import os
from dotenv import load_dotenv
import gradio as gr
from langchain_core.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableSequence

# Load environment variables
load_dotenv()

HF_TOKEN = os.getenv("HF_TOKEN")

# Initialize the HuggingFace model
llm = HuggingFaceEndpoint(
    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
    huggingfacehub_api_token=HF_TOKEN,
    temperature=0.7,
    max_new_tokens=700
)

# Define a prompt template for generating a blog
TEMPLATE = """
Write a detailed blog post on the following topic:
Topic: {topic}
Make sure the blog post is informative, engaging, well-structured, and complete in 500 words only.
"""

# Create a prompt template instance
blog_prompt_template = PromptTemplate(input_variables=["topic"], template=TEMPLATE)

# Create a chain
blog_chain = blog_prompt_template | llm | StrOutputParser()

def generate_blog_post(topic: str) -> str:
    if topic:
        # Generate the blog post
        blog_post = blog_chain.invoke({"topic": topic})
        return blog_post
    else:
        return "Please enter a topic for the blog post."

# Define the Gradio interface
interface = gr.Interface(
    fn=generate_blog_post,
    inputs=[
        gr.Textbox(label="Blog Topic", placeholder="Enter the topic here"),
    ],
    outputs="text",
    title="AI Blog Generator",
    description="Welcome to the AI Blog Generator. This tool allows you to generate high-quality, engaging blog posts in just a few clicks. Simply provide a topic, and the AI will create a detailed blog post for you.",
    theme="default"
)

if __name__ == "__main__":
    interface.launch()