INITIAL_COMMIT

requirements.txt

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+langchain
+langchain_openai 
+langchain_core
+python-dotenv
+streamlit
+langchain_community
+langserve
+sse_starlette
+bs4
+pypdf
+chromadb
+faiss-cpu
+groq
+cassio
+langchain-groq
+langchainhub
+sentence_transformers
+PyPDF2
+langchain-objectbox
+pytesseract 
+pdf2image 
+pillow

talk2doc_app.py

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+import os
+import pytesseract
+import streamlit as st
+import time
+
+from dotenv import load_dotenv
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.schema import Document
+from langchain_community.vectorstores import FAISS
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from pdf2image import convert_from_path
+from PyPDF2 import PdfReader
+from io import BytesIO
+
+load_dotenv()
+
+groq_api_key = os.environ["groq"]
+os.environ["LANGCHAIN_TRACING_V2"] = "true"
+os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
+
+st.title("Talk to your Documents!!!")
+st.sidebar.title("Model fine-tuning")
+model = st.sidebar.selectbox("Select the model that you want to use",
+                             ('llama-3.1-8b-instant', 'llama-3.1-70b-versatile', 'llama3-70b-8192',
+                              'llama3-8b-8192', 'mixtral-8x7b-32768', 'gemma2-9b-it', 'gemma-7b-it'))
+temprature = st.sidebar.slider("Temperature", min_value=0., max_value=1., value=0.7)
+tokens = st.sidebar.slider("Tokens", min_value=256, max_value=4096, value=1024)
+
+llm = ChatGroq(groq_api_key=groq_api_key, model_name=model, temperature=temprature, max_tokens=tokens)
+prompt = ChatPromptTemplate.from_template(
+"""
+Answer the questions based on the provided context only.
+Please provide the most accurate response based on the question.
+Be respectful and friendly, and you can use emojis too.
+You do not know anything out of context, and if the question 
+is out of context simply say that you do not know.
+Do not provide output based on your general knowledge.
+The response provided must be more than 256 tokens.
+<context>
+{context}
+<context>
+Questions:{input}
+"""
+)
+
+def ocr_pdf_page(file_path, page_number):
+    images = convert_from_path(file_path, first_page=page_number, last_page=page_number)
+    if images:
+        return pytesseract.image_to_string(images[0])
+    return ""
+
+
+def process_uploaded_pdfs(uploaded_files):
+    documents = []
+    for uploaded_file in uploaded_files:
+        with BytesIO(uploaded_file.read()) as pdf_stream:
+            pdf_reader = PdfReader(pdf_stream)
+            for i, page in enumerate(pdf_reader.pages):
+                text = page.extract_text() or ""
+                if not text.strip():
+                    text = ocr_pdf_page(uploaded_file.name, i + 1)
+                documents.append(Document(page_content=text, metadata={"source": uploaded_file.name, "page": i + 1}))
+    return documents
+
+# def process_uploaded_pdfs(uploaded_files):
+#     documents = []
+#     for uploaded_file in uploaded_files:
+#         with BytesIO(uploaded_file.read()) as pdf_stream:
+#             pdf_reader = PdfReader(pdf_stream)
+#             for i, page in enumerate(pdf_reader.pages):
+#                 text = page.extract_text() or ""
+#                 if not text.strip():
+#                     text = ocr_pdf_page(uploaded_file.name, i + 1)
+#                 documents.append({"page_content": text, "metadata": {"source": uploaded_file.name, "page": i + 1}})
+#     return documents
+
+def vector_embedding_pdfs(uploaded_files):
+    if "vector" not in st.session_state:
+        st.session_state.embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
+        st.session_state.pdf_docs = process_uploaded_pdfs(uploaded_files)
+        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.pdf_docs)
+        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
+        st.session_state.vectors.save_local("./faissDsRagGroq", "index_hf")
+
+uploaded_files = st.file_uploader("Upload PDF documents", type="pdf", accept_multiple_files=True)
+
+if uploaded_files and st.button("Documents Embedding"):
+    start = time.process_time()
+    vector_embedding_pdfs(uploaded_files)
+    end = time.process_time()
+    st.write("Embedding completed!!!")
+    st.write(f"Time taken for generating embeddings: {(end - start):.2f} seconds...")
+
+if st.button("Load vector db"):
+    st.session_state.embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
+    start = time.process_time()
+    st.session_state.vector_db = FAISS.load_local("./faissDsRagGroq", embeddings=st.session_state.embeddings,
+                                                   index_name="index_hf", allow_dangerous_deserialization=True)
+    end = time.process_time()
+    st.write("Embeddings Loaded!!!")
+    st.write(f"Time taken for loading embeddings: {(end - start):.2f} seconds")
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+input_prompt = st.text_input("Enter Your Question From Documents")
+
+if input_prompt:
+    document_chain = create_stuff_documents_chain(llm, prompt)
+    retriever = st.session_state.vector_db.as_retriever()
+    retrieval_chain = create_retrieval_chain(retriever, document_chain)
+
+    start = time.process_time()
+    response = retrieval_chain.invoke({"input": input_prompt})
+    response_time = time.process_time() - start
+
+    st.session_state.chat_history.append({"question": input_prompt, "response": response['answer']})
+
+    st.write(f"Response time: {response_time:.2f} seconds")
+    st.write(response['answer'])
+
+    with st.expander("Document Similarity Search"):
+        for i, doc in enumerate(response["context"]):
+            st.write(doc.page_content)
+            st.write("--------------------------------")
+
+    with st.expander("Chat History"):
+        for chat in st.session_state.chat_history:
+            st.write(f"**Question:** {chat['question']}")
+            st.write(f"**Response:** {chat['response']}")
+            st.write("---")