Create app.py

app.py

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+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
+
+# Set Hugging Face token securely
+hf_token = os.getenv("HF_TOKEN")  # Ensure you set your token as an environment variable in Hugging Face Spaces
+
+if hf_token is None:
+    st.error("Hugging Face token not found. Please set the 'HF_TOKEN' environment variable.")
+    st.stop()
+
+# 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)
+
+# Hugging Face pipeline for text generation
+qa_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
+
+# 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
+)
+
+# 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!"}
+    ]
+
+# 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)
+
+# 6) CHAT INPUT
+user_input = st.chat_input("اكتب سؤالك هنا")
+
+# 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)
+
+    # 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}"
+
+    # 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": response})
+    with st.chat_message("assistant"):
+        st.markdown(f'<div class="rtl">{response}</div>', unsafe_allow_html=True)