Update app.py

app.py

@@ -2,32 +2,26 @@ import gradio as gr
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.vectorstores import Chroma
-
 from langchain.chains import ConversationalRetrievalChain
 from langchain.chat_models import ChatOpenAI
-
 from langchain.document_loaders import PyPDFLoader
 import os
-
 import fitz
 from PIL import Image
 
 # Global variables
 COUNT, N = 0, 0
 chat_history = []
-chain = ''
-enable_box = gr.Textbox.update(value=None, 
-                          placeholder='Upload your OpenAI API key', interactive=True)
-disable_box = gr.Textbox.update(value='OpenAI API key is Set', interactive=False)
+chain = None  # Initialize chain as None
 
 # Function to set the OpenAI API key
 def set_apikey(api_key):
     os.environ['OPENAI_API_KEY'] = api_key
-    return disable_box
+    return disable_box  # Update the disable_box
 
 # Function to enable the API key input box
 def enable_api_box():
-    return enable_box
+    return enable_box  # Update the enable_box
 
 # Function to add text to the chat history
 def add_text(history, text):
@@ -38,61 +32,54 @@ def add_text(history, text):
 
 # Function to process the PDF file and create a conversation chain
 def process_file(file):
+    global chain  # Access the global 'chain' variable
     if 'OPENAI_API_KEY' not in os.environ:
         raise gr.Error('Upload your OpenAI API key')
 
     loader = PyPDFLoader(file.name)
     documents = loader.load()
-
     embeddings = OpenAIEmbeddings()
-    
     pdfsearch = Chroma.from_documents(documents, embeddings)
-
-    chain = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0.3), 
-                                   retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
-                                   return_source_documents=True)
+    chain = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0.3),
+                                    retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
+                                    return_source_documents=True)
     return chain
 
 # Function to generate a response based on the chat history and query
 def generate_response(history, query, btn):
-    global COUNT, N, chat_history, chain
-    
+    global COUNT, N, chat_history, chain 
     if not btn:
         raise gr.Error(message='Upload a PDF')
+
     if COUNT == 0:
         chain = process_file(btn)
         COUNT += 1
-    
+
     result = chain({"question": query, 'chat_history': chat_history}, return_only_outputs=True)
     chat_history += [(query, result["answer"])]
     N = list(result['source_documents'][0])[1][1]['page']
 
     for char in result['answer']:
-        history[-1][-1] += char
-        yield history, ''
+        history[-1][-1] += char  # Update the last response
+        yield history, ''  
 
 # Function to render a specific page of a PDF file as an image
 def render_file(file):
     global N
     doc = fitz.open(file.name)
     page = doc[N]
-    # Render the page as a PNG image with a resolution of 300 DPI
-    pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
+    pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72)) 
     image = Image.frombytes('RGB', [pix.width, pix.height], pix.samples)
     return image
 
 # Gradio application setup
 with gr.Blocks() as demo:
-    # Create a Gradio block
-
     with gr.Column():
         with gr.Row():
             with gr.Column(scale=0.8):
-                api_key = gr.Textbox(
-                    placeholder='Enter OpenAI API key',
-                    show_label=False,
-                    interactive=True
-                ).style(container=False)
+                enable_box = gr.Textbox(placeholder='Enter OpenAI API key', 
+                                        show_label=False, interactive=True).style(container=False)
+                disable_box = gr.Textbox(value='OpenAI API key is Set', interactive=False) 
             with gr.Column(scale=0.2):
                 change_api_key = gr.Button('Change Key')