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import streamlit as st | |
from industry_research import get_company_info | |
from use_case_generation import generate_use_cases | |
from dataset_collection import collect_datasets | |
from proposal_compilation import compile_proposal | |
st.title("AI and GenAI Use Case Generator") | |
# Step 1: Get company URL | |
company_url = st.text_input("Enter Company Website URL", "https://example.com") | |
if st.button("Generate Proposal"): | |
# Step 2: Get company info | |
st.write("Fetching company information...") | |
company_info = get_company_info(company_url) | |
st.write("Company Information:", company_info) | |
# Step 3: Generate use cases based on company info | |
st.write("Generating use cases...") | |
use_cases = generate_use_cases(company_info) | |
st.write("Use Cases:", use_cases["use_cases"]) | |
# Step 4: Collect dataset links | |
st.write("Collecting relevant datasets...") | |
resource_file = collect_datasets() | |
st.write("Datasets saved in:", resource_file) | |
# Step 5: Compile final proposal | |
proposal_file = compile_proposal(use_cases, resource_file) | |
st.write("Final proposal saved as:", proposal_file) | |
with open(proposal_file, "r") as f: | |
st.download_button("Download Proposal", f, file_name=proposal_file) | |