upload

apikey.py

@@ -0,0 +1,8 @@
+import streamlit as st
+
+def get_apikey():
+    OPENAI_API_KEY = st.text_input(":blue[Enter Your OPENAI API-KEY :]", 
+                placeholder="Paste your OpenAI API key here (sk-...)",
+                type="password",
+                )
+    return OPENAI_API_KEY

app.py

@@ -0,0 +1,105 @@
+import streamlit as st
+import os
+from apikey import get_apikey
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chains import  LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+
+
+
+def make_context(docs):
+  context = ""
+  for doc in docs:
+    doc = doc.page_content +  "\n\nSource: " + doc.metadata
+    context = context + doc + "\n\n"
+  return context
+
+
+
+
+OPENAI_API_KEY = get_apikey()
+
+
+os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
+
+if OPENAI_API_KEY:
+    llm = ChatOpenAI(model='gpt-3.5-turbo',temperature = 0, openai_api_key=OPENAI_API_KEY, max_tokens=800)
+    gptturbo = ChatOpenAI(model='gpt-3.5-turbo',temperature = 0, openai_api_key=OPENAI_API_KEY, max_tokens=800)
+
+
+    if "generated" not in st.session_state:
+           st.session_state["generated"] = []
+    if "past" not in st.session_state:
+        st.session_state["past"] = []
+    if "input" not in st.session_state:
+        st.session_state["input"] = ""
+    if "stored_session" not in st.session_state:
+        st.session_state["stored_session"] = []
+    if "memory" not in st.session_state:
+        st.session_state.memory = ConversationBufferMemory(memory_key="chat_history")
+
+
+
+    uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
+
+
+    if uploaded_file is not None:
+        pdf_reader = PdfReader(uploaded_file)
+        data = ""
+        for page in pdf_reader.pages:
+            data += page.extract_text()
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size = 800,
+            chunk_overlap = 0
+        )
+
+
+        texts = text_splitter.split_text(data)       
+        embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+        docsearch = FAISS.from_texts(texts, embedding = embeddings)
+
+        question = question = st.text_input('Ask any question', key="input")
+
+        if question:
+            docs = docsearch.similarity_search(query=question)
+            for doc in docs:
+                 doc.metadata = uploaded_file.name
+            template = """
+your job is to answer the questions asked by the users. Create a final answer with references ("SOURCES").
+If the answer is not in the context, then try to answer it using your own knowledge.
+Source of the context is written at the end of the context.
+At the end of your answer write the source of the context in the following way: \n\nSource: (source)
+Chat history is also provided to you.
+Context: {context}
+---
+Chat History: {chat_history}
+Question: {question}
+Answer: Let's think step by step and give best answer possible. Use points when needed.
+"""
+
+            context = make_context(docs)
+            prompt = PromptTemplate(template=template, input_variables=["context", "question", "chat_history"]).partial(context=context)
+
+            llm_chain = LLMChain(prompt=prompt, llm=gptturbo, verbose=False, memory=st.session_state.memory)
+
+
+            response = llm_chain.run(question)
+            st.session_state.past.append(question)
+            st.session_state.generated.append(response)
+
+        with st.expander("Conversation", expanded=True):
+            for i in range(len(st.session_state['generated'])-1, -1, -1):
+                st.info(st.session_state["past"][i],icon="🧐")
+                st.success(st.session_state["generated"][i], icon="🤖")
+
+            
+
+
+
+

requirements.txt

@@ -0,0 +1,5 @@
+langchain
+faiss-cpu
+OpenAI
+PyPDF2
+tiktoken