Create app.py

app.py

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+from llama_index.readers import TrafilaturaWebReader
+from llama_index import VectorStoreIndex
+from llama_index import ServiceContext
+from langchain.llms import HuggingFaceHub
+from llama_index.llms import LangChainLLM
+
+repo_id = 'HuggingFaceH4/zephyr-7b-beta'
+
+def loading_website(): return "Loading..."
+
+def load_url(url):
+    documents = TrafilaturaWebReader().load_data([url])
+    llm = LangChainLLM(llm=HuggingFaceHub(repo_id=repo_id, model_kwargs={'temperature': 0.2, 'max_tokens': 4096, 'top_p': 0.95}))
+    service_context = ServiceContext.from_defaults(llm=llm, embed_model="local:BAAI/bge-small-en-v1.5")
+    index = VectorStoreIndex.from_documents(documents, service_context=service_context)
+    global query_engine
+    query_engine = index.as_query_engine()
+    return 'Ready'
+
+# def chat(query):
+#     response = query_engine.query(query)
+#     return str(response)
+
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, ''
+
+def bot(history):
+    response = infer(history[-1][0])
+    history[-1][1] = response
+    return history
+
+def infer(question):
+    response = query_engine.query(question)
+    return str(response)
+
+with gr.Blocks(theme='WeixuanYuan/Soft_dark') as demo:
+    with gr.Column():
+        chatbot = gr.Chatbot([], elem_id='chatbot')
+
+        with gr.Row():
+            paper_id = gr.Textbox(label='Web Address', placeholder='http://karpathy.github.io/2019/04/25/recipe/')
+            langchain_status = gr.Textbox(label='Status', placeholder='', interactive=False)
+            load_paper = gr.Button('Load Website')
+
+        with gr.Row():
+            question = gr.Textbox(label='Question', placeholder='Type your query...')
+            submit_btn = gr.Button('Submit')
+
+    load_paper.click(paper_changes, inputs=[paper_id], 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)
+
+demo.launch(share=True)