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app.py
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app.py
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import streamlit as st
from openai import OpenAI
import json, os, httpx, asyncio
import requests, time
from typing import Dict, Any
import pickle
from api.calc_consumption_context import get_consumption_context
from tenacity import retry, stop_after_attempt, wait_exponential
from pydantic import BaseModel # Import BaseModel for creating request body
from api.nutrient_analyzer import get_nutrient_analysis
from api.data_extractor import extract_data, find_product, get_product
from api.ingredients_analysis import get_ingredient_analysis
from api.claims_analysis import get_claims_analysis
from api.cumulative_analysis import generate_final_analysis
#Used the @st.cache_resource decorator on this function.
#This Streamlit decorator ensures that the function is only executed once and its result (the OpenAI client) is cached.
#Subsequent calls to this function will return the cached client, avoiding unnecessary recreation.
@st.cache_resource
def get_openai_client():
#Enable debug mode for testing only
return OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
client = get_openai_client()
@st.cache_resource
def create_assistant_and_embeddings():
global client
assistant1 = client.beta.assistants.create(
name="Processing Level",
instructions="You are an expert dietician. Use your knowledge base to answer questions about the processing level of food product.",
model="gpt-4o",
tools=[{"type": "file_search"}],
temperature=0,
top_p = 0.85
)
# Create a vector store
vector_store1 = client.beta.vector_stores.create(name="Processing Level Vec")
# Ready the files for upload to OpenAI
file_paths = ["docs/Processing_Level.docx"]
file_streams = [open(path, "rb") for path in file_paths]
# Use the upload and poll SDK helper to upload the files, add them to the vector store,
# and poll the status of the file batch for completion.
file_batch1 = client.beta.vector_stores.file_batches.upload_and_poll(
vector_store_id=vector_store1.id, files=file_streams
)
# You can print the status and the file counts of the batch to see the result of this operation.
print(file_batch1.status)
print(file_batch1.file_counts)
#Processing Level
assistant1 = client.beta.assistants.update(
assistant_id=assistant1.id,
tool_resources={"file_search": {"vector_store_ids": [vector_store1.id]}},
)
return assistant1
assistant_p = create_assistant_and_embeddings()
def extract_data_from_product_image(images_list):
raw_response = extract_data({"images_list" : images_list})
return raw_response
def get_product_list(product_name_by_user):
raw_response = find_product(product_name_by_user)
return raw_response
def get_product_info(product_name):
print(f"getting product info from mongodb for {product_name}")
product_info = get_product(product_name)
return product_info
# Define a sample request body that matches NutrientAnalysisRequest
class NutrientAnalysisRequest(BaseModel):
product_info_from_db: dict
async def analyze_nutrition_using_icmr_rda(product_info_from_db):
raw_response = await get_nutrient_analysis(NutrientAnalysisRequest(product_info_from_db=product_info_from_db))
return raw_response
def generate_cumulative_analysis(
brand_name: str,
product_name: str,
nutritional_level: str,
processing_level: str,
all_ingredient_analysis: str,
claims_analysis: str,
refs: list
):
print(f"Calling cumulative-analysis API with refs : {refs}")
raw_response = generate_final_analysis({'brand_name': brand_name, 'product_name': product_name, 'nutritional_level': nutritional_level, 'processing_level': processing_level, 'all_ingredient_analysis': all_ingredient_analysis, 'claims_analysis': claims_analysis, 'refs': refs})
return raw_response
async def analyze_processing_level_and_ingredients(product_info_from_db, assistant_p_id):
print("calling processing level and ingredient_analysis func")
print(f"assistant_p_id is of type {type(assistant_p_id)}")
request_payload = {
"product_info_from_db": product_info_from_db,
"assistant_p_id": assistant_p_id
}
raw_response = await get_ingredient_analysis(request_payload)
print("Processing and Ingredient analysis finished!")
return raw_response
def analyze_claims_list(product_info_from_db):
print("calling claims analysis func")
raw_response = get_claims_analysis(product_info_from_db)
return raw_response
async def analyze_product(product_info_from_db):
global assistant_p
if product_info_from_db:
brand_name = product_info_from_db.get("brandName", "")
product_name = product_info_from_db.get("productName", "")
start_time = time.time()
# Verify each function is async and returns a coroutine
coroutines = []
# Ensure each function is an async function and returns a coroutine
nutrition_coro = analyze_nutrition_using_icmr_rda(product_info_from_db)
processing_coro = analyze_processing_level_and_ingredients(product_info_from_db, assistant_p.id)
coroutines.append(nutrition_coro)
coroutines.append(processing_coro)
# Conditionally add claims analysis
# You can use asyncio.to_thread() to run the synchronous analyze_claims function in a separate thread, allowing it to run in parallel with your other asynchronous functions. Here’s how you can do it:
if product_info_from_db.get("claims"):
claims_coro = asyncio.to_thread(analyze_claims_list, product_info_from_db)
coroutines.append(claims_coro)
# Debug: Print coroutine types to verify
print("Coroutines:", [type(coro) for coro in coroutines])
# Parallel API calls
results = await asyncio.gather(*coroutines)
# Unpack results based on the number of coroutines
nutritional_level_json = results[0]
refs_ingredient_analysis_json = results[1]
claims_analysis_json = results[2] if len(results) > 2 else None
# Extract data from API results
nutritional_level = nutritional_level_json["nutrition_analysis"]
refs = refs_ingredient_analysis_json["refs"]
all_ingredient_analysis = refs_ingredient_analysis_json["all_ingredient_analysis"]
processing_level = refs_ingredient_analysis_json["processing_level"]
claims_analysis = claims_analysis_json["claims_analysis"] if claims_analysis_json else ""
# Generate final analysis
final_analysis = generate_cumulative_analysis(
brand_name,
product_name,
nutritional_level,
processing_level,
all_ingredient_analysis,
claims_analysis,
refs
)
print(f"DEBUG - Cumulative analysis finished in {time.time() - start_time} seconds")
return final_analysis
# Streamlit app
# Initialize session state
if 'messages' not in st.session_state:
st.session_state.messages = []
if 'uploaded_files' not in st.session_state:
st.session_state.uploaded_files = []
def chatbot_response(images_list, product_name_by_user, extract_info = True):
# Process the user input and generate a response
processing_level = ""
harmful_ingredient_analysis = ""
claims_analysis = ""
image_urls = []
if product_name_by_user != "":
similar_product_list_json = get_product_list(product_name_by_user)
if similar_product_list_json and extract_info == False:
with st.spinner("Fetching product information from our database... This may take a moment."):
print(f"similar_product_list_json : {similar_product_list_json}")
if 'error' not in similar_product_list_json.keys():
similar_product_list = similar_product_list_json['products']
return similar_product_list, "Product list found from our database"
else:
return [], "Product list not found"
elif extract_info == True:
with st.spinner("Analyzing product using data from 3,000+ peer-reviewed journal papers..."):
st.caption("This may take a few minutes")
st.chat_input("Please wait ...", disabled=True)
product_info_raw = get_product_info(product_name_by_user)
print(f"DEBUG product_info_raw from name: {type(product_info_raw)} {product_info_raw}")
if not product_info_raw:
return [], "product not found because product information in the db is corrupt"
if 'error' not in product_info_raw.keys():
final_analysis = asyncio.run(analyze_product(product_info_raw))
return [], final_analysis
else:
return [], f"Product information could not be extracted from our database because of {product_info_raw['error']}"
else:
return [], "Product not found in our database."
#elif "http:/" in image_urls_str.lower() or "https:/" in image_urls_str.lower()
elif len(images_list) > 1:
# Extract image URL from user input
#if "," not in image_urls_str:
# image_urls.append(image_urls_str)
#else:
# for url in image_urls_str.split(","):
# if "http:/" in url.lower() or "https:/" in url.lower():
# image_urls.append(url)
with st.spinner("Analyzing product using data from 3,000+ peer-reviewed journal papers..."):
st.caption("This may take a few minutes")
st.chat_input("Please wait ...", disabled=True)
product_info_raw = extract_data_from_product_image(images_list)
print(f"DEBUG product_info_raw from image : {product_info_raw}")
if 'error' not in product_info_raw.keys():
final_analysis = asyncio.run(analyze_product(product_info_raw))
return [], final_analysis
else:
return [], f"Product information could not be extracted from the image because of {json.loads(product_info_raw)['error']}"
else:
return [], "I'm here to analyze food products. Please provide an image URL (Example : http://example.com/image.jpg) or product name (Example : Harvest Gold Bread)"
class SessionState:
"""Handles all session state variables in a centralized way"""
@staticmethod
def initialize():
initial_states = {
"messages": [],
"uploaded_files": [],
"product_selected": False,
"product_shared": False,
"analyze_more": True,
"welcome_shown": False,
"yes_no_choice": None,
"welcome_msg": "Welcome to ConsumeWise! What product would you like me to analyze today? Example : Noodles, Peanut Butter etc",
"similar_products": [],
"awaiting_selection": False,
"current_user_input": "",
"selected_product": None,
"awaiting_image_upload": False
}
for key, value in initial_states.items():
if key not in st.session_state:
st.session_state[key] = value
class ProductSelector:
"""Handles product selection logic"""
@staticmethod
def handle_selection():
if st.session_state.similar_products:
# Create a container for the selection UI
selection_container = st.container()
with selection_container:
# Radio button for product selection
choice = st.radio(
"Select a product:",
st.session_state.similar_products + ["None of the above"],
key="product_choice"
)
# Confirm button
confirm_clicked = st.button("Confirm Selection")
print(f"Is Selection made by user ? : {confirm_clicked}")
# Only process the selection when confirm is clicked
msg = ""
if confirm_clicked:
st.session_state.awaiting_selection = False
if choice != "None of the above":
#st.session_state.selected_product = choice
st.session_state.messages.append({"role": "assistant", "content": f"You selected {choice}"})
print(f"Selection made by user : {choice}")
_, msg = chatbot_response([], choice.split(" by ")[0], extract_info=True)
print(f"msg is {msg}")
#Check if analysis couldn't be done because db had incomplete information
if msg != "product not found because product information in the db is corrupt":
#Only when msg is acceptable
st.session_state.messages.append({"role": "assistant", "content": msg})
with st.chat_message("assistant"):
st.markdown(msg)
st.session_state.product_selected = True
keys_to_keep = ["messages", "welcome_msg"]
keys_to_delete = [key for key in st.session_state.keys() if key not in keys_to_keep]
for key in keys_to_delete:
del st.session_state[key]
st.session_state.welcome_msg = "What product would you like me to analyze next?"
st.experimental_rerun()
if (choice == "None of the above" or msg == "product not found because product information in the db is corrupt") and len(st.session_state.uploaded_files) == 0:
if not st.session_state.awaiting_image_upload:
st.session_state.messages.append(
{"role": "assistant", "content": "Please provide the images of the product to analyze based on the latest information."}
)
with st.chat_message("assistant"):
st.markdown("Please provide the images of the product to analyze based on the latest information.")
# Add a file uploader to allow users to upload multiple images
st.session_state.awaiting_image_upload = True
uploaded_files = st.file_uploader(
"Upload product images here:",
type=["jpg", "jpeg", "png"],
accept_multiple_files=True
)
if uploaded_files:
st.session_state.messages.append(
{"role": "assistant", "content": f"{len(uploaded_files)} images uploaded for analysis."}
)
with st.chat_message("assistant"):
st.markdown(f"{len(uploaded_files)} images uploaded for analysis.")
st.session_state.uploaded_files = uploaded_files
st.session_state.awaiting_image_upload = False
st.experimental_rerun()
# Prevent further chat input while awaiting selection
return True # Indicates selection is in progress
return False # Indicates no selection in progress
class ChatManager:
"""Manages chat interactions and responses"""
@staticmethod
def process_response(user_input):
if not st.session_state.product_selected:
#if "http:/" not in user_input and "https:/" not in user_input:
print(f"DEBUG : st.session_state.uploaded_files inside process_response : {st.session_state.uploaded_files}")
if len(st.session_state.uploaded_files) == 0:
response, status = ChatManager._handle_product_name(user_input)
else:
print("Calling handle_product_url")
response, status = ChatManager._handle_product_url()
return response, status
@staticmethod
def _handle_product_name(user_input):
if not st.session_state.awaiting_image_upload and user_input:
st.session_state.product_shared = True
st.session_state.current_user_input = user_input
similar_products, _ = chatbot_response(
[], user_input, extract_info=False
)
st.session_state.similar_products = similar_products
if len(st.session_state.similar_products) > 0:
st.session_state.awaiting_selection = True
return "Here are some similar products from our database. Please select:", "no success"
# Add a file uploader to allow users to upload multiple images
# No similar products found
st.session_state.awaiting_image_upload = True
# Only show message and uploader if waiting for image upload
if st.session_state.awaiting_image_upload and len(st.session_state.uploaded_files) == 0:
if user_input:
with st.chat_message("assistant"):
st.markdown(f"Please provide images of the product since {len(st.session_state.similar_products)} similar products found in our database")
# Append the message to session state for chat history
st.session_state.messages.append({"role": "assistant", "content": f"Please provide images of the product since {len(st.session_state.similar_products)} similar products found in our database"})
st.chat_input("Please upload files ...", disabled=True)
uploaded_files = st.file_uploader(
"Upload product images here:",
type=["jpg", "jpeg", "png"],
accept_multiple_files=True
)
print(f"DEBUG : uploaded_files after st.file_uploader() : {uploaded_files}")
if len(uploaded_files) > 0:
st.session_state.uploaded_files = uploaded_files
st.session_state.awaiting_image_upload = False
print(f"DEBUG: User uploaded files: {uploaded_files}")
return f"{len(uploaded_files)} images uploaded for analysis.", "no success"
else:
# Show a temporary message until files are uploaded
st.info("Waiting for images to be uploaded.")
print("Waiting for images to be uploaded!")
st.stop()
@staticmethod
def _handle_product_url():
#is_valid_url = (".jpeg" in user_input or ".jpg" in user_input) and \
# ("http:/" in user_input or "https:/" in user_input)
if not st.session_state.product_shared:
return "Please provide the product name first"
if len(st.session_state.uploaded_files) > 1 and st.session_state.product_shared:
_, msg = chatbot_response(
st.session_state.uploaded_files, "", extract_info=True
)
st.session_state.product_selected = True
if msg != "product not found because image is not clear" and "Product information could not be extracted from the image" not in msg:
response = msg
status = "success"
elif msg == "product not found because image is not clear":
response = msg + ". Please share clear image URLs!"
status = "no success"
else:
response = msg + ".Please re-try!!"
status = "no success"
return response, status
st.session_state.uploaded_files = []
return "Please provide more than 1 images of the product to capture complete information.", "no success"
def main():
# Initialize session state
SessionState.initialize()
# Display title
st.title("ConsumeWise - Your Food Product Analysis Assistant")
# Show welcome message
if not st.session_state.welcome_shown:
st.session_state.messages.append({
"role": "assistant",
"content": st.session_state.welcome_msg
})
st.session_state.welcome_shown = True
# Display chat history
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Handle product selection if awaiting
selection_in_progress = False
if st.session_state.awaiting_selection:
print("Awaiting selection")
selection_in_progress = ProductSelector.handle_selection()
# Only show chat input if not awaiting selection
print(f"Selection in progress ? : {selection_in_progress}")
if not selection_in_progress:
user_input = st.chat_input("Enter your message:", key="user_input")
print(f"DEBUG - user_input : {user_input} and len(st.session_state.uploaded_files) is {len(st.session_state.uploaded_files)}")
if user_input or st.session_state.awaiting_image_upload or len(st.session_state.uploaded_files) > 0:
if user_input:
# Add user message to chat
st.session_state.messages.append({"role": "user", "content": user_input})
with st.chat_message("user"):
st.markdown(user_input)
# Process response
print(f"DEBUG - Calling process_response with user_input : {user_input}")
response, status = ChatManager.process_response(user_input)
print(f"DEBUG - response from process_response is {response}")
if "Waiting for images to be uploaded" not in response:
with st.chat_message("assistant"):
st.markdown(response)
st.session_state.messages.append({"role": "assistant", "content": response})
if status == "success":
SessionState.initialize() # Reset states for next product
keys_to_keep = ["messages", "welcome_msg"]
keys_to_delete = [key for key in st.session_state.keys() if key not in keys_to_keep]
for key in keys_to_delete:
del st.session_state[key]
st.session_state.welcome_msg = "What product would you like me to analyze next?"
print("Re-running...")
st.experimental_rerun()
else:
# Disable chat input while selection is in progress
st.chat_input("Please confirm your selection above first...", disabled=True)
# Clear chat history button
if st.button("Clear Chat History"):
st.session_state.clear()
st.experimental_rerun()
# Call the wrapper function in Streamlit
if __name__ == "__main__":
main()