Update app.py

app.py

@@ -1,114 +1,58 @@
+# app.py
+
 import streamlit as st
 import os
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-# --- LANGCHAIN IMPORTS ---
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_experimental.text_splitter import SemanticChunker
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
-
-# 1) SET UP PAGE
-st.title("💬 المحادثة التفاعلية - إدارة البيانات وحماية البيانات الشخصية")
-local_file = "Policies001.pdf"
-
-index_folder = "faiss_index"
-
-# Inject custom CSS for right-to-left text
-st.markdown(
-    """
-    <style>
-        .rtl {
-            direction: rtl;
-            text-align: right;
-        }
-    </style>
-    """,
-    unsafe_allow_html=True
-)
-
-# 2) LOAD OR BUILD VECTORSTORE
-embeddings = HuggingFaceEmbeddings(
-    model_name="CAMeL-Lab/bert-base-arabic-camelbert-mix",
-    model_kwargs={"trust_remote_code": True}
-)
-
-if os.path.exists(index_folder):
-    vectorstore = FAISS.load_local(index_folder, embeddings, allow_dangerous_deserialization=True)
-else:
-    loader = PyPDFLoader(local_file)
-    documents = loader.load()
-
-    text_splitter = SemanticChunker(
-        embeddings=embeddings,
-        breakpoint_threshold_type='percentile',
-        breakpoint_threshold_amount=90
-    )
-    chunked_docs = text_splitter.split_documents(documents)
-
-    vectorstore = FAISS.from_documents(chunked_docs, embeddings)
-    vectorstore.save_local(index_folder)
-
-# 3) CREATE RETRIEVER
-retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 5})
 
-# 4) SET UP "COMMAND-R7B-ARABIC" AS LLM
-# Authenticate and load the model
-model_name = "CohereForAI/c4ai-command-r7b-arabic-02-2025"  # Replace with the actual Hugging Face model ID
+# Local imports
+from embedding import load_embeddings
+from vectorstore import load_or_build_vectorstore
+from chain_setup import build_conversational_chain
 
-# Set Hugging Face token securely
-hf_token = os.getenv("HF_TOKEN")  # Ensure you set your token as an environment variable in Hugging Face Spaces
+def main():
+    st.title("💬 المحادثة التفاعلية - ادارة البياناتات و حماية البيانات الشخصية")
 
-if hf_token is None:
-    st.error("Hugging Face token not found. Please set the 'HF_TOKEN' environment variable.")
-    st.stop()
+    # Paths and constants
+    local_file = "Policies001.pdf"
+    index_folder = "faiss_index"
 
-# Load tokenizer and model using the token
-tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=hf_token)
-model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=hf_token)
+    # Step 1: Load Embeddings
+    embeddings = load_embeddings()
 
-# Hugging Face pipeline for text generation
-qa_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
+    # Step 2: Build or load VectorStore
+    vectorstore = load_or_build_vectorstore(local_file, index_folder, embeddings)
 
-# Memory object to store conversation
-memory = ConversationBufferMemory(
-    memory_key="chat_history",  # key used internally by the chain
-    return_messages=True        # ensures we get the entire message history
-)
+    # Step 3: Build the Conversational Retrieval Chain
+    qa_chain = build_conversational_chain(vectorstore)
 
-# 5) MANAGE SESSION STATE FOR UI CHAT
-if "messages" not in st.session_state:
-    st.session_state["messages"] = [
-        {"role": "assistant", "content": "👋 مرحبًا! اسألني أي شيء عن إدارة البيانات وحماية البيانات الشخصية!"}
-    ]
+    # Step 4: Session State for UI Chat
+    if "messages" not in st.session_state:
+        st.session_state["messages"] = [
+            {"role": "assistant", "content": "👋 مرحبًا! اسألني أي شيء عن إدارة البيانات وحماية البيانات الشخصية"}
+        ]
 
-# Display existing messages in chat format
-for msg in st.session_state["messages"]:
-    with st.chat_message(msg["role"]):
-        # Apply the "rtl" class to style Arabic text correctly
-        st.markdown(f'<div class="rtl">{msg["content"]}</div>', unsafe_allow_html=True)
+    # Display existing messages
+    for msg in st.session_state["messages"]:
+        with st.chat_message(msg["role"]):
+            st.markdown(msg["content"])
 
-# 6) CHAT INPUT
-user_input = st.chat_input("اكتب سؤالك هنا")
+    # Step 5: Chat Input
+    user_input = st.chat_input("اكتب سؤالك")
 
-# 7) PROCESS NEW USER MESSAGE
-if user_input:
-    # a) Display user message in UI
-    st.session_state["messages"].append({"role": "user", "content": user_input})
-    with st.chat_message("user"):
-        st.markdown(f'<div class="rtl">{user_input}</div>', unsafe_allow_html=True)
+    # Step 6: Process user input
+    if user_input:
+        # a) Display user message
+        st.session_state["messages"].append({"role": "user", "content": user_input})
+        with st.chat_message("user"):
+            st.markdown(user_input)
 
-    # b) Run pipeline to generate a response
-    # Combine retriever results and user input for context-aware answering
-    retrieved_docs = retriever.get_relevant_documents(user_input)
-    context = "\n".join([doc.page_content for doc in retrieved_docs])
-    full_input = f"السياق:\n{context}\n\nالسؤال:\n{user_input}"
+        # b) Run chain
+        response_dict = qa_chain({"question": user_input})
+        answer = response_dict["answer"]
 
-    # Generate answer using the pipeline
-    response = qa_pipeline(full_input, max_length=500, num_return_sequences=1)[0]["generated_text"]
+        # c) Display assistant response
+        st.session_state["messages"].append({"role": "assistant", "content": answer})
+        with st.chat_message("assistant"):
+            st.markdown(answer)
 
-    # c) Display assistant response
-    st.session_state["messages"].append({"role": "assistant", "content": response})
-    with st.chat_message("assistant"):
-        st.markdown(f'<div class="rtl">{response}</div>', unsafe_allow_html=True)
+if __name__ == "__main__":
+    main()