Create app.py

app.py

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+import streamlit as st
+from streamlit_option_menu import option_menu
+from langchain_community.document_loaders import PyPDFLoader, WebBaseLoader
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_chroma import Chroma
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+import os
+import bs4
+import speech_recognition as sr
+from sqlalchemy import create_engine
+import pandas as pd
+import requests
+
+# Set page config
+st.set_page_config(page_title='🤖 GRASP', layout='wide', initial_sidebar_state='expanded')
+
+# Custom CSS for styling
+st.markdown("""
+    <style>
+        body {
+            font-family: 'Roboto', sans-serif;
+            background-color: #E8F6F3;
+        }
+        .stButton>button {
+            background-color: #4CAF50;
+            color: white;
+            border: none;
+            padding: 10px 20px;
+            font-size: 16px;
+            border-radius: 5px;
+        }
+        .stButton>button:hover {
+            background-color: #45A049;
+        }
+        .stTextInput>div>div>input {
+            border-radius: 5px;
+            border: 1px solid #ccc;
+            padding: 10px;
+        }
+        .stSidebar>div>div>div>div {
+            background-color: #E8F6F3;
+        }
+        .stFileUploader>label>div>div>button {
+            background-color: #4CAF50;
+            color: white;
+            border: none;
+            padding: 10px 20px;
+            font-size: 16px;
+            border-radius: 5px;
+        }
+        .stFileUploader>label>div>div>button:hover {
+            background-color: #45A049;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Get API key from the user
+st.sidebar.header("API Key")
+api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="password")
+os.environ['OPENAI_API_KEY'] = api_key
+
+def home_page():
+    st.markdown("<h1 style='text-align: center;'> 🤖 Generative Retrieval Augmented Search Platform</h1>",unsafe_allow_html=True)
+    st.header("Welcome to GRASP")
+    st.subheader("Explore and learn about each RAG method on their respective pages.")
+
+def pdf_rag_page():
+    st.title('PDF RAG 📄')
+    st.sidebar.header("Upload your PDF")
+    uploaded_file = st.sidebar.file_uploader("Choose a PDF file", type=["pdf"])
+
+    st.write("### Instructions 📜")
+    st.write("""
+        1. Upload a PDF file using the sidebar.
+        2. Enter your query in the text input below.
+        3. Enter your OpenAI API Key in the sidebar.
+        4. Click the 'Get Results' button to process the PDF and provide relevant answers based on the content.
+    """)
+
+    def load_and_process_pdf(file):
+        with open(file.name, "wb") as f:
+            f.write(file.getbuffer())
+        loader = PyPDFLoader(file.name)
+        docs = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        splits = text_splitter.split_documents(docs)
+        vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
+        retriever = vectorstore.as_retriever()
+        system_prompt = (
+            "You are an assistant for question-answering tasks. "
+            "Use the following pieces of retrieved context to answer "
+            "the question. If you don't know the answer, say that you "
+            "don't know. Use three sentences maximum and keep the "
+            "answer concise."
+            "\n\n"
+            "{context}"
+        )
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", system_prompt),
+            ("human", "{input}"),
+        ])
+        llm = ChatOpenAI(model="gpt-3.5-turbo-0125", api_key=api_key)
+        question_answer_chain = create_stuff_documents_chain(llm, prompt)
+        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+        return rag_chain
+
+    rag_chain = None
+    if uploaded_file:
+        rag_chain = load_and_process_pdf(uploaded_file)
+
+    input_text = st.text_input("Please feel free to ask any doubts! 📝")
+
+    if st.button("Get Results"):
+        if input_text and rag_chain:
+            with st.spinner('Processing...'):
+                try:
+                    response = rag_chain.invoke({"input": input_text})
+                    st.write(response["answer"])
+                except Exception as e:
+                    st.error(f"An error occurred: {e}")
+        elif input_text:
+            st.warning("Please upload a PDF file to ask questions.")
+
+def web_rag_page():
+    st.title('Web RAG 🌐')
+    st.sidebar.header("Enter a web URL")
+    url = st.sidebar.text_input("URL")
+
+    st.write("### Instructions 📜")
+    st.write("""
+        1. Enter a web URL using the sidebar.
+        2. Enter your query in the text input below.
+        3. Enter your OpenAI API Key in the sidebar.
+        4. Click the 'Get Results' button to process the webpage content and provide relevant answers based on the content.
+    """)
+
+    def load_and_process_web(url):
+        loader = WebBaseLoader(web_paths=(url,), bs_kwargs=dict(parse_only=bs4.SoupStrainer(class_=("mw-body-content", "mw-headline"))))
+        documents = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200, add_start_index=True)
+        all_splits = text_splitter.split_documents(documents)
+        vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
+        retriever = vectorstore.as_retriever()
+        system_prompt = (
+            "You are an assistant for question-answering tasks. "
+            "Use the following pieces of retrieved context to answer "
+            "the question. If you don't know the answer, say that you "
+            "don't know. Use three sentences maximum and keep the "
+            "answer concise."
+            "\n\n"
+            "{context}"
+        )
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", system_prompt),
+            ("human", "{input}"),
+        ])
+        llm = ChatOpenAI(model="gpt-3.5-turbo-0125", api_key=api_key)
+        question_answer_chain = create_stuff_documents_chain(llm, prompt)
+        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+        return rag_chain
+
+    rag_chain = None
+    if url:
+        rag_chain = load_and_process_web(url)
+
+    input_text = st.text_input("Please feel free to ask any doubts! 📝")
+
+    if st.button("Get Results"):
+        if input_text and rag_chain:
+            with st.spinner('Processing...'):
+                try:
+                    response = rag_chain.invoke({"input": input_text})
+                    st.write(response["answer"])
+                except Exception as e:
+                    st.error(f"An error occurred: {e}")
+        elif input_text:
+            st.warning("Please enter a URL to ask questions.")
+
+def text_document_rag_page():
+    st.title('Text Document RAG 📄')
+    st.sidebar.header("Upload your Text Document")
+    uploaded_file = st.sidebar.file_uploader("Choose a text file", type=["txt"])
+
+    st.write("### Instructions 📜")
+    st.write("""
+        1. Upload a text file using the sidebar.
+        2. Enter your query in the text input below.
+        3. Enter your OpenAI API Key in the sidebar.
+        4. Click the 'Get Results' button to process the text content and provide relevant answers based on the content.
+    """)
+
+    def load_and_process_text(file):
+        content = file.read().decode('utf-8')
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        splits = text_splitter.split_text(content)
+        vectorstore = Chroma.from_texts(texts=splits, embedding=OpenAIEmbeddings())
+        retriever = vectorstore.as_retriever()
+        system_prompt = (
+            "You are an assistant for question-answering tasks. "
+            "Use the following pieces of retrieved context to answer "
+            "the question. If you don't know the answer, say that you "
+            "don't know. Use three sentences maximum and keep the "
+            "answer concise."
+            "\n\n"
+            "{context}"
+        )
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", system_prompt),
+            ("human", "{input}"),
+        ])
+        llm = ChatOpenAI(model="gpt-3.5-turbo-0125", api_key=api_key)
+        question_answer_chain = create_stuff_documents_chain(llm, prompt)
+        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+        return rag_chain
+
+    rag_chain = None
+    if uploaded_file:
+        rag_chain = load_and_process_text(uploaded_file)
+
+    input_text = st.text_input("Please feel free to ask any doubts! 📝")
+
+    if st.button("Get Results"):
+        if input_text and rag_chain:
+            with st.spinner('Processing...'):
+                try:
+                    response = rag_chain.invoke({"input": input_text})
+                    st.write(response["answer"])
+                except Exception as e:
+                    st.error(f"An error occurred: {e}")
+        elif input_text:
+            st.warning("Please upload a text file to ask questions.")
+
+def audio_rag_page():
+    st.title('Audio RAG 🎤')
+    st.sidebar.header("Upload your Audio")
+    uploaded_file = st.sidebar.file_uploader("Choose an audio file", type=["wav"])
+
+    st.write("### Instructions 📜")
+    st.write("""
+        1. Ensure your audio file is in a supported format (PCM WAV, AIFF/AIFF-C, or Native FLAC).
+        2. Upload an audio file using the sidebar.
+        3. Enter your query in the text input below.
+        4. Enter your OpenAI API Key in the sidebar.
+        5. Click the 'Get Results' button to process the text extracted from the audio and provide relevant answers based on the content.
+    """)
+
+    def load_and_process_audio(file):
+        recognizer = sr.Recognizer()
+        try:
+            audio_file = sr.AudioFile(file)
+        except ValueError:
+            st.error("Audio file could not be read as PCM WAV, AIFF/AIFF-C, or Native FLAC; check if file is corrupted or in another format.")
+            return None
+        with audio_file as source:
+            audio = recognizer.record(source)
+        text = recognizer.recognize_google(audio)
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        splits = text_splitter.split_text(text)
+        vectorstore = Chroma.from_texts(texts=splits, embedding=OpenAIEmbeddings())
+        retriever = vectorstore.as_retriever()
+        system_prompt = (
+            "You are an assistant for question-answering tasks. "
+            "Use the following pieces of retrieved context to answer "
+            "the question. If you don't know the answer, say that you "
+            "don't know. Use three sentences maximum and keep the "
+            "answer concise."
+            "\n\n"
+            "{context}"
+        )
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", system_prompt),
+            ("human", "{input}"),
+        ])
+        llm = ChatOpenAI(model="gpt-3.5-turbo-0125", api_key=api_key)
+        question_answer_chain = create_stuff_documents_chain(llm, prompt)
+        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+        return rag_chain
+
+    rag_chain = None
+    if uploaded_file:
+        rag_chain = load_and_process_audio(uploaded_file)
+
+    input_text = st.text_input("Please feel free to ask any doubts! 📝")
+
+    if st.button("Get Results"):
+        if input_text and rag_chain:
+            with st.spinner('Processing...'):
+                try:
+                    response = rag_chain.invoke({"input": input_text})
+                    st.write(response["answer"])
+                except Exception as e:
+                    st.error(f"An error occurred: {e}")
+        elif input_text:
+            st.warning("Please upload an audio file to ask questions.")
+
+def database_rag_page():
+    st.title('Database RAG 🗄️')
+    st.sidebar.header("Enter Database Credentials")
+    db_url = st.sidebar.text_input("Database URL")
+    table_name = st.sidebar.text_input("Table Name")
+
+    st.write("### Instructions 📜")
+    st.write("""
+        1. Enter the database URL and table name using the sidebar.
+        2. Enter your query in the text input below.
+        3. Enter your OpenAI API Key in the sidebar.
+        4. Click the 'Get Results' button to process the data from the specified table and provide relevant answers based on the content.
+    """)
+
+    def load_and_process_db(db_url, table_name):
+        engine = create_engine(db_url)
+        df = pd.read_sql_table(table_name, engine)
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        splits = text_splitter.split_text(df.to_string())
+        vectorstore = Chroma.from_texts(texts=splits, embedding=OpenAIEmbeddings())
+        retriever = vectorstore.as_retriever()
+        system_prompt = (
+            "You are an assistant for question-answering tasks. "
+            "Use the following pieces of retrieved context to answer "
+            "the question. If you don't know the answer, say that you "
+            "don't know. Use three sentences maximum and keep the "
+            "answer concise."
+            "\n\n"
+            "{context}"
+        )
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", system_prompt),
+            ("human", "{input}"),
+        ])
+        llm = ChatOpenAI(model="gpt-3.5-turbo-0125", api_key=api_key)
+        question_answer_chain = create_stuff_documents_chain(llm, prompt)
+        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+        return rag_chain
+
+    rag_chain = None
+    if db_url and table_name:
+        rag_chain = load_and_process_db(db_url, table_name)
+
+    input_text = st.text_input("Please feel free to ask any doubts! 📝")
+
+    if st.button("Get Results"):
+        if input_text and rag_chain:
+            with st.spinner('Processing...'):
+                try:
+                    response = rag_chain.invoke({"input": input_text})
+                    st.write(response["answer"])
+                except Exception as e:
+                    st.error(f"An error occurred: {e}")
+        elif input_text:
+            st.warning("Please enter database credentials to ask questions.")
+
+def api_rag_page():
+    st.title('API RAG 🔌')
+    st.sidebar.header("Enter API Endpoint")
+    api_url = st.sidebar.text_input("API URL")
+
+    st.write("### Instructions 📜")
+    st.write("""
+        1. Enter the API URL using the sidebar.
+        2. Enter your query in the text input below.
+        3. Enter your OpenAI API Key in the sidebar.
+        4. Click the 'Get Results' button to process the data from the specified API endpoint and provide relevant answers based on the content.
+    """)
+
+    def load_and_process_api(api_url):
+        response = requests.get(api_url)
+        data = response.json()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        splits = text_splitter.split_text(str(data))
+        vectorstore = Chroma.from_texts(texts=splits, embedding=OpenAIEmbeddings())
+        retriever = vectorstore.as_retriever()
+        system_prompt = (
+            "You are an assistant for question-answering tasks. "
+            "Use the following pieces of retrieved context to answer "
+            "the question. If you don't know the answer, say that you "
+            "don't know. Use three sentences maximum and keep the "
+            "answer concise."
+            "\n\n"
+            "{context}"
+        )
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", system_prompt),
+            ("human", "{input}"),
+        ])
+        llm = ChatOpenAI(model="gpt-3.5-turbo-0125", api_key=api_key)
+        question_answer_chain = create_stuff_documents_chain(llm, prompt)
+        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+        return rag_chain
+
+    rag_chain = None
+    if api_url:
+        rag_chain = load_and_process_api(api_url)
+
+    input_text = st.text_input("Please feel free to ask any doubts! 📝")
+
+    if st.button("Get Results"):
+        if input_text and rag_chain:
+            with st.spinner('Processing...'):
+                try:
+                    response = rag_chain.invoke({"input": input_text})
+                    st.write(response["answer"])
+                except Exception as e:
+                    st.error(f"An error occurred: {e}")
+        elif input_text:
+            st.warning("Please enter an API URL to ask questions.")
+
+# Extend the navigation menu
+with st.sidebar:
+    selected = option_menu(
+        "🤖 GRASP",
+        ["Home", "PDF RAG 📄", "Web RAG 🌐", "Text Document RAG 📄", "Audio RAG 🎤", "Database RAG 🗄️", "API RAG 🔌"],
+        icons=["house", "file-earmark-pdf", "globe", "file-earmark-text", "mic", "database", "plug"],
+        default_index=0,
+        styles={
+            "container": {"padding": "5px"},
+            "nav-link": {"font-size": "16px", "text-align": "left", "margin": "0px"},
+            "nav-link-selected": {"background-color": "#4CAF50"},
+        }
+    )
+
+# Display the selected page
+if selected == "Home":
+    home_page()
+elif selected == "PDF RAG 📄":
+    pdf_rag_page()
+elif selected == "Web RAG 🌐":
+    web_rag_page()
+elif selected == "Text Document RAG 📄":
+    text_document_rag_page()
+elif selected == "Audio RAG 🎤":
+    audio_rag_page()
+elif selected == "Database RAG 🗄️":
+    database_rag_page()
+elif selected == "API RAG 🔌":
+    api_rag_page()
+
+# Additional User Feedback Section
+st.sidebar.header("User Feedback")
+feedback = st.sidebar.text_area("Provide your feedback here:")
+if st.sidebar.button("Submit Feedback"):
+    with open("feedback.txt", "a") as f:
+        f.write(f"Feedback: {feedback}\n")
+    st.sidebar.success("Feedback submitted successfully!")