app.py

import os
import requests
import sentence_transformers

import streamlit as st

VECTOR_DB ="bbf2ef09-875b-4737-a793-499409a108b0"
JSON_DB = "f49e274a-b5c3-4573-81a2-32df8f96e97b"

IBM_API_KEY = os.getenv("IBM_API_KEY")

IBM_URL_TOKEN = "https://iam.cloud.ibm.com/identity/token"
IBM_URL_CHAT = "https://us-south.ml.cloud.ibm.com/ml/v1/text/chat?version=2023-10-25"

if "messages" not in st.session_state:
    st.session_state.messages = []
if "query" not in st.session_state:
    st.session_state.query = ""
if "extended_query" not in st.session_state:
    st.session_state.extended_query = ""

##############################################
##
##   IBM API
##
##############################################
def IBM_token():
    # Define the headers
    headers = {
        "Content-Type": "application/x-www-form-urlencoded"
    }
    
    # Define the data payload
    data = {
        "grant_type": "urn:ibm:params:oauth:grant-type:apikey",
        "apikey": IBM_API_KEY
    }
    
    # Make the POST request
    response = requests.post(IBM_URL_TOKEN, headers=headers, data=data)
    st.session_state.IBM_ACCESS_TOKEN = response.json().get("access_token", "")


def IBM_chat (messages, temperature = 0.7):
    body = {
        "model_id": "ibm/granite-3-8b-instruct",
        "project_id": os.getenv("IBM_PROJECT_ID"),
        "messages": messages,
        "max_tokens": 10000,
        "temperature": temperature,
        "time_limit": 40000
    }
    headers = {
    	"Accept": "application/json",
    	"Content-Type": "application/json",
    	"Authorization": "Bearer " + st.session_state.IBM_ACCESS_TOKEN
    }    
    response = requests.post(
    	IBM_URL_CHAT,
    	headers=headers,
    	json=body
    )
    
    if response.status_code != 200:
    	raise Exception("Non-200 response: " + str(response.text))
    
    response = response.json()
    return response["choices"][0]["message"]["content"]

def IBM_query (prompt, temperature = 0.7):
    messages = [{"role": "user", "content": prompt}]
    return IBM_chat(messages, temperature)
    
def get_credentials():
	return {
		"url" : "https://us-south.ml.cloud.ibm.com",
		"apikey" : os.getenv("IBM_API_KEY")
	}

##############################################
##
##   Vector DB
##
##############################################

from ibm_watsonx_ai.client import APIClient
from ibm_watsonx_ai.foundation_models.embeddings.sentence_transformer_embeddings import SentenceTransformerEmbeddings

def rerank( client, documents, query, top_n ):
    from ibm_watsonx_ai.foundation_models import Rerank

    reranker = Rerank(
        model_id="cross-encoder/ms-marco-minilm-l-12-v2",
        api_client=client,
        params={
            "return_options": {
                "top_n": top_n
            },
            "truncate_input_tokens": 512
        }
    )

    reranked_results = reranker.generate(query=query, inputs=documents)["results"]

    new_documents = []
    
    for result in reranked_results:
        result_index = result["index"]
        new_documents.append(documents[result_index])
        
    return new_documents


import subprocess
import gzip
import json
import chromadb
import random
import string

def hydrate_chromadb():
    #data = st.session_state.client.data_assets.get_content(JSON_DB)
    #stringified_vectors = str(content, "utf-8")
    with open("lablab - json.txt", "r", encoding="utf-8") as f:
    #with open("lablab.gzip", "rb") as f:
        gz = f.read()
    #content = gzip.decompress(gz)
    #stringified_vectors = str(content, "utf-8")

    vectors = json.loads(gz)

    chroma_client = chromadb.PersistentClient(path="./chroma_db")

    # make sure collection is empty if it already existed
    collection_name = "my_collection"
    try:
        collection = chroma_client.delete_collection(name=collection_name)
    except:
        print("Collection didn't exist - nothing to do.")
    collection = chroma_client.create_collection(name=collection_name)

    vector_embeddings = []
    vector_documents = []
    vector_metadatas = []
    vector_ids = []

    for vector in vectors:
        vector_embeddings.append(vector["embedding"])
        vector_documents.append(vector["content"]
                               )
        #metadata = vector["metadata"]
        #lines = metadata["loc"]["lines"]
        clean_metadata = {}
        clean_metadata["source"] = "Lablab website"
        #clean_metadata["asset_id"] = metadata["asset_id"]
        #clean_metadata["asset_name"] = metadata["asset_name"]
        #clean_metadata["url"] = metadata["url"]
        #clean_metadata["from"] = lines["from"]
        #clean_metadata["to"] = lines["to"]
        vector_metadatas.append(clean_metadata)
        
        #asset_id = vector["metadata"]["asset_id"]
        random_string = ''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
        #id = "{}:{}-{}-{}".format(asset_id, lines["from"], lines["to"], random_string)
        vector_ids.append(random_string)

    collection.add(
        embeddings=vector_embeddings,
        documents=vector_documents,
        metadatas=vector_metadatas,
        ids=vector_ids
    )
    return collection

def proximity_search( question ):
    query_vectors = st.session_state.emb.embed_query(question)
    query_result = st.session_state.chroma_collection.query(
        query_embeddings=query_vectors,
        n_results=st.session_state.top_n,
        include=["documents", "metadatas", "distances"]
    )

    documents = list(reversed(query_result["documents"][0]))

    #if st.session_state.vector_index_properties["settings"].get("rerank"):
    #    documents = rerank(st.session_state.client, documents, question, 10) # st.session_state.vector_index_properties["settings"]["top_k"])

    return "\n".join(documents)

def do_query(query):
    # add the submissions as context (only in prompt, not in history)
    grounding = proximity_search(query)
    prompt = query + ". For a project share the image as markdown and mention the url as well. The context for the question: " + grounding;
    #messages = st.session_state.messages.copy()
    #messages.append({"role": "user", "content": prompt})
    #st.session_state.messages.append({"role": "user", "content": query})
    messages = [{"role": "user", "content": prompt}]

    # Get response from IBM
    with st.spinner("Thinking..."):
        assistant_reply = IBM_chat(messages, 0)     ## no creativity here, just searching

    # Display assistant message
    st.chat_message("assistant").markdown(assistant_reply)
    #st.session_state.messages.append({"role": "assistant", "content": assistant_reply})
    #st.session_state.query = query

############################
##
##   UI
##
############################

# Load the banner image from the same directory
st.image("banner_policy.jpg", use_container_width=True)

# set up sidebar
st.sidebar.title("🧙 Synergy Scrolling")
st.sidebar.write(
    "Synergy Scrolling analyzes policies and finds relevant past projects. "
    "This tool helps match your policy or business idea with projects from "
    "previous LabLab hackathons."
)

################ INIT

if "client" not in st.session_state:
    with st.spinner("⏳ Waking the wizard ..."):
        IBM_token()
        wml_credentials = get_credentials()
        st.session_state.client = APIClient(credentials=wml_credentials, project_id=os.getenv("IBM_PROJECT_ID"))
        
        #vector_index_details = st.session_state.client.data_assets.get_details(VECTOR_DB)
        #st.session_state.vector_index_properties = vector_index_details["entity"]["vector_index"]
        #st.session_state.top_n = 20 if st.session_state.vector_index_properties["settings"].get("rerank") else int(st.session_state.vector_index_properties["settings"]["top_k"])
        
        st.session_state.emb = SentenceTransformerEmbeddings('sentence-transformers/all-MiniLM-L6-v2')
        st.session_state.top_n = 10

if "chroma_collection" not in st.session_state:
    with st.spinner("⏳ Dusting off the scroll books ..."):
        st.session_state.chroma_collection = hydrate_chromadb()

query = ""

################ main UI

st.title("🔮 Policy Scroll")
st.subheader("AI-Powered Project & Policy Matching")
st.write("Explore the Lab Lab Library to find relevant past projects that align with your policy or new initiative.")

################ sidebar UI

policy_input = st.sidebar.text_area("📝 Enter Your Policy or Business Idea:")

if st.sidebar.button("🔗 Analyze with IBM Granite"):
    if policy_input.strip():
        prompt = f"Define search criteria for projects to implement: {policy_input}"
        
        # Get response from IBM
        with st.spinner("Analyzing..."):
            result = IBM_query(prompt, 0.7)
            st.session_state["extended_query"] = "Find 3 projects that best match and explain why, with these criteria: " + result
    else:
        st.sidebar.warning("Please enter a policy or business idea first!")

# Display AI result in another textarea
st.sidebar.text_area("💡 Extended query:", value=st.session_state.get("extended_query", ""), height=150)
if st.sidebar.button("🔍 Search for synergy"):
    query = st.session_state.get("extended_query", "")
    
# Suggested search queries as buttons
col1, col2, col3 = st.columns(3)

with col1:
    q = "Projects with a link with Solarpunk"
    if st.button(q):
        query = q

with col2:
    q = "DEI aware projects"
    if st.button(q):
        query = q

with col3:
    q = "Decentral projects"
    if st.button(q):
        query = q
        
# User input in Streamlit
user_input = st.text_input("Describe your policy or project to find relevant Lab Lab projects...", "")
    
# Display chat history
#for message in st.session_state.messages:
#    with st.chat_message(message["role"]):
#        st.markdown(message["content"])

if user_input:
    do_query(user_input)

if query:
    do_query(query)