Update app.py

app.py

@@ -5,9 +5,10 @@ from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
-from langchain_groq import ChatGroq
+from langchain_groq import ChatGroq # Changed to import ChatGroq
 from langchain.prompts import ChatPromptTemplate
 
+# Import the new dynamic template components
 from htmlTemplates import css, user_template, get_bot_template, BOT_AVATARS_BASE64
 
 
@@ -71,7 +72,7 @@ def generate_friendly_response(question, label, llm, selected_source_english):
     return result.content.strip()
 
 
-# ------------------- Handle User Input -------------------
+# ------------------- Handle User Input (Logic Only) -------------------
 def handle_userinput(user_question):
     if st.session_state.conversation is None:
         st.warning("منابع هنوز آماده نشده‌اند.")
@@ -104,10 +105,11 @@ def main():
     if "messages" not in st.session_state:
         st.session_state.messages = []
     if "llm" not in st.session_state:
+        # Changed LLM to use Groq API with ChatGroq
         st.session_state.llm = ChatGroq(
             model_name="llama-3.1-8b-instant",
             temperature=0.2,
-            api_key=os.environ["GROQ_API_KEY"]
+            api_key=os.environ["GROQ_API_KEY"] # Changed to use GROQ_API_KEY
         )
     if "selected_source_english" not in st.session_state:
         st.session_state.selected_source_english = "Khamenei"
@@ -156,13 +158,11 @@ def main():
             placeholder = st.empty()
             placeholder.info(f"⏳ در حال بارگذاری منابع {selected_source_persian}...")
             try:
-                # ✅ مدل جایگزین مطمئن و در دسترس
                 embeddings = HuggingFaceEmbeddings(
-                    model_name="HooshvareLab/bert-base-parsbert-uncased",
+                    model_name="heydariAI/persian-embeddings",
                     model_kwargs={'trust_remote_code': True},
                     cache_folder="/tmp/hf_cache"
                 )
-
                 vector_path = f"Resources/{st.session_state.selected_source_english}/faiss_index"
                 vectorstore = FAISS.load_local(
                     vector_path,