app.py

import streamlit as st
from langchain_groq import ChatGroq
from langchain_ollama import ChatOllama
from langchain_community.document_loaders import PyMuPDFLoader
from langchain.schema import Document
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from pydantic import BaseModel, Field
import os
import json
from prompts import REAG_SYSTEM_PROMPT, rag_prompt

# Hugging Face Spaces API Key Handling
st.set_page_config(page_title="ReAG", layout="centered")

# Set API Key using Hugging Face Secrets
os.environ["GROQ_API_KEY"] = st.secrets["GROQ_API_KEY"]

# Initialize LLM models
llm_relevancy = ChatGroq(model="llama-3.3-70b-versatile", temperature=0)
llm = ChatOllama(model="deepseek-r1:14b", temperature=0.6, max_tokens=3000)

# Define schema for extracted content
class ResponseSchema(BaseModel):
    content: str = Field(..., description="Relevant content from the document")
    reasoning: str = Field(..., description="Why this content was selected")
    is_irrelevant: bool = Field(..., description="True if the content is irrelevant")

class RelevancySchemaMessage(BaseModel):
    source: ResponseSchema

relevancy_parser = JsonOutputParser(pydantic_object=RelevancySchemaMessage)

# Function to format document
def format_doc(doc: Document) -> str:
    return f"Document_Title: {doc.metadata.get('title', 'Unknown')}\nPage: {doc.metadata.get('page', 'Unknown')}\nContent: {doc.page_content}"

# Extract relevant context function
def extract_relevant_context(question,documents):
    result = []
    for doc in documents:
        formatted_documents = format_doc(doc)
        system = f"{REAG_SYSTEM_PROMPT}\n\n# Available source\n\n{formatted_documents}"
        prompt = f"""Determine if the 'Avaiable source' content supplied is sufficient and relevant to ANSWER the QUESTION asked.
        QUESTION: {question}
        #INSTRUCTIONS TO FOLLOW
        1. Analyze the context provided thoroughly to check its relevancy to help formulizing a response for the QUESTION asked.
        2, STRICTLY PROVIDE THE RESPONSE IN A JSON STRUCTURE AS DESCRIBED BELOW:
            ```json
               {{"content":<<The page content of the document that is relevant or sufficient to answer the question asked>>,
                 "reasoning":<<The reasoning for selecting The page content with respect to the question asked>>,
                 "is_irrelevant":<<Specify 'True' if the content in the document is not sufficient or relevant.Specify 'False' if the page content is sufficient to answer the QUESTION>>
                 }}
            ```
         """
        messages =[ {"role": "system", "content": system},
                       {"role": "user", "content": prompt},
                    ]
        response = llm_relevancy.invoke(messages)
        print(response.content)
        formatted_response = relevancy_parser.parse(response.content)
        result.append(formatted_response)
    final_context = []
    for items in result:
        if (items['is_irrelevant'] == False) or ( items['is_irrelevant'] == 'false') or (items['is_irrelevant'] == 'False'):
            final_context.append(items['content'])
    return final_context

# Generate response using RAG Prompt
def generate_response(question, final_context):
    with st.spinner("📝 Generating Response..."):
        prompt = PromptTemplate(template=rag_prompt, input_variables=["question", "context"])
        chain = prompt | llm
        response = chain.invoke({"question": question, "context": final_context})
        return response.content.split("\n\n")[-1]

# Streamlit UI
st.title("📚 ReAG")
st.markdown("Upload a PDF and ask questions based on its content.")

uploaded_file = st.file_uploader("📂 Upload PDF", type=["pdf"])
if uploaded_file:
    with st.spinner("📥 Uploading and processing PDF..."):
        file_path = f"/tmp/{uploaded_file.name}"  
        with open(file_path, "wb") as f:
            f.write(uploaded_file.getbuffer())
        
        loader = PyMuPDFLoader(file_path)
        docs = loader.load()
        st.success("✅ PDF uploaded and processed successfully!")

        question = st.text_input("❓ Ask a question about the document:")
        if st.button("🚀 Get Answer"):
            if question:
                final_context = extract_relevant_context(question, docs)
                if final_context:
                    answer = generate_response(question, final_context)
                    st.success(f"🧠 **Response:**\n\n{answer}")
                else:
                    st.warning("⚠️ No relevant information found in the document.")