Update app.py

app.py

@@ -1,77 +1,58 @@
-# Save as app.py
+# app.py
 import streamlit as st
-import fitz  # PyMuPDF
-import faiss
-import os
 import numpy as np
+import faiss
 from sentence_transformers import SentenceTransformer
 import requests
 
-# Load SentenceTransformer model
-embedder = SentenceTransformer("all-MiniLM-L6-v2")
+st.set_page_config(page_title="Meraj Graphics Assistant")
 
-# Load and split PDF
-def load_and_split_pdf(pdf_path):
-    doc = fitz.open(pdf_path)
-    chunks = []
-    for page in doc:
-        text = page.get_text()
-        chunks.extend([chunk.strip() for chunk in text.split("\n") if chunk.strip()])
-    return chunks
+# Load model and index
+@st.cache_resource
+def load_data():
+    embedder = SentenceTransformer("all-MiniLM-L6-v2")
+    index = faiss.read_index("index.faiss")
+    chunks = np.load("chunks.npy", allow_pickle=True)
+    return embedder, index, chunks
 
-# Embed chunks and save in FAISS
-def create_faiss_index(text_chunks):
-    embeddings = embedder.encode(text_chunks)
-    index = faiss.IndexFlatL2(embeddings.shape[1])
-    index.add(np.array(embeddings))
-    return index, text_chunks, embeddings
+embedder, index, chunks = load_data()
 
-# Search in FAISS
-def search_faiss(query, index, text_chunks, k=3):
-    query_vec = embedder.encode([query])
-    D, I = index.search(query_vec, k)
-    results = [text_chunks[i] for i in I[0]]
+# Search FAISS
+def search(query, top_k=3):
+    q_embed = embedder.encode([query])
+    D, I = index.search(np.array(q_embed), top_k)
+    results = [chunks[i] for i in I[0]]
     return "\n".join(results)
 
-# Call Groq API with context
-def ask_groq(query, context, groq_api_key):
+# Call Groq API
+def query_groq(context, question, api_key):
+    url = "https://api.groq.com/openai/v1/chat/completions"
     headers = {
-        "Authorization": f"Bearer {groq_api_key}",
+        "Authorization": f"Bearer {api_key}",
         "Content-Type": "application/json"
     }
-
     data = {
         "model": "llama3-8b-8192",
         "messages": [
-            {"role": "system", "content": "Answer the user's question using the provided context."},
-            {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
+            {"role": "system", "content": "Answer based on the context."},
+            {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {question}"}
         ]
     }
-
-    response = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=data)
-    return response.json()['choices'][0]['message']['content'] if response.status_code == 200 else response.text
-
-# Streamlit UI
-st.set_page_config(page_title="Meraj Graphics RAG Bot")
-st.title("📄 Meraj Graphics Assistant")
-
-uploaded_pdf = st.file_uploader("Upload your PDF with business info", type="pdf")
-
-if uploaded_pdf:
-    with open("temp.pdf", "wb") as f:
-        f.write(uploaded_pdf.read())
-
-    st.success("PDF uploaded successfully!")
-    chunks = load_and_split_pdf("temp.pdf")
-    index, chunk_texts, embeddings = create_faiss_index(chunks)
-
-    query = st.text_input("Ask a question about our services:")
-    groq_key = st.text_input("Enter your Groq API key", type="password")
-
-    if st.button("Get Answer"):
-        if query and groq_key:
-            context = search_faiss(query, index, chunk_texts)
-            answer = ask_groq(query, context, groq_key)
-            st.markdown(f"**Answer:** {answer}")
-        else:
-            st.warning("Please enter both a query and API key.")
+    response = requests.post(url, headers=headers, json=data)
+    return response.json()["choices"][0]["message"]["content"]
+
+# UI
+st.title("📋 Meraj Graphics Chat Assistant")
+
+question = st.text_input("Ask something about our services:")
+groq_key = st.text_input("Groq API Key", type="password")
+
+if st.button("Get Answer"):
+    if not question or not groq_key:
+        st.warning("Please provide both question and API key.")
+    else:
+        with st.spinner("Searching..."):
+            context = search(question)
+            answer = query_groq(context, question, groq_key)
+            st.success("Answer:")
+            st.write(answer)