Replaced API form

app.py

@@ -1,42 +1,33 @@
 import gradio as gr
 import os
 import time
-
 from langchain.document_loaders import OnlinePDFLoader
-
 from langchain.text_splitter import CharacterTextSplitter
-
-
 from langchain.llms import OpenAI
-
 from langchain.embeddings import OpenAIEmbeddings
-
-
 from langchain.vectorstores import Chroma
-
 from langchain.chains import ConversationalRetrievalChain
 
+# Set OpenAI API key from environment variable
+os.environ['OPENAI_API_KEY'] = os.getenv("Your_API_Key")
+
 def loading_pdf():
     return "Loading..."
 
-def pdf_changes(pdf_doc, open_ai_key):
-    if openai_key is not None:
-        os.environ['OPENAI_API_KEY'] = open_ai_key
-        loader = OnlinePDFLoader(pdf_doc.name)
-        documents = loader.load()
-        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-        texts = text_splitter.split_documents(documents)
-        embeddings = OpenAIEmbeddings()
-        db = Chroma.from_documents(texts, embeddings)
-        retriever = db.as_retriever()
-        global qa 
-        qa = ConversationalRetrievalChain.from_llm(
-            llm=OpenAI(temperature=0.5), 
-            retriever=retriever, 
-            return_source_documents=False)
-        return "Ready"
-    else:
-        return "You forgot OpenAI API key"
+def pdf_changes(pdf_doc):
+    loader = OnlinePDFLoader(pdf_doc.name)
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_documents(documents)
+    embeddings = OpenAIEmbeddings()
+    db = Chroma.from_documents(texts, embeddings)
+    retriever = db.as_retriever()
+    global qa 
+    qa = ConversationalRetrievalChain.from_llm(
+        llm=OpenAI(temperature=0.5), 
+        retriever=retriever, 
+        return_source_documents=False)
+    return "Ready"
 
 def add_text(history, text):
     history = history + [(text, None)]
@@ -51,22 +42,18 @@ def bot(history):
         time.sleep(0.05)
         yield history
     
-
 def infer(question, history):
-    
     res = []
     for human, ai in history[:-1]:
         pair = (human, ai)
         res.append(pair)
     
     chat_history = res
-    #print(chat_history)
     query = question
     result = qa({"question": query, "chat_history": chat_history})
-    #print(result)
     return result["answer"]
 
-css="""
+css = """
 #col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
 """
 
@@ -75,31 +62,31 @@ title = """
     <h1>Chat with PDF • OpenAI</h1>
     <p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br />
     when everything is ready, you can start asking questions about the pdf ;) <br />
-    This version is set to store chat history, and uses OpenAI as LLM, don't forget to copy/paste your OpenAI API key</p>
+    This version is set to store chat history, and uses OpenAI as LLM.</p>
 </div>
 """
 
-
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.HTML(title)
         
         with gr.Column():
-            openai_key = gr.Textbox(label="You OpenAI API key", type="password")
             pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
             with gr.Row():
                 langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
                 load_pdf = gr.Button("Load pdf to langchain")
         
         chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
-        question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
+        question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter")
         submit_btn = gr.Button("Send Message")
-    load_pdf.click(loading_pdf, None, langchain_status, queue=False)    
-    load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], outputs=[langchain_status], queue=False)
+        
+    load_pdf.click(loading_pdf, None, langchain_status, queue=False)
+    load_pdf.click(pdf_changes, inputs=[pdf_doc], outputs=[langchain_status], queue=False)
     question.submit(add_text, [chatbot, question], [chatbot, question]).then(
         bot, chatbot, chatbot
     )
     submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(
-        bot, chatbot, chatbot)
+        bot, chatbot, chatbot
+    )
 
-demo.launch()
+demo.launch()