app.py

import streamlit as st
from transformers import pipeline

# Initialize the Hugging Face pipeline
pipe = pipeline("text-generation", model="mistralai/mathstral-7B-v0.1")

# Function to get response from the model
def get_response(input_text, keywords, blog_style, max_new_tokens=250):
    # Prompt Template
    template = """
    Generate technical project ideas for {blog_style} job profile for a topic {input_text} using these keywords: {keywords}.
    """
    
    prompt = template.format(blog_style=blog_style, input_text=input_text, keywords=keywords)
    
    # Generate the response from the model
    response = pipe(prompt, max_new_tokens=max_new_tokens)
    return response[0]['generated_text']  # Extract the generated text

# Streamlit configuration
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:
    keywords = 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 = get_response(input_text, keywords, blog_style, max_new_tokens)
    st.write(response)