Update app.py

app.py

@@ -5,7 +5,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from langchain_community.vectorstores import Chroma
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import TextLoader
-from langchain_huggingface import HuggingFaceEmbeddings  # ✅ UPDATED IMPORT
+from langchain_huggingface import HuggingFaceEmbeddings
 
 # Fix Torch SDPA issue
 os.environ["TORCH_USE_CUDA_DSA"] = "1"
@@ -30,7 +30,6 @@ vector_db = Chroma(persist_directory=CHROMA_DB_PATH, embedding_function=HuggingF
 PROMPT_TEMPLATE = """
 You are an expert research assistant. Use the provided context to answer the query. 
 If unsure, state that you don't know. Be concise and factual (max 3 sentences).
-
 Query: {user_query} 
 Context: {document_context} 
 Answer:
@@ -53,8 +52,8 @@ def find_related_documents(query):
 
 # Function to generate answers using Hugging Face Model
 def generate_answer(question, uploaded_file=None):
-    # Process file if uploaded
-    if uploaded_file is not None:
+    # Handle optional file upload
+    if uploaded_file and hasattr(uploaded_file, "name"):
         file_path = os.path.join(UPLOAD_FOLDER, uploaded_file.name)
         with open(file_path, "wb") as f:
             f.write(uploaded_file.read())
@@ -74,8 +73,8 @@ def generate_answer(question, uploaded_file=None):
 
     return answer
 
-# Gradio UI for chatbot with File Upload Support ✅ FIXED
-def chatbot_interface(question, uploaded_file):
+# Gradio UI for chatbot with truly optional file upload
+def chatbot_interface(question, uploaded_file=None):
     return generate_answer(question, uploaded_file)
 
 with gr.Blocks() as demo:
@@ -83,11 +82,11 @@ with gr.Blocks() as demo:
     
     with gr.Row():
         question_input = gr.Textbox(label="Ask a Question")
-        file_input = gr.File(label="Upload a Document (Optional)")
+        file_input = gr.File(label="Upload a Document (Optional)", optional=True)  # ✅ Explicitly optional
 
     submit_btn = gr.Button("Get Answer")
     output_text = gr.Textbox(label="Answer")
 
     submit_btn.click(chatbot_interface, inputs=[question_input, file_input], outputs=output_text)
 
-demo.launch()
+demo.launch()