Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -1,6 +1,6 @@
|
|
1 |
import gradio as gr
|
2 |
from langchain.document_loaders import OnlinePDFLoader
|
3 |
-
from langchain.text_splitter import
|
4 |
from langchain.embeddings import HuggingFaceHubEmbeddings
|
5 |
from langchain.vectorstores import FAISS
|
6 |
from langchain.llms import HuggingFaceHub
|
@@ -21,16 +21,14 @@ def pdf_changes(pdf_doc):
|
|
21 |
|
22 |
loader = OnlinePDFLoader(pdf_doc.name)
|
23 |
pages = loader.load_and_split()
|
24 |
-
text_splitter =
|
25 |
-
chunk_size=
|
26 |
-
chunk_overlap=
|
27 |
-
separators=['\n\n', '\n', '(?=>\. )', ' ', '']
|
28 |
)
|
29 |
docs = text_splitter.split_documents(pages)
|
30 |
embeddings = HuggingFaceHubEmbeddings()
|
31 |
db = FAISS.from_documents(docs, embeddings)
|
32 |
-
|
33 |
-
llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.1, "max_new_tokens":250})
|
34 |
global qa
|
35 |
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=db.as_retriever())
|
36 |
return "Ready"
|
@@ -39,7 +37,7 @@ def book_changes(book):
|
|
39 |
db = FAISS.load_local( book , embeddings = HuggingFaceHubEmbeddings() )
|
40 |
llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.1, "max_new_tokens":250})
|
41 |
global qa
|
42 |
-
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=db.as_retriever())
|
43 |
return "Ready"
|
44 |
|
45 |
|
|
|
1 |
import gradio as gr
|
2 |
from langchain.document_loaders import OnlinePDFLoader
|
3 |
+
from langchain.text_splitter import CharacterTextSplitter
|
4 |
from langchain.embeddings import HuggingFaceHubEmbeddings
|
5 |
from langchain.vectorstores import FAISS
|
6 |
from langchain.llms import HuggingFaceHub
|
|
|
21 |
|
22 |
loader = OnlinePDFLoader(pdf_doc.name)
|
23 |
pages = loader.load_and_split()
|
24 |
+
text_splitter = CharacterTextSplitter(
|
25 |
+
chunk_size=350,
|
26 |
+
chunk_overlap=0,
|
|
|
27 |
)
|
28 |
docs = text_splitter.split_documents(pages)
|
29 |
embeddings = HuggingFaceHubEmbeddings()
|
30 |
db = FAISS.from_documents(docs, embeddings)
|
31 |
+
llm = HuggingFaceHub(repo_id="google/flan-ul2", model_kwargs={"temperature":0.1, "max_new_tokens":300})
|
|
|
32 |
global qa
|
33 |
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=db.as_retriever())
|
34 |
return "Ready"
|
|
|
37 |
db = FAISS.load_local( book , embeddings = HuggingFaceHubEmbeddings() )
|
38 |
llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.1, "max_new_tokens":250})
|
39 |
global qa
|
40 |
+
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=db.as_retriever(), return_source_documents=True)
|
41 |
return "Ready"
|
42 |
|
43 |
|