Create app.py

app.py

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+import gradio as gr
+from langchain.document_loaders import OnlinePDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceHubEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import HuggingFaceHub
+from langchain.chains import RetrievalQA
+
+
+import sentence_transformers
+import faiss
+
+def loading_pdf():
+    return "Loading..."
+
+def pdf_changes(pdf_doc):
+    
+    loader = OnlinePDFLoader(pdf_doc.name)
+    pages = loader.load_and_split()
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1024,
+        chunk_overlap=64,
+        separators=['\n\n', '\n', '(?=>\. )', ' ', '']
+    )
+    docs  = text_splitter.split_documents(pages)
+    embeddings = HuggingFaceHubEmbeddings()
+    db = FAISS.from_documents(docs, embeddings)
+    
+    llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":1, "max_length":1000000})
+    global qa 
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff",  retriever=db.as_retriever(search_kwargs={"k": 3}))
+    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 PDF</h1>   
+"""
+
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML(title)
+        
+        with gr.Column():
+            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 ")
+        submit_btn = gr.Button("Send message")
+    #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
+    )
+
+demo.launch()