Delete app.py

app.py

@@ -1,81 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[8]:
-
-
-import pickle
-import numpy as np
-import streamlit as st
-import warnings
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-
-from pinecone import Pinecone
-from langchain_pinecone import PineconeVectorStore
-from langchain.chains import RetrievalQA
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.llms import HuggingFacePipeline
-
-warnings.filterwarnings("ignore")
-
-# ==================== Load Embeddings & Docs ====================
-try:
-    embeddings = np.load("embeddings.npy")
-    with open("documents.pkl", "rb") as f:
-        all_docs = pickle.load(f)
-except Exception as e:
-    st.error(f"❌ Error loading embeddings or documents: {e}")
-    st.stop()
-
-# ==================== Setup Pinecone ====================
-try:
-    pc = Pinecone(api_key="pcsk_5gLaFZ_UMKqGsMfKLKbjRuD8qkV8vm53YyfmmPBW2GrHUX5JKN3KQcz6zmKL44Fn4ZtN33")  # Replace with your actual key
-    index = pc.Index("changi-rag-384")
-except Exception as e:
-    st.error(f"❌ Error connecting to Pinecone: {e}")
-    st.stop()
-
-# ==================== Embedding Model ====================
-embed_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-
-# ==================== Vector Store & Retriever ====================
-vectorstore = PineconeVectorStore(
-    index=index,
-    embedding=embed_model,
-    text_key="page_content"  # This should match your metadata content key
-)
-retriever = vectorstore.as_retriever()
-
-# ==================== HuggingFace QA Model ====================
-model_name = "google/flan-t5-base"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-qa_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
-llm = HuggingFacePipeline(pipeline=qa_pipeline)
-
-qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
-
-# ==================== Streamlit UI ====================
-st.set_page_config(page_title="Changi RAG Chatbot", layout="wide")
-st.title("🛫 Changi Airport RAG Chatbot")
-
-query = st.text_input("Ask me anything about Changi Airport facilities:")
-
-if query:
-    with st.spinner("Thinking..."):
-        try:
-            response = qa.run(query)
-            st.write("### ✈️ Answer:")
-            st.success(response)
-        except Exception as e:
-            st.error(f"⚠️ Failed to generate answer: {e}")
-
-
-
-
-# In[ ]:
-
-
-
-