add app and requirements

app.py

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+import os
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import OpenAI
+from langchain.chains import ConversationalRetrievalChain
+import pickle
+import gradio as gr
+import time
+
+
+
+
+def upload_file(file, key):
+
+    # Set the Enviroment variable
+    os.environ["OPENAI_API_KEY"] = key
+
+    # load document
+    loader = PyPDFLoader(file.name)
+    documents = loader.load()
+
+    # split the documents into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(documents)
+
+    # OPENAI embegddings
+    embeddings = OpenAIEmbeddings()
+
+    # create the vectorestore to use as the index
+    db = FAISS.from_documents(documents, embeddings)
+
+    with open("vectorstore.pkl", "wb") as f:
+        pickle.dump(db, f)
+
+    return file.name
+
+
+with gr.Blocks() as demo:
+
+    openai_key = gr.Textbox(label="OPENAI API KEY")
+    file_output = gr.File(label="Please select a pdf file wait for the document to be displayed here")
+    upload_button = gr.UploadButton("Click to upload a pdf document", file_types=["pdf"], file_count="single")
+    upload_button.upload(upload_file, inputs = [upload_button, openai_key], outputs= file_output)
+    
+    
+    chatbot = gr.Chatbot(label="Chat")
+    msg = gr.Textbox(label="Enter your query")
+    clear = gr.Button("Clear")
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        user_message = history[-1][0]
+        with open("vectorstore.pkl", "rb") as f:
+            vectorstore = pickle.load(f)
+        llm = OpenAI(temperature=0)
+        qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)
+        hist = []
+        if history[-1][1] != None:
+            hist = history
+            
+        result = qa({"question": user_message, "chat_history": hist})
+        history[-1][1] = result['answer']
+        return history
+    
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+
+demo.launch()

requirements.txt

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+aiofiles==23.1.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+altair==4.2.2
+anyio==3.6.2
+async-timeout==4.0.2
+attrs==23.1.0
+cachetools==5.3.0
+certifi==2022.12.7
+charset-normalizer==3.1.0
+click==8.1.3
+colorama==0.4.6
+contourpy==1.0.7
+cycler==0.11.0
+dataclasses-json==0.5.7
+entrypoints==0.4
+faiss-cpu==1.7.3
+fastapi==0.95.1
+ffmpy==0.3.0
+filelock==3.11.0
+fonttools==4.39.3
+frozenlist==1.3.3
+fsspec==2023.4.0
+gptcache==0.1.12
+gradio==3.27.0
+gradio_client==0.1.3
+greenlet==2.0.2
+h11==0.14.0
+httpcore==0.17.0
+httpx==0.24.0
+huggingface-hub==0.13.4
+idna==3.4
+importlib-resources==5.12.0
+Jinja2==3.1.2
+jsonschema==4.17.3
+kiwisolver==1.4.4
+langchain==0.0.141
+linkify-it-py==2.0.0
+markdown-it-py==2.2.0
+MarkupSafe==2.1.2
+marshmallow==3.19.0
+marshmallow-enum==1.5.1
+matplotlib==3.7.1
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+multidict==6.0.4
+mypy-extensions==1.0.0
+numpy==1.24.2
+openai==0.27.4
+openapi-schema-pydantic==1.2.4
+orjson==3.8.10
+packaging==23.1
+pandas==2.0.0
+Pillow==9.5.0
+pydantic==1.10.7
+pydub==0.25.1
+pyparsing==3.0.9
+pypdf==3.8.0
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2023.3
+PyYAML==6.0
+regex==2023.3.23
+requests==2.28.2
+semantic-version==2.10.0
+six==1.16.0
+sniffio==1.3.0
+SQLAlchemy==1.4.47
+starlette==0.26.1
+tenacity==8.2.2
+tiktoken==0.3.3
+toolz==0.12.0
+tqdm==4.65.0
+typing-inspect==0.8.0
+typing_extensions==4.5.0
+tzdata==2023.3
+uc-micro-py==1.0.1
+urllib3==1.26.15
+uvicorn==0.21.1
+websockets==11.0.1
+yarl==1.8.2
+zipp==3.15.0