Upload chatwithyourpdf_bot.py

chatwithyourpdf_bot.py

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+# -*- coding: utf-8 -*-
+"""ChatWithYourPDF_Bot.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1rWK0dbIv4_9J74u21VdV7dUdZihi3k4n
+
+# **Chat With Your PDF**
+
+# **Import Libraries**
+"""
+
+! pip install langchain
+
+! pip install pypdf
+
+!pip install openai
+
+!pip install tiktoken
+
+!pip install -U docarray
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+my_key = 'Your API'
+
+import openai
+import os
+os.environ['OPENAI_API_KEY'] = my_key
+
+llm_name = "gpt-3.5-turbo"
+
+!pip install -U langchain langchain-community
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
+from langchain.vectorstores import DocArrayInMemorySearch
+from langchain.document_loaders import TextLoader
+from langchain.chains import RetrievalQA,  ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import TextLoader
+from langchain.document_loaders import PyPDFLoader
+
+"""# **Define Functions**"""
+
+def load_db(file, chain_type, k):
+    # load documents
+    loader = PyPDFLoader(file)
+    documents = loader.load()
+    # split documents
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
+    docs = text_splitter.split_documents(documents)
+    # define embedding
+    embeddings = OpenAIEmbeddings()
+    # create vector database from data
+    db = DocArrayInMemorySearch.from_documents(docs, embeddings)
+    # define retriever
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
+    # create a chatbot chain. Memory is managed externally.
+    qa = ConversationalRetrievalChain.from_llm(
+        llm=ChatOpenAI(model_name=llm_name, temperature=0),
+        chain_type=chain_type,
+        retriever=retriever,
+        return_source_documents=True,
+        return_generated_question=True,
+    )
+    return qa
+
+"""# **Define the ChatBotApp Class**"""
+
+import panel as pn
+import param
+
+class cbfs(param.Parameterized):
+    chat_history = param.List([])
+    answer = param.String("")
+    db_query  = param.String("")
+    db_response = param.List([])
+
+    def __init__(self,  **params):
+        super(cbfs, self).__init__( **params)
+        self.panels = []
+        self.loaded_file = "/content/drive/MyDrive/DataRoadMap/MachineLearning-Lecture01.pdf"
+        self.qa = load_db(self.loaded_file,"stuff", 4)
+
+    def call_load_db(self, count):
+        if count == 0 or file_input.value is None:  # init or no file specified :
+            return pn.pane.Markdown(f"Loaded File: {self.loaded_file}")
+        else:
+            file_input.save("temp.pdf")  # local copy
+            self.loaded_file = file_input.filename
+            button_load.button_style="outline"
+            self.qa = load_db("temp.pdf", "stuff", 4)
+            button_load.button_style="solid"
+        self.clr_history()
+        return pn.pane.Markdown(f"Loaded File: {self.loaded_file}")
+
+    def convchain(self, query):
+        if not query:
+            return pn.WidgetBox(pn.Row('User:', pn.pane.Markdown("", width=600)), scroll=True)
+        result = self.qa({"question": query, "chat_history": self.chat_history})
+        self.chat_history.extend([(query, result["answer"])])
+        self.db_query = result["generated_question"]
+        self.db_response = result["source_documents"]
+        self.answer = result['answer']
+        self.panels.extend([
+            pn.Row('User:', pn.pane.Markdown(query, width=600)),
+            pn.Row('ChatBot:', pn.pane.Markdown(self.answer, width=600, styles={'background-color': '#F6F6F6'}))
+        ])
+        inp.value = ''  #clears loading indicator when cleared
+        return pn.WidgetBox(*self.panels,scroll=True)
+
+    @param.depends('db_query ', )
+    def get_lquest(self):
+        if not self.db_query :
+            return pn.Column(
+                pn.Row(pn.pane.Markdown(f"Last question to DB:", styles={'background-color': '#F6F6F6'})),
+                pn.Row(pn.pane.Str("no DB accesses so far"))
+            )
+        return pn.Column(
+            pn.Row(pn.pane.Markdown(f"DB query:", styles={'background-color': '#F6F6F6'})),
+            pn.pane.Str(self.db_query )
+        )
+
+    @param.depends('db_response', )
+    def get_sources(self):
+        if not self.db_response:
+            return
+        rlist=[pn.Row(pn.pane.Markdown(f"Result of DB lookup:", styles={'background-color': '#F6F6F6'}))]
+        for doc in self.db_response:
+            rlist.append(pn.Row(pn.pane.Str(doc)))
+        return pn.WidgetBox(*rlist, width=600, scroll=True)
+
+    @param.depends('convchain', 'clr_history')
+    def get_chats(self):
+        if not self.chat_history:
+            return pn.WidgetBox(pn.Row(pn.pane.Str("No History Yet")), width=600, scroll=True)
+        rlist=[pn.Row(pn.pane.Markdown(f"Current Chat History variable", styles={'background-color': '#F6F6F6'}))]
+        for exchange in self.chat_history:
+            rlist.append(pn.Row(pn.pane.Str(exchange)))
+        return pn.WidgetBox(*rlist, width=600, scroll=True)
+
+    def clr_history(self,count=0):
+        self.chat_history = []
+        return
+
+"""# **Instantiate and Display the App**"""
+
+!pip install jupyter_bokeh
+
+import panel as pn
+pn.extension()  # Activate the panel extension
+
+cb = cbfs()
+
+file_input = pn.widgets.FileInput(accept='.pdf')
+button_load = pn.widgets.Button(name="Load pdf", button_type='primary')
+button_clearhistory = pn.widgets.Button(name="Clear History", button_type='warning')
+button_clearhistory.on_click(cb.clr_history)
+inp = pn.widgets.TextInput( placeholder='Enter text here…')
+
+bound_button_load = pn.bind(cb.call_load_db, button_load.param.clicks)
+conversation = pn.bind(cb.convchain, inp)
+
+jpg_pane = pn.pane.Image( '/content/drive/MyDrive/DataRoadMap/free-pdf-upload-icon-3389-thumb.png')
+
+tab1 = pn.Column(
+    pn.Row(inp),
+    pn.layout.Divider(),
+    pn.panel(conversation,  loading_indicator=True, height=300),
+    pn.layout.Divider(),
+)
+tab2= pn.Column(
+    pn.panel(cb.get_lquest),
+    pn.layout.Divider(),
+    pn.panel(cb.get_sources ),
+)
+tab3= pn.Column(
+    pn.panel(cb.get_chats),
+    pn.layout.Divider(),
+)
+tab4=pn.Column(
+    pn.Row( file_input, button_load, bound_button_load),
+    pn.Row( button_clearhistory, pn.pane.Markdown("Clears chat history. Can use to start a new topic" )),
+    pn.layout.Divider(),
+    pn.Row(jpg_pane.clone(width=400))
+)
+dashboard = pn.Column(
+    pn.Row(pn.pane.Markdown('# Chat With Your PDF_Bot')),
+    pn.Tabs(('Conversation', tab1), ('Database', tab2), ('Chat History', tab3),('Configure', tab4))
+)
+dashboard
+