Create app.py

app.py

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+import gradio as gr
+from pdfminer.high_level import extract_text
+from langchain_groq import ChatGroq
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.schema import Document
+from langchain_openai import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_retrieval_chain
+import os
+import markdown2
+
+# Retrieve API keys from HF secrets
+openai_api_key=os.getenv('OPENAI_API_KEY')
+groq_api_key=os.getenv('GROQ_API_KEY')
+google_api_key=os.getenv('GEMINI')
+
+# Initialize API clients with the API keys
+openai_client = ChatOpenAI(model_name="gpt-4o", api_key=openai_api_key)
+groq_client = ChatGroq(model="llama-3.1-70b-versatile", temperature=0, api_key=groq_api_key)
+gemini_client = ChatGoogleGenerativeAI(model="gemini-1.5-pro", api_key=google_api_key)
+
+# Function to extract text from PDF
+def extract_pdf(pdf_path):
+    return extract_text(pdf_path)
+
+# Function to split text into chunks
+def split_text(text):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    return [Document(page_content=t) for t in splitter.split_text(text)]
+
+# Function to generate embeddings and store in vector database
+def generate_embeddings(docs):
+    embeddings = OpenAIEmbeddings(api_key=openai_api_key)
+    return FAISS.from_documents(docs, embeddings)
+
+# Function for query preprocessing and simple HyDE-Lite
+def preprocess_query(query):
+    prompt = ChatPromptTemplate.from_template("""
+    Your role is to optimize user queries for retrieval from a GDPR regulation document.
+    Transform the query into a more affirmative, keyword-focused statement.
+    The transformed query should look like probable related passages in the official document.
+
+    Query: {query}
+
+    Optimized query:
+    """)
+    chain = prompt | openai_client
+    return chain.invoke({"query": query}).content
+
+# Function to create RAG chain with Groq
+def create_rag_chain():
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", "You are an AI assistant helping with GDPR-related queries. Use the following context from the official GDPR regulation document to answer the user's question:\n\n{context}"),
+        ("human", "{input}")
+    ])
+    document_chain = create_stuff_documents_chain(groq_client, prompt)
+    return create_retrieval_chain(vector_store.as_retriever(), document_chain)
+
+# Function for Gemini response with long context
+def gemini_response(query):
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", "You are an AI assistant helping with GDPR-related queries. Use the following full content of the official GDPR regulation document to answer the user's question:\n\n{context}"),
+        ("human", "{input}")
+    ])
+    chain = prompt | gemini_client
+    return chain.invoke({"context": full_pdf_content, "input": query}).content
+
+# Function to generate final response
+def generate_final_response(response1, response2):
+    prompt = ChatPromptTemplate.from_template("""
+    You are an AI assistant helping educators understand and implement AI data protection and GDPR compliance.
+    Your goal is to provide simple, practical explanation of and advice on how to meet GDPR requirements based on the given responses.
+    To do so, analyze the following two responses, combining similar elements and highlighting any differences. This MUST be done 
+    internally as a hidden state. Only output your own final response.
+    If the responses contradict each other on important points, include that in your response.
+    """)
+    chain = prompt | openai_client
+    return chain.invoke({"response1": response1, "response2": response2}).content
+
+def markdown_to_html(content):
+    return markdown2.markdown(content)
+
+def process_query(user_query):
+    preprocessed_query = preprocess_query(user_query)
+    
+    # Get RAG response using Groq 
+    rag_response = rag_chain.invoke({"input": preprocessed_query})["answer"]
+    
+    # Get Gemini response with full PDF content
+    gemini_resp = gemini_response(preprocessed_query)
+    
+    final_response = generate_final_response(rag_response, gemini_resp)
+    html_content = markdown_to_html(final_response)
+    
+    return rag_response, gemini_resp, html_content
+
+# Initialize
+GDPR_PDF_PATH = "/content/GDPR.pdf"
+full_pdf_content = extract_pdf(GDPR_PDF_PATH)
+extracted_text = extract_pdf(GDPR_PDF_PATH)
+documents = split_text(extracted_text)
+vector_store = generate_embeddings(documents)
+rag_chain = create_rag_chain()
+
+# Gradio interface
+iface = gr.Interface(
+    fn=process_query,
+    inputs=gr.Textbox(label="Ask your data protection related question"),
+    outputs=[
+        gr.Textbox(label="RAG Pipeline (Llama3.1) Response"),
+        gr.Textbox(label="Long Context (Gemini 1.5 Pro) Response"),
+        gr.HTML(label="Final (GPT-4o) Response")
+    ],
+    title="Data Protection Team",
+    description="Get responses combining advanced RAG, Long Context, and SOTA models to data protection related questions .",
+    allow_flagging="never" 
+)
+
+iface.launch(debug=True)