app.py

import streamlit as st
import os
from PIL import Image
import pytesseract
from pdf2image import convert_from_path
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain_groq import ChatGroq
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.vectorstores import VectorStoreRetriever

# Initialize the Groq API Key and the model
os.environ["GROQ_API_KEY"] = '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'
# config = {'max_new_tokens': 512, 'context_length': 8000}
llm = ChatGroq(
    model='llama3-70b-8192',
    temperature=0.5,
    max_tokens=None,
    timeout=None,
    max_retries=2
)

# Define OCR functions for image and PDF files
def ocr_image(image_path, language='eng+guj'):
    img = Image.open(image_path)
    text = pytesseract.image_to_string(img, lang=language)
    return text

def ocr_pdf(pdf_path, language='eng+guj'):
    images = convert_from_path(pdf_path)
    all_text = ""
    for img in images:
        text = pytesseract.image_to_string(img, lang=language)
        all_text += text + "\n"
    return all_text

def ocr_file(file_path):
    file_extension = os.path.splitext(file_path)[1].lower()

    if file_extension == ".pdf":
        text_re = ocr_pdf(file_path, language='guj+eng')
    elif file_extension in [".jpg", ".jpeg", ".png", ".bmp"]:
        text_re = ocr_image(file_path, language='guj+eng')
    else:
        raise ValueError("Unsupported file format. Supported formats are PDF, JPG, JPEG, PNG, BMP.")

    return text_re

def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
    chunks = text_splitter.split_text(text)
    return chunks

# Function to create or update the vector store
def get_vector_store(text_chunks):
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
    
    # Ensure the directory exists before saving the vector store
    os.makedirs("faiss_index", exist_ok=True)
    vector_store.save_local("faiss_index")
    
    return vector_store

# Function to process multiple files and extract vector store
def process_ocr_and_pdf_files(file_paths):
    raw_text = ""
    for file_path in file_paths:
        raw_text += ocr_file(file_path) + "\n"
    text_chunks = get_text_chunks(raw_text)
    return get_vector_store(text_chunks)

# embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-MiniLM-L6-v2", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
# new_vector_store = FAISS.load_local(
#     "faiss_index", embeddings, allow_dangerous_deserialization=True
# )

# docs = new_vector_store.similarity_search("qux")
# Conversational chain for Q&A
def get_conversational_chain():
    template = """You are an intelligent educational assistant specialized in handling queries about documents. You have been provided with OCR-processed text from the uploaded files that contains important educational information.

Core Responsibilities:
1. Language Processing:
   - Identify the language of the user's query (English or Gujarati)
   - Respond in the same language as the query
   - If the query is in Gujarati, ensure the response maintains proper Gujarati grammar and terminology
   - For technical terms, provide both English and Gujarati versions when relevant

2. Document Understanding:
   - Analyze the OCR-processed text from the uploaded files
   - Account for potential OCR errors or misinterpretations
   - Focus on extracting accurate information despite possible OCR imperfections

3. Response Guidelines:
   - Provide direct, clear answers based solely on the document content
   - If information is unclear due to OCR quality, mention this limitation
   - For numerical data (dates, percentages, marks), double-check accuracy before responding
   - If information is not found in the documents, clearly state: "This information is not present in the uploaded documents"

4. Educational Context:
   - Maintain focus on educational queries related to the document content
   - For admission-related queries, emphasize important deadlines and requirements
   - For scholarship information, highlight eligibility criteria and application processes
   - For course-related queries, provide detailed, accurate information from the documents

5. Response Format:
   - Structure responses clearly with relevant subpoints when necessary
   - For complex information, break down the answer into digestible parts
   - Include relevant reference points from the documents when applicable
   - Format numerical data and dates clearly

6. Quality Control:
   - Verify that responses align with the document content
   - Don't make assumptions beyond the provided information
   - If multiple interpretations are possible due to OCR quality, mention all possibilities
   - Maintain consistency in terminology throughout the conversation

Important Rules:
- Never make up information not present in the documents
- Don't combine information from previous conversations or external knowledge
- Always indicate if certain parts of the documents are unclear due to OCR quality
- Maintain professional tone while being accessible to students and parents
- If the query is out of scope of the uploaded documents, politely redirect to relevant official sources

Context from uploaded documents:
{context}

Chat History:
{history}

Current Question: {question}
Assistant: Let me provide a clear and accurate response based on the uploaded documents...
"""
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-MiniLM-L6-v2", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
    new_vector_store = FAISS.load_local(
        "faiss_index", embeddings, allow_dangerous_deserialization=True
    )
    QA_CHAIN_PROMPT = PromptTemplate(input_variables=["history", "context", "question"], template=template)
    qa_chain = RetrievalQA.from_chain_type(llm, retriever=new_vector_store.as_retriever(), chain_type='stuff', verbose=True, chain_type_kwargs={"verbose": True,"prompt": QA_CHAIN_PROMPT,"memory": ConversationBufferMemory(memory_key="history",input_key="question"),})
    return qa_chain
     

def user_input(user_question):
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
    docs = new_db.similarity_search(user_question)
    chain = get_conversational_chain()
    response = chain({"input_documents": docs, "query": user_question}, return_only_outputs=True)
    result = response.get("result", "No result found")
    
    # Save the question and answer to session state for history tracking
    if 'conversation_history' not in st.session_state:
        st.session_state.conversation_history = []
    
    # Append new question and response to the history
    st.session_state.conversation_history.append({'question': user_question, 'answer': result})
    
    return result

# Streamlit app to upload files and interact with the Q&A system
def main():
    st.title("File Upload and OCR Processing")
    st.write("Upload up to 5 files (PDF, JPG, JPEG, PNG, BMP)")

    uploaded_files = st.file_uploader("Choose files", type=["pdf", "jpg", "jpeg", "png", "bmp"], accept_multiple_files=True)

    if len(uploaded_files) > 0:
        file_paths = []

        # Save uploaded files and process them
        for uploaded_file in uploaded_files[:5]:  # Limit to 5 files
            file_path = os.path.join("temp", uploaded_file.name)
            os.makedirs(os.path.dirname(file_path), exist_ok=True)
            with open(file_path, "wb") as f:
                f.write(uploaded_file.getbuffer())
            file_paths.append(file_path)

        # Process the OCR and PDF files and store the vector data
        st.write("Processing files...")
        vector_store = process_ocr_and_pdf_files(file_paths)
        st.write("Processing completed! The vector store has been updated.")

        # Ask user for a question related to the documents
        user_question = st.text_input("Ask a question related to the uploaded documents:")

        if user_question:
            response = user_input(user_question)
            st.write("Answer:", response)

            # Button to display chat history

            # if st.button("Show Chat History"):
            #     history = st.session_state.get('history', [])
            #     if history:
            #         st.write("Conversation History:")
            #         for idx, (q, a) in enumerate(history):
            #             st.write(f"Q{idx+1}: {q}")
            #             st.write(f"A{idx+1}: {a}")
            #     else:
            #         st.write("No conversation history.")
            with st.expander('Conversation History'):
                for entry in st.session_state.conversation_history:
                    st.info(f"Q: {entry['question']}\nA: {entry['answer']}")
    

if __name__ == "__main__":
    main()