added app and requirements files

app.py

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+from langchain.chains.question_answering import load_qa_chain
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.document_loaders import TextLoader
+from langchain.vectorstores import FAISS
+from langchain import HuggingFaceHub
+import requests
+import textwrap
+import gradio as gr
+import os
+
+# Define a function to process the API key and text input and save them in variables
+def process_inputs(api_key, text_input):
+    # Store the API key in a variable called `api_key_var`
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key
+
+
+    
+    # Check if the text input is a URL to a text file or the text itself
+    if text_input.startswith("http"):
+        # If the input is a URL, download the text file and store the text in a variable called `text_var`
+        response = requests.get(text_input)
+        text_var = response.text
+    else:
+        # If the input is the text itself, store it in a variable called `text_var`
+        text_var = text_input
+
+    with open("document.txt", "w") as f:
+      f.write(text_var)
+
+    return "Inputs saved successfully"
+
+
+def talk(question):
+
+    loader = TextLoader('./document.txt')
+    documents = loader.load()
+
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    docs = text_splitter.split_documents(documents)
+
+    llm=HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature":0, "max_length":512})
+    chain = load_qa_chain(llm, chain_type="stuff")
+    embeddings = HuggingFaceEmbeddings()
+
+
+    print(docs)
+    db = FAISS.from_documents(docs, embeddings)
+    query = question
+    docs = db.similarity_search(query)
+    return chain.run(input_documents=docs, question=query)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("Talk with your document")
+    with gr.Tab("Configs"):
+        # Create Gradio interfaces to get the API key and text input from the user
+        api_key_input = gr.inputs.Textbox(label="Enter your API key")
+        text_input = gr.inputs.Textbox(label="Enter text or URL to text file")
+        text_output = gr.Textbox(label="Status of configuration")
+        text_button = gr.Button("Save configuration")
+
+    with gr.Tab("QA"):
+        q_input = gr.inputs.Textbox(label="Please write your question")
+        q_output = gr.Textbox(label="Answer from AI")
+        q_button = gr.Button("Ask question")
+
+    text_button.click(process_inputs, inputs=[api_key_input, text_input], outputs=text_output)
+    q_button.click(talk, inputs= q_input, outputs=q_output)
+    #q_button.click(process_inputs, inputs=[api_key_input, text_input], outputs=text_output)
+
+demo.launch(debug=True)

requirements.txt

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+install langchain
+install huggingface_hub
+install sentence_transformers
+install gradio
+faiss-cpu