Update app.py

app.py

@@ -8,41 +8,26 @@ from langchain import hub
 from langchain.schema.runnable import RunnablePassthrough
 from langchain.schema.output_parser import StrOutputParser
 import os
-import os
-os.environ['USER_AGENT'] = 'myagent'
-# Set your OpenAI API key
-#openai_api_key = os.environ.get("OPENAI_API_KEY")
 
+os.environ['USER_AGENT'] = 'myagent'
 os.environ['OPENAI_API_KEY'] = os.environ.get("OPENAI_API_KEY")
-#os.environ["OPENAI_API_KEY"] = "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"
 
-# Global variable to store the RAG chain object
 rag_chain = None
 
 def process_url(url):
     try:
-        # Initialize the loader with the specified web path
         loader = WebBaseLoader(web_paths=[url])
         docs = loader.load()
-
-        # Split the documents
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200, add_start_index=True)
         all_splits = text_splitter.split_documents(docs)
-
-        # Create vectorstore
         vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
         retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 2})
-
-        # Define the prompt
         prompt = hub.pull("rlm/rag-prompt")
-
-        # Define the LLM
         llm = ChatOpenAI(model="gpt-4")
-
-        # Define the RAG chain
+        
         def format_docs(docs):
             return "\n\n".join(doc.page_content for doc in docs)
-
+        
         global rag_chain
         rag_chain = (
             {"context": retriever | format_docs, "question": RunnablePassthrough()}
@@ -50,12 +35,11 @@ def process_url(url):
             | llm
             | StrOutputParser()
         )
-
         return "Successfully processed the URL. You can now ask questions."
     except Exception as e:
         return f"Error processing URL: {e}"
 
-def chat_with_rag_chain(message):
+def chat_with_rag_chain(message, history):
     global rag_chain
     if rag_chain:
         try:
@@ -66,23 +50,32 @@ def chat_with_rag_chain(message):
     else:
         return "Please enter a URL first and process it."
 
-# Gradio interface for entering the URL
-url_input_interface = gr.Interface(
-    fn=process_url,
-    inputs=gr.Textbox(label="Enter URL", placeholder="https://example.com"),
-    outputs=gr.Textbox(label="Status"),
-    title="RAG Chain URL Processor",
-    description="Enter a URL to process the article using a RAG chain model."
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# RAG Chain URL Processor and Chat Interface")
+    
+    with gr.Tab("URL Processor"):
+        url_input = gr.Textbox(label="Enter URL", placeholder="https://example.com")
+        process_button = gr.Button("Process URL")
+        url_output = gr.Textbox(label="Status")
+        
+        process_button.click(process_url, inputs=url_input, outputs=url_output)
+    
+    with gr.Tab("Chat Interface"):
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox(label="Your Question")
+        clear = gr.Button("Clear")
 
-# Gradio chat interface for Q&A
-chat_interface = gr.Interface(
-    fn=chat_with_rag_chain,
-    inputs=gr.Textbox(label="Your Question"),
-    outputs=gr.Textbox(label="Response"),
-    title="RAG Chain Chat Interface",
-    description="Chat with the RAG chain model after processing a URL."
-)
+        def user(user_message, history):
+            return "", history + [[user_message, None]]
+
+        def bot(history):
+            bot_message = chat_with_rag_chain(history[-1][0], history)
+            history[-1][1] = bot_message
+            return history
+
+        msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+            bot, chatbot, chatbot
+        )
+        clear.click(lambda: None, None, chatbot, queue=False)
 
-# Combining the two interfaces in a tab layout
-gr.TabbedInterface([url_input_interface, chat_interface], ["URL Processor", "Chat Interface"]).launch(debug=True)
+demo.launch(debug=True)