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import streamlit as st
from transformers import pipeline
# Function to get response from LLaMA 2 model
from transformers import AutoTokenizer, AutoModelForCausalLM
def getLLamaresponse(input_text, keywords, blog_style):
# Load the LLaMA 2 model from Hugging Face
model_name = "https://api-inference.huggingface.co/models/mistralai/mathstral-7B-v0.1"
llm = pipeline('text-generation', model=model_name)
# Prompt Template
template = """
Generate project idea for {blog_style} by using keywords like {keywords} for the profession of {input_text}.
"""
# Format the prompt
prompt = template.format(blog_style=blog_style, input_text=input_text, keywords=keywords)
# Generate the response from the LLaMA 2 model
response = llm(prompt, max_length=250, temperature=0.01)
return response[0]['generated_text']
st.set_page_config(page_title="Generate Project Idea",
page_icon='🤖',
layout='centered',
initial_sidebar_state='collapsed')
st.header("Generate Project Idea 🤖")
input_text = st.text_input("Enter the Topic")
# Creating two more columns for additional fields
col1, col2 = st.columns([5, 5])
with col1:
no_words = st.text_input('Keywords')
with col2:
blog_style = st.selectbox('Generating project idea for',
('Researchers', 'Data Scientist', 'Software Developer', 'Common People', " "), index=0)
submit = st.button("Generate")
# Final response
if submit:
response = getLLamaresponse(input_text, no_words, blog_style)
st.write(response)
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