Create app.py

app.py

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+import gradio as gr
+
+from langchain.document_loaders import OnlinePDFLoader
+
+from langchain.text_splitter import CharacterTextSplitter
+text_splitter = CharacterTextSplitter(chunk_size=350, chunk_overlap=0)
+
+from langchain.llms import HuggingFaceHub
+flan_ul2 = HuggingFaceHub(repo_id="google/flan-ul2", model_kwargs={"temperature":0.1, "max_new_tokens":300})
+
+from langchain.embeddings import HuggingFaceHubEmbeddings
+embeddings = HuggingFaceHubEmbeddings()
+
+from langchain.vectorstores import Chroma
+
+from langchain.chains import RetrievalQA
+
+def infer(pdf_doc):
+    loader = OnlinePDFLoader(pdf_doc)
+    documents = loader.load()
+    texts = text_splitter.split_documents(documents)
+    db = Chroma.from_documents(texts, embeddings)
+    retriever = db.as_retriever()
+    qa = RetrievalQA.from_chain_type(llm=flan_ul2, chain_type="stuff", retriever=retriever, return_source_documents=True)
+    query = "What is the title of this paper?"
+    result = qa({"query": query})
+
+    return result
+
+gr.Interface(fn=infer, inputs=[gr.Textbox(value="https://arxiv.org/pdf/2304.03757.pdf")], outputs=[gr.Textbox()]).launch()