Update app.py

app.py

@@ -1,50 +1,55 @@
 import os
 import gradio as gr
 from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import TextLoader
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.document_loaders import TextLoader
 from langchain.chains import RetrievalQA
 from langchain_huggingface import HuggingFaceEndpoint
 
-# === Load your Arabic knowledge file ===
+# Load knowledge from Arabic text file
 loader = TextLoader("knowledge.txt", encoding="utf-8")
 docs = loader.load()
 
-# === Split the text into chunks ===
+# Split documents into chunks
 text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
 documents = text_splitter.split_documents(docs)
 
-# === Arabic-compatible multilingual embeddings ===
+# Arabic-capable multilingual sentence embeddings
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
 
-# === Store documents in FAISS vector store ===
+# Create FAISS vector store
 vectorstore = FAISS.from_documents(documents, embeddings)
 
-# === Load LLM via Hugging Face Endpoint (secure token from secrets) ===
+# Get token from secret
 token = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
 
+# Correct way: pass temperature and max_new_tokens explicitly
 llm = HuggingFaceEndpoint(
     repo_id="tiiuae/falcon-7b-instruct",
     huggingfacehub_api_token=token,
-    model_kwargs={"temperature": 0.3, "max_new_tokens": 256}
+    temperature=0.3,
+    max_new_tokens=256
 )
 
-# === Create retrieval-based QA chain ===
-qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever())
+# Create the RetrievalQA chain
+qa = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=vectorstore.as_retriever()
+)
 
-# === Define Arabic chatbot function ===
+# Arabic chatbot function
 def answer_question_arabic(question):
     return qa.run(question)
 
-# === Gradio interface ===
+# Gradio interface
 iface = gr.Interface(
     fn=answer_question_arabic,
     inputs=gr.Textbox(lines=2, placeholder="اكتب سؤالك هنا", label="سؤال"),
     outputs=gr.Textbox(label="الرد"),
     title="المساعد الذكي للقطاع الوزاري",
-    description="اكتب أي سؤال متعلق بالخدمات أو الإجراءات داخل القطاع وسنجيبك باستخدام قاعدة المعرفة المتوفرة."
+    description="اكتب أي سؤال متعلق بالخدمات أو الإجراءات داخل القطاع، وسنقدم لك الرد بناءً على قاعدة المعرفة."
 )
 
-# === Launch the app ===
 iface.launch()