import streamlit as st from streamlit_chat import message from langchain.chains import ConversationalRetrievalChain from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.memory import ConversationBufferMemory from langchain_core.prompts import PromptTemplate from langchain_community.vectorstores import FAISS import pdfplumber import docx2txt from langchain_community.embeddings import OllamaEmbeddings from langchain_groq import ChatGroq from dotenv import load_dotenv from easygoogletranslate import EasyGoogleTranslate import os import csv import re from io import StringIO import speech_recognition as sr import pygame from threading import Thread from gtts import gTTS import gc import torch os.environ['CUDA_VISIBLE_DEVICES'] = '' torch.set_num_threads(1) load_dotenv() groq_api_key = os.getenv('GROQ_API_KEY') MAX_DOCUMENTS = 5 def initialize_session_state(): if 'history' not in st.session_state: st.session_state['history'] = [] if 'generated' not in st.session_state: st.session_state['generated'] = ["Hello! Ask me anything about 🤗"] if 'past' not in st.session_state: st.session_state['past'] = ["Hey! 👋"] if 'translated' not in st.session_state: st.session_state['translated'] = ["Hello! Ask me anything about 🤗"] if 'translation_requested' not in st.session_state: st.session_state['translation_requested'] = [False] * len(st.session_state['generated']) if 'chain' not in st.session_state: st.session_state['chain'] = None if 'vector_store' not in st.session_state: st.session_state['vector_store'] = None def translate_text(text, target_language='en'): translator = EasyGoogleTranslate(target_language=target_language) try: return translator.translate(text) except Exception as e: st.error(f"Translation error: {e}") return text def clean_text_for_speech(text): # Replacing symbols and formatting text text = re.sub(r'[*_~#|•●■◆▪]', '', text) text = re.sub(r'\n', ' ', text) text = re.sub(r'\s+', ' ', text) text = re.sub(r'([.!?])\s*', r'\1 ', text) text = re.sub(r'[:;]', ' ', text) text = re.sub(r'[-]', ' ', text) text = re.sub(r'[(){}\[\]]', '', text) # Handle numbers and decimals text = re.sub(r'(\d+)\.(\d+)', r'\1 point \2', text) # Make sure to handle numbers correctly replacements = { '&': 'and', '%': 'percent', '$': 'dollars', '€': 'euros', '£': 'pounds', '@': 'at', '#': 'hashtag', 'e.g.': 'for example', 'i.e.': 'that is', 'etc.': 'et cetera', 'vs.': 'versus', 'fig.': 'figure', 'approx.': 'approximately', } for key, value in replacements.items(): text = text.replace(key, value) return text.strip() def text_to_speech(text, language='en', speed=1.0): cleaned_text = clean_text_for_speech(text) tts = gTTS(text=cleaned_text, lang=language, slow=(speed < 1.0)) tts.save("output.mp3") with open("output.mp3", "rb") as audio_file: audio_bytes = audio_file.read() return audio_bytes def conversation_chat(query, chain, history): template = """ You are an expert analyst with deep knowledge across various fields. Your task is to provide an in-depth, comprehensive analysis of the uploaded documents. Approach each question with critical thinking and attention to detail. You are only allowed to answer questions directly related to the content of the uploaded documents. If a question is outside the scope of the documents, respond with: 'I'm sorry, I can only answer questions about the uploaded documents.' Guidelines for Analysis: 1. Document Overview: - Identify the type of document(s) (research paper, report, data set, etc.) - Summarize the main topic and purpose of each document 2. Content Analysis: - For research papers: Analyze the abstract, introduction, methodology, results, discussion, and conclusion - For reports: Examine executive summary, key findings, and recommendations - For data sets: Describe the structure, variables, and any apparent trends 3. Key Points and Findings: - Highlight the most significant information and insights from each document - Identify any unique or surprising elements in the content 4. Contextual Analysis: - Place the information in a broader context within its field - Discuss how this information relates to current trends or issues 5. Critical Evaluation: - Assess the strengths and limitations of the presented information - Identify any potential biases or gaps in the data or arguments 6. Implications and Applications: - Discuss the potential impact of the findings or information - Suggest possible applications or areas for further research 7. Comparative Analysis (if multiple documents): - Compare and contrast information across different documents - Identify any conflicting data or viewpoints 8. Data Interpretation: - For numerical data: Provide clear explanations of statistics or trends - For qualitative information: Offer interpretations of key quotes or concepts 9. Sourcing and Credibility: - Comment on the credibility of the sources (if apparent) - Note any references to other important works in the field 10. Comprehensive Response: - Ensure all aspects of the question are addressed - Provide a balanced view, considering multiple perspectives if applicable Remember to maintain an objective, analytical tone. Your goal is to provide the most thorough and insightful analysis possible based on the available documents. Previous Context: {previous_context} Question: {query} """ prompt = PromptTemplate.from_template(template) result = chain.invoke({"question": query, "chat_history": history}, prompt=prompt) answer = result.get("answer", "I'm sorry, I couldn't generate an answer.") history.append((query, answer)) return answer def display_chat_history(chain): st.write("Chat History:") for i in range(len(st.session_state['past'])): message(st.session_state['past'][i], is_user=True, key=f'{i}_user', avatar_style="avataaars", seed="Aneka") message(st.session_state['generated'][i], key=f'{i}_bot', avatar_style="bottts", seed="Aneka") col1, col2, col3 = st.columns([2, 1, 1]) with col1: dest_language = st.selectbox('Select language for translation:', options=['hi', 'kn'], index=0, key=f'{i}_lang_select') with col2: if st.button(f'Translate Message {i}', key=f'{i}_translate'): translated_text = translate_text(st.session_state['generated'][i], target_language=dest_language) st.session_state['translated'][i] = translated_text st.session_state['translation_requested'][i] = True st.experimental_rerun() with col3: if st.button(f'Play Message {i}', key=f'{i}_play'): audio_bytes = text_to_speech(st.session_state['generated'][i]) st.audio(audio_bytes, format="audio/mp3") if st.session_state['translation_requested'][i]: message(st.session_state['translated'][i], key=f'{i}_bot_translated', avatar_style="bottts", seed="Aneka") if st.button(f'Play Translated Message {i}', key=f'{i}_play_translated'): audio_bytes = text_to_speech(st.session_state['translated'][i], dest_language) st.audio(audio_bytes, format="audio/mp3") with st.form(key='user_input_form'): user_input = st.text_input("Ask questions about your uploaded documents:", key="user_input") submit_button = st.form_submit_button(label='Send') if submit_button and user_input: output = conversation_chat(user_input, chain, st.session_state['history']) st.session_state['past'].append(user_input) st.session_state['generated'].append(output) st.session_state['translated'].append(output) st.session_state['translation_requested'].append(False) st.rerun() def process_file(file): if file.type == "application/pdf": return process_pdf(file) elif file.type == "text/plain": return file.getvalue().decode("utf-8") elif file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document": return docx2txt.process(file) elif file.type == "text/csv": return process_csv(file) else: st.error(f"Unsupported file type: {file.type}") return "" def process_csv(file): text = "" try: file_content = file.getvalue().decode('utf-8') csvfile = StringIO(file_content) reader = csv.reader(csvfile) headers = next(reader, None) if headers: text += f"CSV Headers: {', '.join(headers)}\n\n" for i, row in enumerate(reader, 1): text += f"Row {i}: {' | '.join(row)}\n" text += f"\nTotal rows: {i}\n" except Exception as e: st.error(f"Error reading CSV file: {e}") return text def process_pdf(file): text = "" with pdfplumber.open(file) as pdf: for page_num, page in enumerate(pdf.pages, 1): page_text = page.extract_text() if page_text: text += f"[Page {page_num}]\n{page_text}\n\n" sections = re.findall(r'(?i)(abstract|introduction|methodology|results|discussion|conclusion).*?\n(.*?)(?=\n(?i)(abstract|introduction|methodology|results|discussion|conclusion)|$)', text, re.DOTALL) structured_text = "\n\n".join([f"{section[0].capitalize()}:\n{section[1]}" for section in sections]) return structured_text if structured_text else text def recognize_speech(): recognizer = sr.Recognizer() with sr.Microphone() as source: st.write("Listening... Please speak now.") try: st.info("Listening for up to 10 seconds...") recognizer.adjust_for_ambient_noise(source, duration=1) audio = recognizer.listen(source, timeout=10, phrase_time_limit=5) st.success("Audio captured. Processing...") except sr.WaitTimeoutError: st.warning("No speech detected. Please try again.") return "" try: text = recognizer.recognize_google(audio) st.success(f"You said: {text}") return text except sr.UnknownValueError: st.error("Sorry, I couldn't understand that.") return "" except sr.RequestError as e: st.error(f"Could not request results; {e}") return "" def create_conversational_chain(vector_store): llm = ChatGroq(groq_api_key=groq_api_key, model_name='llama3-70b-8192') memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) chain = ConversationalRetrievalChain.from_llm( llm=llm, chain_type='stuff', retriever=vector_store.as_retriever(search_kwargs={"k": 5}), memory=memory ) return chain def main(): initialize_session_state() st.set_page_config(page_title="DOCS Chatbot & Translator", layout="wide") st.title("Smart Document Tool 🤓") st.sidebar.header("About App:") st.sidebar.write("This app utilizes Streamlit") uploaded_files = st.file_uploader("Upload your Docs", type=["pdf", "txt", "docx", "csv"], accept_multiple_files=True) if uploaded_files: all_text = "" for uploaded_file in uploaded_files[:MAX_DOCUMENTS]: try: all_text += f"File: {uploaded_file.name}\n\n{process_file(uploaded_file)}\n\n" except Exception as e: st.error(f"Error processing file {uploaded_file.name}: {e}") finally: gc.collect() if len(uploaded_files) > MAX_DOCUMENTS: st.warning(f"Only the first {MAX_DOCUMENTS} documents were processed due to memory constraints.") text_splitter = RecursiveCharacterTextSplitter( chunk_size=4000, chunk_overlap=300, length_function=len, separators=["\n\n", "\n", " ", ""] ) text_chunks = text_splitter.split_text(all_text) embeddings = OllamaEmbeddings(model="nomic-embed-text") with st.spinner('Analyzing Document...'): vector_store = FAISS.from_texts(text_chunks, embedding=embeddings) st.session_state['vector_store'] = vector_store st.session_state['chain'] = create_conversational_chain(vector_store) display_chat_history(st.session_state['chain']) if st.button('Speak Now'): recognized_text = recognize_speech() if recognized_text: st.session_state['past'].append(recognized_text) output = conversation_chat(recognized_text, st.session_state['chain'], st.session_state['history']) st.session_state['generated'].append(output) st.session_state['translated'].append(output) st.session_state['translation_requested'].append(False) audio_bytes = text_to_speech(output) st.audio(audio_bytes, format="audio/mp3") st.rerun() else: st.warning("No speech input was processed. Please try speaking again.") gc.collect() st.sidebar. caption="Your AI Assistant" if __name__ == "__main__": main()