app.py

app.py

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+# 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()