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import streamlit as st
from groq import Groq
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import os
from dotenv import load_dotenv
load_dotenv()
# Load API key from .env or Hugging Face secret
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
# Initialize Groq client
groq_client = Groq(api_key=GROQ_API_KEY)
# Sample knowledge base
docs = [
"Generative Engine Optimization improves AI answers for SEO.",
"RAG combines retrieval with generation for accurate responses.",
"Groq provides ultra-fast inference for LLMs.",
"Streamlit is great for building quick ML apps.",
"Hugging Face offers powerful transformer models and APIs."
]
# Load embedding model
embed_model = SentenceTransformer("all-MiniLM-L6-v2")
doc_embeddings = embed_model.encode(docs)
# Create FAISS index
index = faiss.IndexFlatL2(doc_embeddings.shape[1])
index.add(np.array(doc_embeddings))
# Streamlit UI
st.set_page_config(page_title="GEO Optimizer MVP", layout="centered")
st.title("🔍 GEO Optimization Assistant")
query = st.text_input("Ask a question or enter a topic:")
if st.button("Generate Answer") and query:
query_embedding = embed_model.encode([query])
_, I = index.search(np.array(query_embedding), k=2)
context = "\n".join([docs[i] for i in I[0]])
prompt = f"""You are a helpful assistant. Use the following context to answer the question.
Context:
{context}
Question: {query}
Answer:"""
try:
response = groq_client.chat.completions.create(
model="llama3-8b-8192", # or whatever is available
messages=[{"role": "user", "content": prompt}]
)
answer = response.choices[0].message.content
st.markdown("### ✅ Answer")
st.success(answer)
except Exception as e:
st.error(f"Error: {str(e)}")
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