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import streamlit as st
from streamlit_chat import message
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import DirectoryLoader
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import CTransformers
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.document_loaders.csv_loader import CSVLoader
import requests
from PIL import Image
import pydeck as pdk
import os
import json
st.set_page_config(
page_title="FoodGPT - Nagpur Based Food Recommendation System.",
page_icon="🍊",
layout="wide",
initial_sidebar_state="expanded",
)
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='data.csv')
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500,chunk_overlap=50)
text_chunks = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={'device':"cpu"})
vector_store = FAISS.from_documents(text_chunks,embeddings)
llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q4_0 10.47.24 AM.bin",model_type="llama",
config={'max_new_tokens':128,'temperature':0.01})
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":2}),
memory=memory)
# Sidebar for user input
st.sidebar.title("FoodGPT!🍊")
st.sidebar.info("FoodGPT : A Nagpur Based Food Recommendation Chat! Recommends you the best locally recognized brands for your cravings! As this system is backed with LLMA-2 on hand picked data.")
github_link = "[GitHub]('https://github.com/prasanna-muppidwar/Nagpur-FoodGPT')"
st.sidebar.info("To contribute and Sponser - " + github_link)
st.title("FoodGPT: A Nagpur based Food Recommendation Bot! 🍊")
if 'history' not in st.session_state:
st.session_state['history'] = []
if 'generated' not in st.session_state:
st.session_state['generated'] = ["Hello! I'm FoodGPT, Ask me anything about Nagpur's Food."]
if 'past' not in st.session_state:
st.session_state['past'] = ["Hello!"]
reply_container = st.container()
container = st.container()
with container:
with st.form(key='my_form', clear_on_submit=True):
user_input = st.text_input("Question:", placeholder="Ask anything about Nagpur's Food Joints or cravings", key='input')
image_upload = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
submit_button = st.form_submit_button(label='Send')
try:
if submit_button and user_input:
output = chain({"question": user_input, "chat_history": st.session_state['history']})["answer"]
st.session_state['past'].append(user_input)
st.session_state['generated'].append(output)
except Exception as e:
st.error(f"An error occurred: {str(e)}")
if st.session_state['generated']:
with reply_container:
for i in range(len(st.session_state['generated'])):
message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
import requests
import streamlit as st
import time
def query_image_classification(image_bytes, max_retries=3):
for retry in range(max_retries):
try:
response = requests.post(API_URL, headers=HEADERS, data=image_bytes)
response.raise_for_status() # Raise an error for non-2xx HTTP responses
result = response.json()
return result
except requests.exceptions.RequestException as e:
st.error(f"An error occurred during the API request: {str(e)}")
except ValueError as e:
st.error(f"An error occurred while processing the API response: {str(e)}")
if retry < max_retries - 1:
st.warning(f"Retrying request (attempt {retry + 1}/{max_retries})...")
time.sleep(1) # Wait for a moment before retrying
st.error("No classification result received after multiple retries.")
return None
if image_upload:
image_bytes = image_upload.read()
classification_result = query_image_classification(image_bytes)
if classification_result:
st.image(image_upload, caption="Uploaded Image", use_column_width=True)
if isinstance(classification_result, list) and classification_result:
# Ensure that classification_result is a list of results and not empty
best_label = max(classification_result, key=lambda x: x.get('score', 0))
if 'label' in best_label:
st.header("Image Classification Result:")
st.write(f"Classified as: {best_label['label']}")
else:
st.error("Invalid classification result format. Missing 'label' key.")
else:
st.error("Invalid classification result format or empty result list.")
import pydeck as pdk
st.title("Nagpur Map")
center = [21.1458, 79.0882]
st.pydeck_chart(
pdk.Deck(
map_style="mapbox://styles/mapbox/light-v9",
initial_view_state={
"latitude": center[0],
"longitude": center[1],
"zoom": 13,
"pitch": 10,
},
layers=[
pdk.Layer(
"ScatterplotLayer",
data=[{"position": center, "tooltip": "Nagpur"}],
get_position="position",
get_radius=10000,
get_color=[255, 0, 0],
pickable=True,
),
],
)
)
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