Upload app.py

app.py

@@ -0,0 +1,93 @@
+from langchain.chains import RetrievalQA
+from langchain import HuggingFaceHub
+from langchain.prompts import PromptTemplate
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.document_loaders import PyPDFLoader
+from langchain.vectorstores import FAISS
+from glob import glob
+from tqdm import tqdm
+import yaml
+
+
+def load_config():
+    with open('config.yaml', 'r') as file:
+        config = yaml.safe_load(file)
+    return config
+
+
+config = load_config()
+
+
+def load_embeddings(model_name=config["embeddings"]["name"],
+                    model_kwargs={'device': config["embeddings"]["device"]}):
+    return HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
+
+
+def load_documents(directory: str):
+    """Loads all documents from a directory and returns a list of Document objects
+    args: directory format = directory/
+    """
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=config["TextSplitter"]["chunk_size"],
+                                                   chunk_overlap=config["TextSplitter"]["chunk_overlap"])
+    documents = []
+    for item_path in tqdm(glob(directory + "*.pdf")):
+        loader = PyPDFLoader(item_path)
+        documents.extend(loader.load_and_split(text_splitter=text_splitter))
+
+    return documents
+
+
+template = """Use the following pieces of context to answer the question at the end. 
+If you don't know the answer, just say that you don't know, don't try to make up an answer. 
+Use three sentences maximum and keep the answer as concise as possible. 
+Always say "thanks for asking!" at the end of the answer. 
+{context}
+Question: {question}
+Helpful Answer:"""
+QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
+
+repo_id = "google/flan-t5-xxl"
+
+
+def get_llm():
+    llm = HuggingFaceHub(
+        repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 200}
+    )
+    return llm
+
+
+def answer_question(question: str):
+    embedding_function = load_embeddings()
+    documents = load_documents("data/")
+
+    db = FAISS.from_documents(documents, embedding_function)
+
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 4})
+
+    qa_chain = RetrievalQA.from_chain_type(
+        get_llm(),
+        retriever=retriever,
+        chain_type="stuff",
+        chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
+        return_source_documents=True
+    )
+
+    output = qa_chain({"query": question})
+    return output["result"]
+
+
+# Gradio UI for PDFChat
+with gr.Blocks() as demo:
+    with gr.Tab("PdfChat"):
+        with gr.Row():
+            ans = gr.Textbox(label="Answer", lines=10)
+
+            que = gr.Textbox(label="Ask a Question", lines=3)
+
+            bttn = gr.Button(label="Submit")
+
+        bttn.click(fn=answer_question, inputs=[que], outputs=[ans])
+
+if __name__ == "__main__":
+    demo.launch()