Update app.py

app.py

@@ -1,63 +1,68 @@
+import os
 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
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from huggingface_hub import hf_hub_download
 
-load_dotenv()
-
-# Load API key from .env or Hugging Face secret
+# API key from Hugging Face secrets
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 
-# Initialize Groq client
+# Init 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:
+# UI setup
+st.set_page_config(page_title="GEO MVP - Generative Engine Optimization", layout="wide")
+st.title("🔍 GEO: Generative Engine Optimization")
+
+# Upload document
+uploaded_file = st.file_uploader("📄 Upload a .txt file", type=["txt"])
+
+if uploaded_file:
+    # Save file
+    with open("data.txt", "wb") as f:
+        f.write(uploaded_file.read())
+
+    # Load and split
+    loader = TextLoader("data.txt")
+    documents = loader.load()
+    splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    docs = splitter.split_documents(documents)
+
+    # Embed
+    st.info("🔎 Generating embeddings...")
+    embeddings = HuggingFaceEmbeddings()
+    vectorstore = FAISS.from_documents(docs, embeddings)
+
+    # Build retriever
+    retriever = vectorstore.as_retriever()
+
+    # Prompt setup
+    prompt_template = PromptTemplate.from_template(
+        "You are an expert assistant. Use the following context to answer accurately:\n\n{context}\n\nQ: {question}\nA:"
+    )
+
+    st.success("✅ Data embedded and ready.")
+
+    # Query box
+    user_query = st.text_input("💬 Ask a question based on your uploaded file")
+
+    if user_query:
+        # Retrieve
+        results = retriever.get_relevant_documents(user_query)
+        context = "\n\n".join([doc.page_content for doc in results[:3]])
+
+        # Call Groq
+        prompt = prompt_template.format(context=context, question=user_query)
         response = groq_client.chat.completions.create(
-            model="llama3-8b-8192",  # or whatever is available
-            messages=[{"role": "user", "content": prompt}]
+            messages=[{"role": "user", "content": prompt}],
+            model="mixtral-8x7b-32768",  # Or another Groq-supported model
         )
+
         answer = response.choices[0].message.content
-        st.markdown("### ✅ Answer")
-        st.success(answer)
-    except Exception as e:
-        st.error(f"Error: {str(e)}")
+        st.markdown("### 📥 Answer")
+        st.write(answer)