Update app.py

app.py

@@ -1,7 +1,97 @@
 import gradio as gr
+import pandas as pd
+import json
 
-def greet(name):
-    return "Hello " + name + "!!"
+from langchain.document_loaders import DataFrameLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.llms import HuggingFaceHub
+from langchain.embeddings import HuggingFaceHubEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+from trafilatura import fetch_url, extract
+from trafilatura.spider import focused_crawler
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+
+
+def loading_website():
+    return "Loading..."
+
+def url_changes(url, pages_to_visit, urls_to_scrape, repo_id):
+    to_visit, links = focused_crawler(url, max_seen_urls=pages_to_visit, max_known_urls=urls_to_scrape)
+    print(f"{len(links)} to be crawled")
+
+    results_df = pd.DataFrame()
+    for url in links:
+        downloaded = fetch_url(url)
+        if downloaded:
+          result = extract(downloaded, output_format='json')
+          result = json.loads(result)
+
+          results_df = pd.concat([results_df, pd.DataFrame.from_records([result])])
+
+    loader = DataFrameLoader(results_df, page_content_column="text")
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(documents)
+    embeddings = HuggingFaceHubEmbeddings()
+    db = Chroma.from_documents(texts, embeddings)
+    retriever = db.as_retriever()
+    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0.1, "max_new_tokens":250})
+    global qa
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
+    return "Ready"
+
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, ""
+
+def bot(history):
+    response = infer(history[-1][0])
+    history[-1][1] = response['result']
+    return history
+
+def infer(question):
+
+    query = question
+    result = qa({"query": query})
+
+    return result
+
+css="""
+#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
+"""
+
+title = """
+<div style="text-align: center;max-width: 700px;">
+    <h1>Chat with your website</h1>
+    <p style="text-align: center;">Enter target URL, click the "Load website to LangChain" button</p>
+</div>
+"""
+
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML(title)
+
+        with gr.Column():
+            target_url = gr.Textbox(label="Load URL", placeholder="Enter target URL here. EX: https://www.penta.co.kr/")
+            #pdf_doc = gr.File(label="Load URL", file_types=['.pdf'], type="file")
+            repo_id = gr.Dropdown(label="LLM", choices=["google/flan-ul2", "OpenAssistant/oasst-sft-1-pythia-12b", "beomi/KoAlpaca-Polyglot-12.8B"], value="google/flan-ul2")
+            with gr.Row():
+                langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
+                load_pdf = gr.Button("Load website to langchain")
+
+        chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
+        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)
+    repo_id.change(url_changes, inputs=[target_url, gr.Number(value=20, visible=False), gr.Number(value=200, visible=False), repo_id], outputs=[langchain_status], queue=False)
+    load_pdf.click(url_changes, inputs=[target_url, gr.Number(value=20, visible=False), gr.Number(value=200, visible=False), repo_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()