Update app.py

app.py

@@ -1,11 +1,9 @@
 from pprint import pprint
-from haystack import Document, Pipeline
-from haystack.nodes import BM25Retriever
-from haystack.document_stores import InMemoryDocumentStore
-from haystack.nodes import PromptTemplate, PromptNode
+from getpass import getpass
 from PyPDF2 import PdfReader
 import gradio as gr
 import os
+from transformers import pipeline
 
 # Function to read PDF file content directly
 def read_pdf(pdf_path):
@@ -19,38 +17,14 @@ def read_pdf(pdf_path):
 def process_invoice(file, hf_token, questions):
     # Read the PDF content directly
     pdf_content = read_pdf(file.name)
-    document = Document(content=pdf_content)
-    docs = [document]
 
-    document_store = InMemoryDocumentStore(use_bm25=True)
-    document_store.write_documents(docs)
-    retriever = BM25Retriever(document_store, top_k=2)
-
-    qa_template = PromptTemplate(prompt=
-        """ Using exclusively the information contained in the context, answer only the question asked without adding
-        suggestions for possible questions, and respond exclusively in English. If the answer cannot be deduced from the
-        context, Don't add anything from the references if it is not asked explicitly. Do not repeat the same information twice
-        respond: "Not sure because not relevant to the context.
-        Context: {join(documents)};
-        Question: {query}
-        """)
-
-    prompt_node = PromptNode(
-        model_name_or_path='mistralai/Mixtral-8x7B-Instruct-v0.1',
-        api_key=hf_token,
-        default_prompt_template=qa_template,
-        max_length=500,
-        model_kwargs={"model_max_length": 5000}
-    )
-
-    rag_pipeline = Pipeline()
-    rag_pipeline.add_node(component=retriever, name="retriever", inputs=["Query"])
-    rag_pipeline.add_node(component=prompt_node, name="prompt_node", inputs=["retriever"])
+    # Initialize the Hugging Face pipeline
+    qa_pipeline = pipeline("question-answering", model="mistralai/Mixtral-8x7B-Instruct-v0.1", token=hf_token)
 
     answers = {}
     for question in questions.split(','):
-        result = rag_pipeline.run(query=question.strip())
-        answers[question] = result["results"][0].strip()
+        result = qa_pipeline(question=question.strip(), context=pdf_content)
+        answers[question] = result['answer']
 
     return answers