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import streamlit as st
from langchain.prompts import ChatPromptTemplate
from langchain.schema import SystemMessage, HumanMessage
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from dotenv import load_dotenv
import os
load_dotenv()
HF_TOKEN = os.environ.get('HF_TOKEN')
os.environ['HF_TOKEN'] = HF_TOKEN
llm = HuggingFaceEndpoint(
repo_id="mistralai/Mistral-7B-Instruct-v0.2",
task="text-generation",
huggingfacehub_api_token=HF_TOKEN
)
llm = ChatHuggingFace(llm=llm)
def getLLamaresponse(input_text, no_words, blog_style):
if not input_text or not no_words:
return "⚠️ Please enter a valid topic and word count."
try:
template = """
Write a blog for {blog_style} job profile on the topic "{input_text}"
within {no_words} words.
"""
prompt = ChatPromptTemplate.from_template(template)
messages = [
SystemMessage(content="You are a helpful blog writer."),
HumanMessage(content=prompt.format(blog_style=blog_style, input_text=input_text, no_words=no_words))
]
response = llm.invoke(messages)
return response.content if response else "⚠️ No response from the model."
except Exception as e:
return f"❌ Error: {str(e)}"
st.set_page_config(
page_title="Generate Blogs",
page_icon='🤖',
layout='centered',
initial_sidebar_state='collapsed'
)
st.header("Generate Blogs 🤖")
input_text = st.text_input("Enter the Blog Topic")
col1, col2 = st.columns([5, 5])
with col1:
no_words = st.text_input('Number of Words')
with col2:
blog_style = st.selectbox('Writing the blog for',
('Researchers', 'Data Scientist', 'Common People'),
index=0)
submit = st.button("Generate")
if submit:
response = getLLamaresponse(input_text, no_words, blog_style)
st.write(response)
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