Initial commit.

app.py

@@ -1,7 +1,39 @@
 import gradio as gr
+from langchain import HuggingFaceHub
+from langchain.chains.question_answering import load_qa_chain
+from langchain.document_loaders import PyMuPDFLoader, TextLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from timeit import default_timer as timer
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+loader = TextLoader("rdna3.txt")
+documents = loader.load()
+
+splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
+chunks = splitter.split_documents(documents)
+
+embeddings = HuggingFaceEmbeddings()
+db = FAISS.from_documents(chunks, embeddings)
+
+llm = HuggingFaceHub(
+    repo_id='google/flan-t5-base',
+    model_kwargs={"temperature": 0, "max_length": 128})
+chain = load_qa_chain(llm, chain_type="stuff")
+
+def ask(question):
+    answers = db.similarity_search(question, k=4)
+    result = chain.run(input_documents=answers, question=question)
+    return result
+
+# Warm up.
+ask("What is VGPR")
+
+iface = gr.Interface(
+    fn=ask,
+    inputs=gr.Textbox(label="Question", placeholder="What is..."),
+    outputs=gr.Textbox(label="Answer"),
+    allow_flagging=False)
+
+iface.launch(share=True)

requirements.txt

@@ -0,0 +1,3 @@
+langchain
+faiss-cpu
+sentence_transformers