Update app.py

app.py

@@ -14,19 +14,10 @@ from langchain import HuggingFaceHub
 API_KEY = os.environ["API_KEY"] 
 
 # Create a temporary upload directory
-upload_dir = tempfile.mkdtemp()
 
 # Define global variables for loaders and index
 index = None
 
-def load_file(pdf_file, progress=gr.Progress()):
-    global index
-    uploaded_pdf_path = os.path.join(upload_dir, pdf_file.name)
-    pdf_loader = UnstructuredPDFLoader(uploaded_pdf_path)
-    index = VectorstoreIndexCreator(
-    embedding=HuggingFaceEmbeddings(),
-    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    ).from_loaders([pdf_loader])
 
 def chat(message,history):
     global index
@@ -43,7 +34,7 @@ def chat(message,history):
                                         retriever=index.vectorstore.as_retriever(),
                                         input_key="question")
     # Perform question-answering on the uploaded PDF with the user's question
-    gpt_response = chain.run(message)
+    gpt_response = chain.run("Please provide the context or topic related to the PDF document you'd like to discuss. You can also ask any specific questions you have in mind. "+ message)
     return gpt_response
 
 
@@ -77,8 +68,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                 text = gr.Textbox(label="Status")
             def load_file(pdf_file):
                 global index
-                uploaded_pdf_path = os.path.join(upload_dir, pdf_file.name)
-                pdf_loader = UnstructuredPDFLoader(uploaded_pdf_path)
+                pdf_loader = UnstructuredPDFLoader(pdf_file.name)
                 index = VectorstoreIndexCreator(
                 embedding=HuggingFaceEmbeddings(),
                 text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)