Delete src directory

src/streamlit_app.py

@@ -1,164 +0,0 @@
-import streamlit as st
-from langchain_community.vectorstores import FAISS
-from langchain_huggingface.embeddings import HuggingFaceEmbeddings
-from langchain.chains import RetrievalQA
-from langchain_community.llms import HuggingFacePipeline
-from transformers import pipeline
-from langchain.prompts import PromptTemplate
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_text_splitters.character import RecursiveCharacterTextSplitter
-from langchain.docstore.document import Document
-from langchain.chains import create_retrieval_chain
-from langchain_community.llms import Ollama
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-import os
-import itertools
-from langchain_community.document_loaders import PyMuPDFLoader
-
-# ----------------------
-# Sample Text Content
-# ----------------------
-SAMPLE_TEXT = """Fertilizers help improve soil nutrients and crop yield.
-Irrigation methods vary depending on climate and crop type.
-Crop rotation can enhance soil health and reduce pests.
-Composting is an organic way to enrich the soil.
-Weed management is essential for higher productivity."""
-
-EXAMPLE_QUESTIONS = [
-    "What is this document about?",
-    "What is the role of fertilizers in agriculture?",
-    "Why is crop rotation important?",
-    "How does composting help farming?",
-]
-
-HF_TOKEN = st.secrets["HF_TOKEN"]
-os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_TOKEN
-
-prompt = PromptTemplate(
-    input_variables=["context", "question"],
-    template=(
-        "You are a document question and answer expert.\n"
-        "Use the context below to answer the question.\n"
-        "Context:\n{context}\n\n"
-        "Question: {input}\n"
-    )
-)
-
-# Helper: Read uploaded file
-def read_uploaded_file(uploaded_file):
-    return uploaded_file.read().decode("utf-8")
-
-def ingest_pdf(file_name):
-    """
-    loads content of file using pymupdf
-    input (str): file names and file path
-    output (list): file content divided by pages
-    """
-    pages = []
-    loader = PyMuPDFLoader(file_name)
-
-    for page in loader.alazy_load():
-        pages.append(page)
-
-    return pages
-
-# Load lightweight LLM
-@st.cache_resource
-def load_llm():
-
-    model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
-    # model_name = "meta-llama/Llama-2-7b-chat-hf"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto", device_map="auto")
-    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=256)
-    
-    return HuggingFacePipeline(pipeline=pipe)
-    # pipe = pipeline("text-generation", model="google/flan-t5-small", max_new_tokens=256)
-    # return HuggingFacePipeline(pipeline=pipe)
-    
-# extract 
-def get_chunks(file_content):
-    """
-    split document into chunks
-    """
-    # initialise the recursive method
-    splitter = RecursiveCharacterTextSplitter(
-        chunk_size=100,
-        chunk_overlap=10
-    )
-    chunks = []
-
-    for page in file_content:
-        docs = [Document(page_content=page.page_content)]
-        texts = splitter.split_documents(docs)
-        chunks.append(texts)
-
-    return list(itertools.chain(*chunks))
-
-
-# Build retriever from uploaded content
-# @st.cache_resource
-def build_retriever(docs):
-
-    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-    db = FAISS.from_texts(docs, embeddings)
-    return db.as_retriever()
-
-
-# Streamlit UI
-st.title("DocsQA: Upload & Ask")
-
-st.markdown("Upload a text file and ask questions about its contents.")
-
-# Add sample file download button
-st.download_button(
-    label="📄 Download Sample File",
-    data=SAMPLE_TEXT,
-    file_name="sample_agri.txt",
-    mime="text/plain"
-)
-
-# Show example questions
-with st.expander("Try example questions"):
-    for q in EXAMPLE_QUESTIONS:
-        st.markdown(f"- {q}")
-
-uploaded_file = st.file_uploader("Upload your file", type=["txt","pdf"])
-if uploaded_file is not None:
-    st.write("Filename:", uploaded_file.name)
-    data = uploaded_file.read()
-
-    if uploaded_file.type == "text/plain":
-        # st.text_area("Content", data.decode("utf-8"), height=300)
-        st.info("Uploaded txt file")
-    else:
-        st.info(f"Uploaded {len(data)} bytes (PDF or other format)")
-query = st.text_input("Ask a question ")
-
-if uploaded_file is not None:
-    # st.success("file uploaded")
-    if uploaded_file.type == "text/plain":
-        # st.text_area("Content", data.decode("utf-8"), height=300)
-        st.info("Uploaded txt file")
-        docs = read_uploaded_file(uploaded_file)
-    else:
-        st.info(f"Uploaded {data} bytes (PDF or other format)")
-        docs = st.pdf(uploaded_file.read())
-    
-    retriever = build_retriever(data.decode("utf-8"))
-    llm = load_llm()
-
-    # qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
-    combine_docs_chain = create_stuff_documents_chain(llm, prompt)
-
-    qa_chain = create_retrieval_chain(retriever, combine_docs_chain)
-
-    if query:
-        with st.spinner("Generating answer..."):
-            result = qa_chain.invoke({"input":query})
-            # st.info(result)
-            answer =  result["answer"].split("\nAnswer:")[-1].strip()
-        st.success(answer)
-
-else:
-    st.info("Please upload a `.txt or .pdf` file or use the sample provided.")