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app.py

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+import os
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+import openai
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.vectorstores.pinecone import Pinecone
+#from langchain import LLMChain
+from langchain.chains import ConversationalRetrievalChain
+from langchain.llms import OpenAI
+from langchain.memory import ConversationBufferMemory
+import streamlit as st
+import os
+
+os.environ['OPEN_API_KEY'] ='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'
+
+def db(texts,text_splitter,name):
+    chunks = text_splitter.split_text(texts)
+    embeddings = OpenAIEmbeddings()
+    db = Chroma.from_texts(chunks, embeddings,index_name="pdfgpt",namespace=name)
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":2})
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    qa = ConversationalRetrievalChain.from_llm(llm=OpenAI(temperature=0), retriever=retriever, chain_type="refine",memory=memory)
+    return qa
+
+def ai(prompt):
+    file = open("save.txt","r")
+    system_prompt = str(file.read()) 
+    print(system_prompt)
+    file.close()
+    #prompt=system_prompt+str(": question is :")+prompt
+    result = qa({"question": prompt,  "chat_history": chat_history})
+    return result["answer"]
+
+def main():
+    global qa, chat_history
+    placeholder=st.empty()
+    placeholder.empty()
+    placeholder.title("Upload or Chat PDF")
+    #st.header("PDF/URL QA")
+    #global system_prompt
+    names = ['Upload', 'Random talk']
+    page = st.radio('Format', names)
+
+    if page == 'Upload':
+        pdf = st.file_uploader("Upload your PDF", type="pdf")
+        if pdf is not None:
+            #print(pdf)
+            name=st.text_input("Name of your pdf")
+            name=name.lower()
+            pdf_reader = PdfReader(pdf)
+            texts = ""
+            for page in pdf_reader.pages:
+                texts += page.extract_text()
+            text_splitter = CharacterTextSplitter(
+            separator="\n",
+            chunk_size = 1000,
+            chunk_overlap = 0
+        )
+            qa=db(texts,text_splitter,name)
+            chat_history = []
+            st.header("PDF/URL QA")
+            query = st.text_input("Ask a question in PDF")
+            if query:
+                output = ai(query)
+                chat_history=chat_history.append(query)
+                st.write(output)
+
+    elif page == 'Random talk':
+            chat_history=[]
+            st.header("Start Chatting")
+            name=st.text_input("Your message")
+            prompt = "\"Act like a personal assistant. You can respond to questions, translate sentences, summarize news, and give recommendations. " + user_message + "\""
+            # Call the OpenAI Api to process our prompt
+            openai_response = openai.Completion.create(model="text-davinci-003", prompt=prompt,max_tokens=4000)
+            print("openai response:", openai_response)
+            # Parse the response to get the response text for our prompt
+            response_text = openai_response.choices[0].text
+            st.write( response_text)
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+openai
+goose3
+chromadb
+langchain
+PyPDF2
+python-dotenv
+tiktoken
+streamlit