Delete app.py

app.py

@@ -1,67 +0,0 @@
-import streamlit as st
-import os
-from langchain_community.document_loaders import PyPDFDirectoryLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings   # FIXED: new package
-from langchain_chroma import Chroma
-from langchain_groq import ChatGroq
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.runnables import RunnablePassthrough
-from langchain_core.output_parsers import StrOutputParser
-
-st.title("📚 MCA Module AI Assistant")
-st.markdown("Ask anything about your Computer Vision modules!")
-
-groq_api_key = os.getenv("GROQ_API_KEY")
-llm = ChatGroq(model="llama-3.2-3b-preview", groq_api_key=groq_api_key)
-
-@st.cache_resource
-def prepare_vectorstore():
-    loader = PyPDFDirectoryLoader("mca modules/")
-    docs = loader.load()
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-    splits = text_splitter.split_documents(docs)
-    
-    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-    
-    return Chroma.from_documents(
-        documents=splits,
-        embedding=embeddings,
-        persist_directory="/tmp/chroma_db"   # FIXED: writable path on HF Spaces
-    )
-
-vectorstore = prepare_vectorstore()
-
-template = """You are an expert AI Assistant. Use the provided context to answer the question.
-Rules:
-1. If the user asks for a comparison or specific categories, use a Markdown TABLE.
-2. Use bolding for key terms.
-3. If the answer isn't in the context, say "I cannot find this in your modules."
-
-Context:
-{context}
-
-Question: {question}
-"""
-prompt = ChatPromptTemplate.from_template(template)
-rag_chain = (
-    {"context": vectorstore.as_retriever(), "question": RunnablePassthrough()}
-    | prompt | llm | StrOutputParser()
-)
-
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-if prompt_input := st.chat_input("What is covered in Module 1?"):
-    st.session_state.messages.append({"role": "user", "content": prompt_input})
-    with st.chat_message("user"):
-        st.markdown(prompt_input)
-
-    with st.chat_message("assistant"):
-        response = rag_chain.invoke(prompt_input)
-        st.markdown(response)
-        st.session_state.messages.append({"role": "assistant", "content": response})