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Oct 10, 2024
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Enhance error handling and improve UI for fetching subdirectories #1330

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266 changes: 133 additions & 133 deletions Algorithms and Deep Learning Models/Beyond-GPS-Navi-Bot/app.py
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Original file line number Diff line number Diff line change
@@ -1,133 +1,133 @@
import streamlit as st
import pyttsx3
import speech_recognition as sr
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
load_dotenv()
os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# Initialize pyttsx3 for voice output
engine = pyttsx3.init()
# Function to speak the text
def speak(text):
engine.say(text)
engine.runAndWait()
# Function to listen to voice input
def listen():
r = sr.Recognizer()
with sr.Microphone() as source:
st.write("Listening...")
r.adjust_for_ambient_noise(source)
audio = r.listen(source)
try:
user_input = r.recognize_google(audio)
st.write(f"You said: {user_input}")
return user_input
except sr.UnknownValueError:
st.write("Sorry, I could not understand what you said.")
return None
except sr.RequestError as e:
st.write(f"Could not request results from Google Speech Recognition service; {e}")
return None
def get_pdf_text(pdf_docs):
text=""
for pdf in pdf_docs:
pdf_reader= PdfReader(pdf)
for page in pdf_reader.pages:
text+= page.extract_text()
return text
def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
chunks = text_splitter.split_text(text)
return chunks
def get_vector_store(text_chunks):
embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
vector_store.save_local("faiss_index")
def get_conversational_chain():
prompt_template = """
Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
Context:\n {context}?\n
Question: \n{question}\n
Answer:
"""
model = ChatGoogleGenerativeAI(model="gemini-pro",
temperature=0.3)
prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
return chain
def user_input(user_question):
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
# Load the local FAISS index with dangerous deserialization allowed
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, "question": user_question},
return_only_outputs=True
)
speak(response["output_text"]) # Speak the response
st.write("Reply: ", response["output_text"])
def main():
st.set_page_config("Beyond GPS Navigation")
st.header("Beyond GPS Navigator for Blind")
user_question = st.text_input("Ask your query")
voice_input_button = st.button("Voice Input")
if voice_input_button:
user_question = listen() # Listen to voice input
if user_question:
user_input(user_question)
if user_question:
user_input(user_question)
with st.sidebar:
st.title("Menu:")
pdf_docs = st.file_uploader("Upload your route data and Click on the Submit & Process Button", accept_multiple_files=True)
if st.button("Submit & Process"):
with st.spinner("Processing..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
get_vector_store(text_chunks)
st.success("Done")
if __name__ == "__main__":
main()
import streamlit as st
import pyttsx3
import speech_recognition as sr
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv

load_dotenv()
os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Initialize pyttsx3 for voice output
engine = pyttsx3.init()

# Function to speak the text
def speak(text):
engine.say(text)
engine.runAndWait()

# Function to listen to voice input
def listen():
r = sr.Recognizer()
with sr.Microphone() as source:
st.write("Listening...")
r.adjust_for_ambient_noise(source)
audio = r.listen(source)

try:
user_input = r.recognize_google(audio)
st.write(f"You said: {user_input}")
return user_input
except sr.UnknownValueError:
st.write("Sorry, I could not understand what you said.")
return None
except sr.RequestError as e:
st.write(f"Could not request results from Google Speech Recognition service; {e}")
return None


def get_pdf_text(pdf_docs):
text=""
for pdf in pdf_docs:
pdf_reader= PdfReader(pdf)
for page in pdf_reader.pages:
text+= page.extract_text()
return text


def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
chunks = text_splitter.split_text(text)
return chunks


def get_vector_store(text_chunks):
embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
vector_store.save_local("faiss_index")


def get_conversational_chain():

prompt_template = """
Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
Context:\n {context}?\n
Question: \n{question}\n

Answer:
"""

model = ChatGoogleGenerativeAI(model="gemini-pro",
temperature=0.3)

prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)

return chain


def user_input(user_question):
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")

# Load the local FAISS index with dangerous deserialization allowed
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, "question": user_question},
return_only_outputs=True
)

speak(response["output_text"]) # Speak the response
st.write("Reply: ", response["output_text"])


def main():
st.set_page_config("Beyond GPS Navigation")
st.header("Beyond GPS Navigator for Blind")

user_question = st.text_input("Ask your query")
voice_input_button = st.button("Voice Input")

if voice_input_button:
user_question = listen() # Listen to voice input
if user_question:
user_input(user_question)

if user_question:
user_input(user_question)

with st.sidebar:
st.title("Menu:")
pdf_docs = st.file_uploader("Upload your route data and Click on the Submit & Process Button", accept_multiple_files=True)
if st.button("Submit & Process"):
with st.spinner("Processing..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
get_vector_store(text_chunks)
st.success("Done")


if __name__ == "__main__":
main()
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