modify dockerfile

README.md

@@ -13,3 +13,20 @@ short_description: Upload a document and ask questions based on its content
 
 # Welcome to DocsQA!
 
+
+```
+project/
+├── app.py                      # Main entry point
+├── config.py                   # Configuration settings
+├── utils/
+│   └── document_processor.py  # Document reading & processing
+├── models/
+│   ├── llm_loader.py          # Qwen LLM loading
+│   └── retriever.py           # FAISS retriever setup
+├── chains/
+│   └── qa_chain.py            # QA chain creation
+└── ui/
+    ├── sidebar.py             # Sidebar components
+    └── chat.py                # Chat interface
+```
+

app_archive.py

@@ -1,222 +0,0 @@
-import streamlit as st
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.chains import ConversationalRetrievalChain
-from langchain_community.llms import HuggingFacePipeline
-from langchain.memory import ConversationBufferMemory
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-import torch
-import io
-
-# For PDF processing
-try:
-    from pypdf import PdfReader
-except ImportError:
-    from PyPDF2 import PdfReader
-
-# ----------------------
-# 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?",
-]
-
-# Helper: Read uploaded file (TXT or PDF)
-def read_uploaded_file(uploaded_file):
-    uploaded_file.seek(0)
-    
-    if uploaded_file.type == "application/pdf":
-        # Handle PDF files
-        pdf_reader = PdfReader(io.BytesIO(uploaded_file.read()))
-        text = ""
-        for page in pdf_reader.pages:
-            text += page.extract_text() + "\n"
-    else:
-        # Handle text files
-        text = uploaded_file.read().decode("utf-8")
-    
-    # Split into chunks by lines
-    docs = text.split("\n")
-    docs = [doc.strip() for doc in docs if doc.strip()]
-    return docs
-
-# Load Qwen LLM
-@st.cache_resource
-def load_llm():
-    model_name = "Qwen/Qwen2.5-1.5B-Instruct"  # Using smaller Qwen model for efficiency
-    
-    # Load tokenizer and model
-    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-        device_map="auto" if torch.cuda.is_available() else None,
-        trust_remote_code=True
-    )
-    
-    # Create pipeline
-    pipe = pipeline(
-        "text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=256,
-        temperature=0.7,
-        top_p=0.95,
-        do_sample=True,
-        return_full_text=False
-    )
-    
-    return HuggingFacePipeline(pipeline=pipe)
-
-# Build retriever from uploaded content
-def build_retriever(docs):
-    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-    db = FAISS.from_texts(docs, embeddings)
-    return db.as_retriever()
-
-# Initialize session state
-if 'chat_history' not in st.session_state:
-    st.session_state.chat_history = []
-if 'qa_chain' not in st.session_state:
-    st.session_state.qa_chain = None
-if 'document_processed' not in st.session_state:
-    st.session_state.document_processed = False
-
-# Streamlit UI
-st.title("DocsQA: Chat with Your Document")
-
-st.markdown("Upload a document and have a conversation about its contents! (Powered by Qwen)")
-
-# Sidebar for document upload
-with st.sidebar:
-    st.header("📄 Document Upload")
-    
-    # Add sample file download button
-    st.download_button(
-        label="📄 Download Sample File",
-        data=SAMPLE_TEXT,
-        file_name="sample_agri.txt",
-        mime="text/plain"
-    )
-    
-    uploaded_file = st.file_uploader("Upload your file", type=["txt", "pdf"])
-    
-    if uploaded_file is not None:
-        st.success(f"{uploaded_file.name}")
-        
-        # Process document button
-        if st.button("Process Document", type="primary"):
-            with st.spinner("Processing document..."):
-                try:
-                    docs = read_uploaded_file(uploaded_file)
-                    
-                    if len(docs) > 0:
-                        retriever = build_retriever(docs)
-                        llm = load_llm()
-                        
-                        # Create conversational chain with memory
-                        memory = ConversationBufferMemory(
-                            memory_key="chat_history",
-                            return_messages=True,
-                            output_key="answer"
-                        )
-                        
-                        st.session_state.qa_chain = ConversationalRetrievalChain.from_llm(
-                            llm=llm,
-                            retriever=retriever,
-                            memory=memory,
-                            return_source_documents=True
-                        )
-                        
-                        st.session_state.document_processed = True
-                        st.session_state.chat_history = []
-                        st.success(f"Processed {len(docs)} text chunks!")
-                        st.rerun()
-                    else:
-                        st.error("No content found in file.")
-                        
-                except Exception as e:
-                    st.error(f"Error: {str(e)}")
-    
-    # Show example questions
-    if st.session_state.document_processed:
-        st.markdown("---")
-        st.subheader("💡 Example Questions")
-        for q in EXAMPLE_QUESTIONS:
-            if st.button(q, key=f"example_{q}"):
-                st.session_state.user_input = q
-                st.rerun()
-    
-    # Clear chat button
-    if st.session_state.chat_history:
-        st.markdown("---")
-        if st.button("🗑️ Clear Chat History"):
-            st.session_state.chat_history = []
-            st.rerun()
-
-# Main chat interface
-if not st.session_state.document_processed:
-    st.info("👈 Please upload a document in the sidebar and click 'Process Document' to start chatting!")
-else:
-    # Display chat history
-    for message in st.session_state.chat_history:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-            
-            # Show sources if available
-            if message["role"] == "assistant" and "sources" in message:
-                with st.expander("📚 View Sources"):
-                    for i, source in enumerate(message["sources"]):
-                        st.markdown(f"**Source {i+1}:** {source}")
-    
-    # Chat input
-    if prompt := st.chat_input("Ask a question about your document..."):
-        # Add user message to chat history
-        st.session_state.chat_history.append({"role": "user", "content": prompt})
-        
-        # Display user message
-        with st.chat_message("user"):
-            st.markdown(prompt)
-        
-        # Generate response
-        with st.chat_message("assistant"):
-            with st.spinner("Thinking..."):
-                try:
-                    result = st.session_state.qa_chain({
-                        "question": prompt
-                    })
-                    
-                    answer = result["answer"]
-                    sources = [doc.page_content for doc in result.get("source_documents", [])]
-                    
-                    st.markdown(answer)
-                    
-                    # Show sources
-                    if sources:
-                        with st.expander("📚 View Sources"):
-                            for i, source in enumerate(sources):
-                                st.markdown(f"**Source {i+1}:** {source}")
-                    
-                    # Add assistant message to chat history
-                    st.session_state.chat_history.append({
-                        "role": "assistant",
-                        "content": answer,
-                        "sources": sources
-                    })
-                    
-                except Exception as e:
-                    error_msg = f"Sorry, I encountered an error: {str(e)}"
-                    st.error(error_msg)
-                    st.session_state.chat_history.append({
-                        "role": "assistant",
-                        "content": error_msg
-                    })