Delete app.py

app.py

@@ -1,82 +0,0 @@
-# Import required libraries
-import PyPDF2
-from getpass import getpass
-from haystack.nodes import PreProcessor, PromptModel, PromptTemplate, PromptNode, AnswerParser
-from haystack.document_stores import InMemoryDocumentStore
-from haystack import Document, Pipeline
-from haystack.nodes import BM25Retriever
-from pprint import pprint
-import streamlit as st
-import logging
-from dotenv import load_dotenv
-load_dotenv()
-
-import logging
-logging.basicConfig(level=logging.DEBUG)
-
-# Function to extract text from a PDF
-def extract_text_from_pdf(pdf_path):
-    text = ""
-    with open(pdf_path, "rb") as pdf_file:
-        pdf_reader = PyPDF2.PdfReader(pdf_file)
-        for page_num in range(len(pdf_reader.pages)):
-            page = pdf_reader.pages[page_num]
-            text += page.extract_text() or ""
-    return text
-
-# Extract text from the PDF file
-pdf_file_path = "Data/MR. MPROFY.pdf"
-pdf_text = extract_text_from_pdf(pdf_file_path)
-if not pdf_text:
-    raise ValueError("No text extracted from PDF.")
-
-# Create a Haystack document
-doc = Document(content=pdf_text, meta={"name": "MR. MPROFY"})
-
-# Initialize Document Store
-document_store = InMemoryDocumentStore(use_bm25=True)
-document_store.write_documents([doc])
-
-# Initialize Retriever
-retriever = BM25Retriever(document_store=document_store, top_k=2)
-
-# Define QA Template
-qa_template = PromptTemplate(
-    prompt="""
-    Hi, I'm Mprofier, your friendly AI assistant. I'm here to provide direct and concise answers to your specific questions. 
-    I won’t ask any follow-up questions myself. 
-    If I can't find the answer in the provided context, I'll simply state that I don't have enough information to answer.
-    Context: {join(documents)}; 
-    Question: {query}
-    Answer:
-    """,
-    output_parser=AnswerParser()
-)
-
-# Initialize Prompt Node
-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}
-)
-
-# Build Pipeline
-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"])
-
-# Streamlit Function for Handling Input and Displaying Output
-def run_streamlit_app():
-    st.title("Mprofier - AI Assistant")
-    query_text = st.text_input("Enter your question:")
-    
-    if st.button("Get Answer"):
-        response = rag_pipeline.run(query=query_text)
-        answer = response["answers"][0].answer if response["answers"] else "No answer found."
-        st.write(answer)
-
-# Start the Streamlit application
-if __name__ == "__main__":
-    run_streamlit_app()