Update app.py

app.py

@@ -7,7 +7,6 @@ from langchain_groq import ChatGroq
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains import RetrievalQA
 from langchain.docstore.document import Document
-from tempfile import NamedTemporaryFile
 import nbformat
 
 # Load Groq API Key securely
@@ -15,39 +14,42 @@ os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
 
 # Helper: Read .ipynb file and extract text
 def load_ipynb(file):
-    with NamedTemporaryFile(delete=False, suffix=".ipynb") as temp_file:
-        temp_file.write(file.read())
-        temp_file.flush()
-        with open(temp_file.name, "r", encoding="utf-8") as f:
+    try:
+        with open(file.name, "r", encoding="utf-8") as f:
             nb = nbformat.read(f, as_version=nbformat.NO_CONVERT)
             text = ""
             for cell in nb.cells:
                 if cell.cell_type in ["markdown", "code"]:
                     text += cell.source + "\n\n"
-        return [Document(page_content=text)]
+            return [Document(page_content=text)]
+    except Exception as e:
+        print("Error loading .ipynb:", e)
+        return []
 
 # Helper: Read PDF or IPYNB and build retriever chain
 def process_files(files):
-    all_docs = []
-    for file in files:
-        if file.name.endswith(".pdf"):
-            with NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
-                temp_file.write(file.read())
-                loader = PyPDFLoader(temp_file.name)
+    try:
+        all_docs = []
+        for file in files:
+            if file.name.endswith(".pdf"):
+                loader = PyPDFLoader(file.name)
                 all_docs.extend(loader.load())
-        elif file.name.endswith(".ipynb"):
-            all_docs.extend(load_ipynb(file))
+            elif file.name.endswith(".ipynb"):
+                all_docs.extend(load_ipynb(file))
 
-    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    chunks = splitter.split_documents(all_docs)
+        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+        chunks = splitter.split_documents(all_docs)
 
-    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-    vectorstore = FAISS.from_documents(chunks, embeddings)
-    retriever = vectorstore.as_retriever()
+        embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        vectorstore = FAISS.from_documents(chunks, embeddings)
+        retriever = vectorstore.as_retriever()
 
-    llm = ChatGroq(model_name="llama3-70b-8192", temperature=0)
-    qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
-    return qa_chain
+        llm = ChatGroq(model_name="llama3-70b-8192", temperature=0)
+        qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
+        return qa_chain
+    except Exception as e:
+        print("Error in processing files:", e)
+        return None
 
 # Global chain
 qa_chain = None
@@ -55,12 +57,17 @@ qa_chain = None
 def upload_docs(files):
     global qa_chain
     qa_chain = process_files(files)
+    if qa_chain is None:
+        return "❌ Error processing files. Please make sure the file format is correct."
     return "✅ PDFs or Notebooks uploaded and processed. Now ask your questions."
 
 def ask_question(query):
     if qa_chain is None:
         return "❌ Please upload PDFs or Kaggle Notebooks first."
-    return qa_chain.run(query)
+    try:
+        return qa_chain.run(query)
+    except Exception as e:
+        return f"⚠ Error answering question: {e}"
 
 # Gradio UI
 with gr.Blocks() as app: