Update app.py

app.py

@@ -4,6 +4,7 @@ from tempfile import NamedTemporaryFile
 from langchain.document_loaders import PyPDFLoader
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import Chroma
+from langchain import PromptTemplate, LLMChain
 from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
 
 # Function to save the uploaded PDF to a temporary file
@@ -27,19 +28,14 @@ if uploaded_file is not None:
     embed = HuggingFaceEmbeddings()
     db = Chroma.from_documents(pages, embed)
 
-    # Initialize the GPT-4 model
-    llm = GPT4All(
-        model="./gpt4all-converted.bin"
-    )
-
     # Define a function to get answers
     def get_answer(question):
         doc = db.similarity_search(question, k=4)
         context = doc[0].page_content + doc[1].page_content + doc[2].page_content + doc[3].page_content
 
-        max_seq_length = 512  # You may define this based on your model
+        #max_seq_length = 512  # You may define this based on your model
 
-        context = context[:max_seq_length]
+        #context = context[:max_seq_length]
 
         # Load the model & tokenizer for question-answering
         model_name = "deepset/roberta-base-squad2"