Create app.py

app.py

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+import os
+import time
+from dotenv import load_dotenv
+from groq import Groq
+from langchain_community.document_loaders import PyPDFLoader, WebBaseLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+import streamlit as st
+
+
+def load_docs_from_url(input_url):
+    web_loader = WebBaseLoader(input_url)
+    documents = web_loader.load()
+    splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20)
+    split_documents = splitter.split_documents(documents)
+    st.write('Documents Loaded from URL')
+    return split_documents
+
+def load_docs_from_pdf(file):
+    start = time.time()
+    with open("temp.pdf", "wb") as temp_file:
+        temp_file.write(file.getbuffer())
+    pdf_loader = PyPDFLoader("temp.pdf")
+    pdf_documents = pdf_loader.load()
+    splitter = RecursiveCharacterTextSplitter(chunk_size=3000, chunk_overlap=150)
+    processed_documents = splitter.split_documents(pdf_documents)
+    st.write('Documents Loaded')
+    st.write(f"Time taken to load documents: {time.time() - start:.2f} seconds")
+    os.remove("temp.pdf")
+    return processed_documents
+
+def build_vector_db(documents):
+    start = time.time()
+    embedder = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"trust_remote_code": True})
+    db = FAISS.from_documents(documents, embedder)
+    st.write('DB is ready')
+    st.write(f"Time taken to create DB: {time.time() - start:.2f} seconds")
+    return db
+
+def call_groq_api(chat_messages):
+    load_dotenv()
+    api_key = os.getenv("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")
+    if not api_key:
+        st.error("GROQ API key not found. Please set it in your .env file.")
+        return "API key missing."
+
+    groq_client = Groq(api_key=api_key)
+    full_response = ''
+    stream_output = groq_client.chat.completions.create(
+        model="llama3-70b-8192",
+        messages=chat_messages,
+        max_tokens=2048,
+        temperature=1.0,
+        stream=True,
+    )
+
+    for chunk in stream_output:
+        part = chunk.choices[0].delta.content
+        if part:
+            full_response += part
+    return full_response
+
+def summarize_conversation(history):
+    history_text = " ".join([f"{entry['role']}: {entry['content']}" for entry in history])
+    summary_prompt = f"Summarize the following chat history:\n\n{history_text}"
+    summary_messages = [{'role': 'system', 'content': 'You are very good at summarizing the chat between User and Assistant'}]
+    summary_messages.append({'role': 'user', 'content': summary_prompt})
+    return call_groq_api(summary_messages)
+
+def main():
+    st.set_page_config(page_title='Chatbot')
+    st.title("Document Chatbot using RAGs - Harsh Tripathi and Shubham Soni")
+
+    with st.expander("Instructions to upload Text PDF/URL"):
+        st.write("1. Open the sidebar by clicking the menu icon in the top-left corner.")
+        st.write("2. If you're uploading a PDF, click 'Upload PDF', choose your file, and wait until you see the 'Documents Loaded' message.")
+        st.write("3. If you're using a web URL, type it in, click 'Enter Web URL', then hit 'Process URL' and wait for the 'Documents Loaded from URL' confirmation.")
+        st.write("4. Once the documents are loaded, click 'Create Vector Store' to begin processing.")
+        st.write("5. Type your question into the text box and click Submit.")
+        st.write("6. Click 'Generate Chat Summary' to view a summary of the entire chat session.")
+
+    # Session reset button
+    if st.sidebar.button("Reset Session"):
+        for key in st.session_state.keys():
+            del st.session_state[key]
+        st.experimental_rerun()
+
+    st.sidebar.subheader("Choose document source:")
+    input_choice = st.sidebar.radio("Select one:", ("Upload PDF", "Enter Web URL"))
+
+    # Session state initialization
+    if "loaded_docs" not in st.session_state:
+        st.session_state.loaded_docs = None
+    if "document_db" not in st.session_state:
+        st.session_state.document_db = None
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = []
+    if "query_input" not in st.session_state:
+        st.session_state.query_input = ""
+    if "latest_output" not in st.session_state:
+        st.session_state.latest_output = ""
+    if "chat_summary" not in st.session_state:
+        st.session_state.chat_summary = ""
+
+    if input_choice == "Upload PDF":
+        file_input = st.sidebar.file_uploader("Upload a PDF file", type=["pdf"])
+        if file_input is not None:
+            if st.session_state.loaded_docs is None:
+                with st.spinner("Loading documents..."):
+                    documents = load_docs_from_pdf(file_input)
+                st.session_state.loaded_docs = documents
+
+    elif input_choice == "Enter Web URL":
+        input_url = st.sidebar.text_input("Enter URL", key="url_input")
+        if st.session_state.url_input != input_url:
+            st.session_state.url_input = input_url
+            st.session_state.loaded_docs = None
+        if st.sidebar.button('Process URL'):
+            if input_url and st.session_state.loaded_docs is None:
+                with st.spinner("Fetching and processing documents from URL..."):
+                    documents = load_docs_from_url(input_url)
+                st.session_state.loaded_docs = documents
+
+    if st.session_state.loaded_docs is not None:
+        if st.sidebar.button('Create Vector Store'):
+            with st.spinner("Creating vector store..."):
+                db = build_vector_db(st.session_state.loaded_docs)
+            st.session_state.document_db = db
+
+    # Chat handling logic
+    def submit_with_db():
+        user_input = st.session_state.query_input
+        if user_input:
+            doc_retriever = st.session_state.document_db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+            results = doc_retriever.invoke(user_input)
+            constructed_prompt = f'''
+            You are a helpful assistant. Answer the user's question based on the context below.
+            If the answer is not found in the context, respond with general knowledge.
+
+            Context: {results}
+            Chat History: {st.session_state.conversation}
+            Latest Question: {user_input}
+            '''
+            message_flow = [{'role': 'system', 'content': 'You are a very helpful assistant'}]
+            message_flow.append({'role': 'user', 'content': constructed_prompt})
+
+            try:
+                reply = call_groq_api(message_flow)
+            except Exception as err:
+                st.error(f"Error occurred during chat_groq execution: {str(err)}")
+                reply = "An error occurred while fetching response. Please try again."
+
+            st.session_state.latest_output = reply
+            st.session_state.conversation.append({'role': 'user', 'content': user_input})
+            st.session_state.conversation.append({'role': 'assistant', 'content': reply})
+            st.session_state.query_input = ""
+
+    def handle_general_chat():
+        user_input = st.session_state.query_input
+        if user_input:
+            prompt_text = f'''
+            You are a helpful assistant. Answer the question based on the conversation context.
+
+            Chat History: {st.session_state.conversation}
+            Latest Question: {user_input}
+            '''
+            message_flow = [{'role': 'system', 'content': 'You are a very helpful assistant'}]
+            message_flow.append({'role': 'user', 'content': prompt_text})
+
+            try:
+                reply = call_groq_api(message_flow)
+            except Exception as err:
+                st.error(f"Error occurred during chat_groq execution: {str(err)}")
+                reply = "An error occurred while fetching response. Please try again."
+
+            st.session_state.latest_output = reply
+            st.session_state.conversation.append({'role': 'user', 'content': user_input})
+            st.session_state.conversation.append({'role': 'assistant', 'content': reply})
+            st.session_state.query_input = ""
+
+    st.text_area("Enter your question:", key="query_input")
+    if st.session_state.document_db is not None:
+        st.button('Submit', on_click=submit_with_db)  
+    else:
+        st.button('Submit', on_click=handle_general_chat)
+
+    if st.session_state.latest_output:
+        st.write(st.session_state.latest_output)
+
+    if st.button('Generate Chat Summary'):
+        st.session_state.chat_summary = summarize_conversation(st.session_state.conversation)
+
+    if st.session_state.chat_summary:
+        with st.expander("Chat Summary"):
+            st.write(st.session_state.chat_summary)
+
+    with st.expander("Recent Chat History"):
+        latest_entries = st.session_state.conversation[-8:][::-1]
+        paired_entries = []
+        for i in range(0, len(latest_entries), 2):
+            if i+1 < len(latest_entries):
+                paired_entries.extend([latest_entries[i+1], latest_entries[i]])
+            else:
+                paired_entries.append(latest_entries[i])
+        for entry in paired_entries:
+            st.write(f"{entry['role'].capitalize()}: {entry['content']}")
+
+if __name__ == "__main__":
+    main()