Create app.py

app.py

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+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()