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import streamlit as st
import pandas as pd
from sentence_transformers import SentenceTransformer
import numpy as np
import faiss
# Load data
df = pd.read_csv("frameworks.csv")
# Embed the scope column
model = SentenceTransformer("all-MiniLM-L6-v2")
scope_embeddings = model.encode(df["scope"].tolist())
index = faiss.IndexFlatL2(scope_embeddings.shape[1])
index.add(np.array(scope_embeddings))
st.set_page_config(page_title="AI Framework/DPS Finder", layout="centered")
st.title("🤖 AI-Powered Framework & DPS Finder")
st.markdown("""Fill in your procurement details and our AI will recommend suitable frameworks or DPS options.""")
with st.form("proc_form"):
authority_type = st.selectbox("1. What type of contracting authority are you?", ["Local authority", "NHS", "Central government", "Utility"])
award_method = st.selectbox("2. Would you prefer to direct award or run a mini-competition?", ["Direct award", "Mini-competition", "Either"])
contract_value = st.number_input("3. Expected contract value (incl. VAT)", min_value=0)
duration = st.text_input("4. Expected contract duration (incl. extensions)")
description = st.text_area("5. Briefly describe the type of service needed and main deliverables")
submit = st.form_submit_button("🔍 Find Frameworks")
if submit:
if description.strip() == "":
st.warning("Please enter a service description.")
else:
query_vec = model.encode([description])
D, I = index.search(np.array(query_vec), k=5)
candidates = df.iloc[I[0]]
# Filter based on authority, value cap, and award method
matches = candidates[
(candidates["authority_types"].str.contains(authority_type, case=False)) &
(candidates["value_cap"] >= contract_value) &
(
(award_method == "Either") |
((award_method == "Direct award") & (candidates["direct_award"] == "Yes")) |
((award_method == "Mini-competition") & (candidates["direct_award"] == "No"))
)
]
st.subheader("🔎 Recommended Frameworks")
if matches.empty:
st.error("No suitable frameworks found. You may need to consider running a new procurement competition or expanding your criteria.")
else:
top_n = min(len(matches), 3)
for i in range(top_n):
row = matches.iloc[i]
st.success(f"✅ {row['name']}")
st.markdown(f"- **Provider:** {row['provider']}")
st.markdown(f"- **Scope:** {row['scope']}")
st.markdown(f"- **Direct Award:** {row['direct_award']}")
st.markdown(f"- **Value Cap:** £{row['value_cap']:,}")
st.markdown(f"- [🔗 View Framework]({row['link']})")
st.markdown("---")
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