}")
            return None

    except Exception as e:
        st.error(f"Error summarizing content: {e}")
        return None

# Streamlit app interface
st.title("Summarizer AI")
st.markdown("Enter a URL to summarize the content concisely")

with st.form(key='summarizer_form'):
    url = st.text_area(
        label="Enter the URL of the article or blog post:",
        max_chars=250,
        placeholder="https://example.com/article"
    )
    submit_button = st.form_submit_button(label="Summarize")

if submit_button and url:
    with st.spinner("Summarizing..."):
        summary = summarize_text(url)
        if summary:
            st.subheader("Summary")
            st.write(summary)
        else:
            st.error("Unable to generate summary. Please check the URL or try again.")