Add Bangla model API

app.py

@@ -1,9 +1,10 @@
 import os
 import gradio as gr
-from config import DATA_DIR, UPLOADED_PDF_PATH, VECTOR_DIR, GROQ_API_KEY
+from config import DATA_DIR, VECTOR_DIR, GROQ_API_KEY
 from ingestion import ingest_pdf
 from rag import answer, reset_memory
 
+# ---- Runtime folders ----
 os.makedirs(DATA_DIR, exist_ok=True)
 os.makedirs(VECTOR_DIR, exist_ok=True)
 
@@ -13,10 +14,8 @@ def do_ingest(pdf_file):
     if pdf_file is None:
         return "Please upload a PDF."
 
-    with open(UPLOADED_PDF_PATH, "wb") as f:
-        f.write(pdf_file.read())
-
-    msg = ingest_pdf(UPLOADED_PDF_PATH)
+    # pdf_file is a path string from Gradio
+    msg = ingest_pdf(pdf_file)
     reset_memory()
     return f"✅ {msg}"
 
@@ -32,7 +31,7 @@ with gr.Blocks() as demo:
     gr.Markdown("## 📚 Chat with Your PDF — FAISS + Groq + Memory (LangChain)")
 
     with gr.Row():
-        pdf = gr.File(label="Upload PDF", file_types=[".pdf"], type="file")
+        pdf = gr.File(label="Upload PDF", file_types=[".pdf"], type="filepath")
         ingest_btn = gr.Button("Ingest PDF")
 
     status = gr.Textbox(label="Status", interactive=False)

rag.py

@@ -2,14 +2,15 @@ from langchain_community.vectorstores import FAISS
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain.memory import ConversationBufferMemory
 from langchain_groq import ChatGroq
-from langchain.chains import conversational_retrieval
+from langchain.chains import ConversationalRetrievalChain
 from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
 from config import VECTOR_DIR, EMBED_MODEL, GROQ_API_KEY, GROQ_MODEL
 
-_memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+# ---- Memory ----
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 
 def reset_memory():
-    _memory.clear()
+    memory.clear()
 
 def build_chain():
     embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
@@ -18,15 +19,20 @@ def build_chain():
 
     llm = ChatGroq(model=GROQ_MODEL, api_key=GROQ_API_KEY, temperature=0.1)
 
+    # Simple combined prompt
     chat_prompt = ChatPromptTemplate.from_messages([
-        SystemMessagePromptTemplate.from_template("You are a helpful assistant. Answer using the context."),
-        HumanMessagePromptTemplate.from_template("Context:\n{context}\n\nQuestion:\n{question}\nAnswer clearly and concisely:")
+        SystemMessagePromptTemplate.from_template(
+            "You are a helpful assistant. Use the context to answer user questions."
+        ),
+        HumanMessagePromptTemplate.from_template(
+            "Context:\n{context}\n\nQuestion:\n{question}\nAnswer clearly and concisely:"
+        )
     ])
 
-    chain = conversational_retrieval.from_llm(
+    chain = ConversationalRetrievalChain.from_llm(
         llm=llm,
         retriever=retriever,
-        memory=_memory,
+        memory=memory,
         combine_docs_chain_kwargs={"prompt": chat_prompt}
     )
     return chain