app.py

import streamlit as st
import os
from prompts import prompts
from constants import JSON_SCHEMA_FOR_GPT, UPDATED_MODEL_ONLY_SCHEMA, JSON_SCHEMA_FOR_LOC_ONLY
from gpt import runAssistant, checkRunStatus, retrieveThread, createAssistant, saveFileOpenAI, startAssistantThread, \
    create_chat_completion_request_open_ai_for_summary, addMessageToThread, create_image_completion_request_gpt
from summarizer import create_brand_html, create_langchain_openai_query
from theme import flux_generated_image, flux_generated_image_seed
import time
from PIL import Image
import io


def process_run(st, thread_id, assistant_id):
    run_id = runAssistant(thread_id, assistant_id)
    status = 'running'
    while status != 'completed':
        with st.spinner('. . .'):
            time.sleep(20)
            status = checkRunStatus(thread_id, run_id)
    thread_messages = retrieveThread(thread_id)
    for message in thread_messages:
        if not message['role'] == 'user':
            return message["content"]
        else:
            pass


def page1():
    st.title("Upload Product")
    st.markdown("<h2 style='color:#FF5733; font-weight:bold;'>Add a Product</h2>", unsafe_allow_html=True)
    st.markdown("<p style='color:#444;'>Upload your product images, more images you upload better the AI learns</p>",
                unsafe_allow_html=True)
    uploaded_files = st.file_uploader("Upload Images", accept_multiple_files=True, key="uploaded_files_key")
    product_description = st.text_area("Describe the product", value=st.session_state.get("product_description", ""))
    col1, col2 = st.columns([1, 2])
    with col1:
        if st.button("Save"):
            st.session_state['uploaded_files'] = uploaded_files
            st.session_state['product_description'] = product_description
            st.success("Product information saved!")
    with col2:
        if st.button("Add product and move to next page"):
            if not uploaded_files:
                st.warning("Please upload at least one image.")
            elif not product_description:
                st.warning("Please provide a description for the product.")
            else:
                st.session_state['uploaded_files'] = uploaded_files
                st.session_state['product_description'] = product_description
                st.session_state['page'] = "Page 2"


def page2():
    st.title("Tell us about your shoot preference")
    st.markdown("<h3 style='color:#444;'>What are you shooting today?</h3>", unsafe_allow_html=True)
    shoot_type = st.radio("Select your shoot type:", ["Editorial", "Catalogue"], index=0)
    st.session_state['shoot_type'] = shoot_type
    brand_link = st.text_input("Add your brand link:", value=st.session_state.get("brand_link", ""))
    st.session_state['brand_link'] = brand_link
    if st.button("Get Brand Summary"):
        if brand_link:
            brand_summary_html = create_brand_html(brand_link)
            brand_summary = create_langchain_openai_query(brand_summary_html)
            st.session_state['brand_summary'] = brand_summary
            st.success("Brand summary fetched!")
        else:
            st.warning("Please add a brand link.")
    brand_summary_value = st.session_state.get('brand_summary', "")
    editable_summary = st.text_area("Brand Summary:", value=brand_summary_value, height=100)
    st.session_state['brand_summary'] = editable_summary
    product_info = st.text_area("Tell us something about your product:", value=st.session_state.get("product_info", ""))
    st.session_state['product_info'] = product_info
    reference_images = st.file_uploader("Upload Reference Images", accept_multiple_files=True,
                                        key="reference_images_key")
    st.session_state['reference_images'] = reference_images
    if st.button("Give Me Ideas"):
        st.session_state['page'] = "Page 3"


def page3():
    st.title("Scene Suggestions")
    st.write("Based on your uploaded product and references!")
    feedback = st.chat_input("Provide feedback:")
    if not st.session_state.get("assistant_initialized", False):
        assistant_id = createAssistant("You are a helpful assistant who is an expert in Fashion Shoots.")
        updated_prompt = prompts["IDEA_GENERATION_PROMPT"].format(
            brand_details=st.session_state["brand_summary"],
            product_details=st.session_state["product_info"],
            type_of_shoot=st.session_state["shoot_type"],
            json_schema=JSON_SCHEMA_FOR_GPT,
            product_name=st.session_state["product_description"]
        )
        file_locations = []
        for uploaded_file in st.session_state['uploaded_files']:
            bytes_data = uploaded_file.getvalue()
            image = Image.open(io.BytesIO(bytes_data))
            image.verify()
            location = f"temp_image_{uploaded_file.name}"
            with open(location, "wb") as f:
                f.write(bytes_data)
            file_locations.append(location)
            image.close()
        for uploaded_file in st.session_state['reference_images']:
            bytes_data = uploaded_file.getvalue()
            image = Image.open(io.BytesIO(bytes_data))
            image.verify()
            location = f"temp2_image_{uploaded_file.name}"
            with open(location, "wb") as f:
                f.write(bytes_data)
            file_locations.append(location)
            image.close()
        file_ids = [saveFileOpenAI(location) for location in file_locations]
        thread_id = startAssistantThread(file_ids, updated_prompt, "yes", "yes")
        st.session_state.assistant_id = assistant_id
        st.session_state.thread_id = thread_id
        st.session_state.assistant_initialized = True
        regenerate_images(thread_id, assistant_id)
    if feedback:
        if 'images' in st.session_state and 'descriptions' in st.session_state:
            for image_path in st.session_state['images']:
                os.remove(image_path)
            del st.session_state['images']
            del st.session_state['descriptions']
            del st.session_state["json_descriptions"]
        addMessageToThread(st.session_state.thread_id, feedback)
        regenerate_images(st.session_state.thread_id, st.session_state.assistant_id)
    selected_image_index = None
    cols = st.columns(1)
    for i in range(len(st.session_state["images"])):
        with cols[i]:
            st.image(st.session_state.images[i], caption=st.session_state.descriptions[i], use_column_width=True)
            if st.radio(f"Select {i + 1}", [f"Select Image {i + 1}"], key=f"radio_{i}"):
                selected_image_index = i
    if selected_image_index is not None and st.button("Refine"):
        st.session_state.selected_image_index = selected_image_index
        st.session_state.selected_image = st.session_state.images[selected_image_index]
        st.session_state.selected_text = st.session_state.descriptions[selected_image_index]
        st.session_state['page'] = "Page 4"
    if st.button("Go Back!"):
        st.session_state.page = "Page 2"


def regenerate_images(thread_id, assistant_id):
    """Helper function to generate images and descriptions."""
    response_from_process_list = []
    for _ in range(1):  # Assuming you generate 1 set of image/description
        response_from_process = process_run(st, thread_id, assistant_id)
        response_from_process_list.append(response_from_process)

    summary_list = []
    for final_response in response_from_process_list:
        prompt_for_idea_summary = prompts["IDEA_SUMMARY_PROMPT"].format(
            json_schema=str(final_response)
        )
        summary = create_chat_completion_request_open_ai_for_summary(prompt_for_idea_summary, "No")
        summary_list.append(summary)

    # Generate images based on the summaries
    flux_generated_theme_image = []
    for summary in summary_list:
        theme_image = flux_generated_image(summary)
        flux_generated_theme_image.append(theme_image["file_name"])

    # Save the new images and descriptions in session state
    st.session_state["images"] = flux_generated_theme_image
    st.session_state["descriptions"] = summary_list
    st.session_state["json_descriptions"] = response_from_process_list


def page4():
    import json
    selected_theme_text_by_user = st.session_state.json_descriptions[st.session_state.selected_image_index]
    print(selected_theme_text_by_user)
    schema_for_model_bg = {"type": "object",
                           "properties": {
                               "Model": {
                                   "type": "string",
                                   "description": "The model name or identifier."
                               },
                               "Background": {
                                   "type": "string",
                                   "description": "Description or type of the background."
                               }},
                           "required": ["Model", "Background"],
                           "additionalProperties": False
                           }
    prompt_to_get_details = (f"You are provided with a brief of a Fashion Shoot : "
                             f"{st.session_state[\"json_descriptions\"]}.\n Now provide me a JSON which will"
                             f"have two keys ```Model``` and ```Background```. Provide all detail's"
                             f"present about model and background in the brief provided by you. Just provide a "
                             f"natural langauge description. I will use it as description of model and "
                             f"background needed by the brand Output JSON following the schema")
    response_from_open_ai = create_chat_completion_request_open_ai_for_summary(prompt_to_get_details,
                                                                               schema_name="model_bg",
                                                                               json_schema=schema_for_model_bg,
                                                                               json_mode="yes")
    json_response_from_open_ai = json.loads(response_from_open_ai)
    with (st.sidebar):
        st.title(st.session_state["product_info"])
        st.write("Product Image")
        st.image(st.session_state['uploaded_files'])
        st.text("Scene Suggestion:")
        st.image(st.session_state.selected_image)
        dimensions = st.text_input("Enter Dimensions e.g 3:4, 1:2", key="Dimensions")
        seed = st.selectbox(
            "Seed Preference",
            ("Fixed", "Random"),
        )
        if seed == "Fixed":
            seed_number = st.number_input("Enter an integer:", min_value=1, max_value=100000, value=10, step=1)
        else:
            seed_number = 0
            st.text("Thanks will take care")
        model_preference = st.selectbox(
            "Model Preference",
            ("Create Own/Edit Pre-filled", "Ideas", "Upload Reference"),
        )
        if model_preference == "Create Own/Edit Pre-filled":
            pre_filled_model_details = st.text_area("Model Idea", value=json_response_from_open_ai["Model"],
                                                    key="Model Idea")
        elif model_preference == "Ideas":
            prompt_to_generate_idea = ("Your task is to create model ideas for shoot of a  product of a brand. "
                                       "The details about the brand: ```{brand_details}.\n The product: {product_name},"
                                       "which is: ```{product_details}```.\n Reference images for the product and "
                                       "brands shoot idea is already provided with you. Additionally brand wants to "
                                       "have a ```{type_of_shoot}``` of the model. Now based on all provided details, "
                                       "think step by step and provide your ideas about what type of model the brand"
                                       "should need based on mentioned JSON format. Also provide a combined prompt "
                                       "which the brand will use to create a shoot image. While creating the "
                                       "combined prompt as mentioned in the JSON schema, do not miss any details you"
                                       " mentioned in the JSON.")
            updated_model_idea_gen_prompt = prompt_to_generate_idea.format(
                brand_details=st.session_state["brand_summary"],
                product_details=st.session_state["product_info"],
                type_of_shoot=st.session_state["shoot_type"],
                product_name=st.session_state["product_description"]

            )
            response_for_only_model = create_chat_completion_request_open_ai_for_summary(updated_model_idea_gen_prompt
                                                                                         , schema_name="model_only",
                                                                                         json_schema=
                                                                                         UPDATED_MODEL_ONLY_SCHEMA,
                                                                                         json_mode="yes")
            pre_filled_model_details = st.text_area("Model Idea", value=response_for_only_model,
                                                    key="Model Idea")
        else:
            uploaded_files = st.file_uploader("Upload one Model Reference Image here",
                                              accept_multiple_files=False, key="uploader")
            bytes_data = uploaded_files.getvalue()
            image = Image.open(io.BytesIO(bytes_data))
            image.verify()
            location = f"temp_image_{uploaded_files.name}"
            with open(location, "wb") as f:
                f.write(bytes_data)
            image.close()
            prompt_to_generate_idea = ("Follow this JSON Schema : {json_schema_model_only}."
                                       "Your task is to create model ideas for shoot of a  product of a brand. "
                                       "The details about the brand: ```{brand_details}.\n The product: {product_name},"
                                       "which is: ```{product_details}```.\n Reference images for the product and "
                                       "brands shoot idea is already provided with you. Additionally brand wants to "
                                       "have a ```{type_of_shoot}``` of the model. Now based on all provided details, "
                                       "think step by step and provide your ideas about what type of model the brand"
                                       "should need based on mentioned JSON format. Also provide a combined prompt "
                                       "which the brand will use to create a shoot image. While creating the "
                                       "combined prompt as mentioned in the JSON schema, do not miss any details you"
                                       " mentioned in the JSON.")
            updated_model_idea_gen_prompt = prompt_to_generate_idea.format(
                json_schema_model_only=UPDATED_MODEL_ONLY_SCHEMA,
                brand_details=st.session_state["brand_summary"],
                product_details=st.session_state["product_info"],
                type_of_shoot=st.session_state["shoot_type"],
                product_name=st.session_state["product_description"]

            )
            json_response = create_image_completion_request_gpt(location, updated_model_idea_gen_prompt)
            pre_filled_model_details = st.text_area("Model Idea", value=json_response,
                                                    key="Model Idea")
        background_preference = st.selectbox(
            "Background Preference",
            ("Create Own/Edit Pre-filled", "Ideas", "Upload Reference"),
        )
        if background_preference == "Create Own/Edit Pre-filled":
            pre_filled_background_details = st.text_area("Background Idea",
                                                         value=json_response_from_open_ai["Background"],
                                                         key="Background Idea")
        elif background_preference == "Ideas":
            prompt_to_generate_idea = ("Follow this JSON Schema : {json_schema_background_only}."
                                       "Your task is to create location/background ideas for shoot of a  "
                                       "product of a brand. "
                                       "The details about the brand: ```{brand_details}.\n The product: {product_name},"
                                       "which is: ```{product_details}```.\n Reference images for the product and "
                                       "brands shoot idea is already provided with you. Additionally brand wants to "
                                       "have a ```{type_of_shoot}``` of the model. Now based on all provided details, "
                                       "think step by step and provide your ideas about what type of location the brand"
                                       "should need based on mentioned JSON format. Also provide a combined prompt "
                                       "which the brand will use to create a shoot image. While creating the "
                                       "combined prompt as mentioned in the JSON schema, do not miss any details you"
                                       " mentioned in the JSON.")
            updated_bg_idea_gen_prompt = prompt_to_generate_idea.format(
                json_schema_background_only=JSON_SCHEMA_FOR_LOC_ONLY,
                brand_details=st.session_state["brand_summary"],
                product_details=st.session_state["product_info"],
                type_of_shoot=st.session_state["shoot_type"],
                product_name=st.session_state["product_description"]

            )
            response_for_only_bg = create_chat_completion_request_open_ai_for_summary(updated_bg_idea_gen_prompt,
                                                                                      schema_name="bg_o",
                                                                                      json_schema=JSON_SCHEMA_FOR_LOC_ONLY,
                                                                                      json_mode="yes")
            pre_filled_background_details = st.text_area("Background Idea", value=response_for_only_bg,
                                                         key="Background Idea")
        else:
            uploaded_files = st.file_uploader("Upload one Background Reference Image here",
                                              accept_multiple_files=False, key="uploader")
            bytes_data = uploaded_files.getvalue()
            image = Image.open(io.BytesIO(bytes_data))
            image.verify()
            location = f"temp2_image_{uploaded_files.name}"
            with open(location, "wb") as f:
                f.write(bytes_data)
            image.close()
            prompt_to_generate_idea = ("Follow this JSON Schema : {json_schema_bg_only}."
                                       "Your task is to create Background/Location ideas for shoot of a  "
                                       "product of a brand. "
                                       "The details about the brand: ```{brand_details}.\n The product: {product_name},"
                                       "which is: ```{product_details}```.\n Reference images for the product and "
                                       "brands shoot idea is already provided with you. Additionally brand wants to "
                                       "have a ```{type_of_shoot}``` of the model. Now based on all provided details, "
                                       "think step by step and provide your ideas about what type of location the brand"
                                       "should need based on mentioned JSON format. Also provide a combined prompt "
                                       "which the brand will use to create a shoot image. While creating the "
                                       "combined prompt as mentioned in the JSON schema, do not miss any details you"
                                       " mentioned in the JSON.")
            updated_bg_idea_gen_prompt = prompt_to_generate_idea.format(
                json_schema_bg_only=JSON_SCHEMA_FOR_LOC_ONLY,
                brand_details=st.session_state["brand_summary"],
                product_details=st.session_state["product_info"],
                type_of_shoot=st.session_state["shoot_type"],
                product_name=st.session_state["product_description"]

            )
            json_response = create_image_completion_request_gpt(location, updated_bg_idea_gen_prompt)
            pre_filled_background_details = st.text_area("Background Idea", value=json_response,
                                                         key="Background Idea")
        start_chat = st.button("Start Chat")
    if "mood_chat_messages" not in st.session_state:
        st.session_state["mood_chat_messages"] = []
    if seed and dimensions and model_preference and background_preference:
        if start_chat:
            final_mood_board_image_prompt = prompts["FINAL_PROMPT_GENERATION"].format(
                brand_details=st.session_state["brand_summary"],
                product_details=st.session_state["product_info"],
                type_of_shoot=st.session_state["shoot_type"],
                product_name=st.session_state["product_description"],
                model_details=pre_filled_model_details,
                location_details=pre_filled_background_details,
                theme_details=str(selected_theme_text_by_user),
                chat_history=str(st.session_state["mood_chat_messages"])
            )
            prompt_for_flux_mood_board = create_chat_completion_request_open_ai_for_summary(
                final_mood_board_image_prompt, "No", system_message=prompts["SYSTEM_PROMPT_FOR_MOOD_BOARD"])
            if seed == "Fixed":
                generated_flux_image = flux_generated_image_seed(prompt_for_flux_mood_board, seed_number, dimensions)
            else:
                generated_flux_image = flux_generated_image(prompt_for_flux_mood_board)
            st.session_state["mood_chat_messages"].append({
                "role": "AI",
                "message": prompt_for_flux_mood_board,
                "image": generated_flux_image["file_name"]
            })
        # for message in st.session_state["mood_chat_messages"]:
          #  if message["role"] == "AI":
           #     st.write(f"Caimera AI: {message['message']}")
            #    st.image(message['image'])
            #else:
             #   st.write(f"**You**: {message['message']}")
        user_input = st.chat_input("Type your message here...")
        if user_input:
            st.session_state["mood_chat_messages"].append({"role": "User", "message": user_input})
            prompt_for_flux_mood_board_n = create_chat_completion_request_open_ai_for_summary(
                user_input, "No", system_message=prompts["SYSTEM_PROMPT_FOR_MOOD_BOARD"])
            if seed == "Fixed":
                generated_flux_image_n = flux_generated_image_seed(prompt_for_flux_mood_board_n, seed_number,
                                                                   dimensions)
            else:
                generated_flux_image_n = flux_generated_image(prompt_for_flux_mood_board_n)
            st.session_state["mood_chat_messages"].append({
                "role": "AI",
                "message": prompt_for_flux_mood_board_n,
                "image": generated_flux_image_n["file_name"]
            })
        for message in st.session_state["mood_chat_messages"]:
            if message["role"] == "AI":
                st.write(f"**AI**: {message['message']}")
                st.image(message['image'])
            else:
                st.write(f"**You**: {message['message']}")
        print(seed_number)


if 'page' not in st.session_state:
    st.session_state.page = "Page 1"

# Routing between pages
if st.session_state.page == "Page 1":
    page1()
elif st.session_state.page == "Page 2":
    page2()
elif st.session_state.page == "Page 3":
    page3()
elif st.session_state.page == "Page 4":
    page4()