app.py

import streamlit as st
import requests
import os
import json
from pypdf import PdfReader
from groq import Groq
from dotenv import load_dotenv
import time
import ast

# Load environment variables
load_dotenv()

# Function to extract text from the uploaded PDF
def extract_text_from_pdf(pdf_file):
    text = ""
    try:
        reader = PdfReader(pdf_file)
        for page in reader.pages:
            text += page.extract_text()
    except Exception as e:
        st.error(f"An error occurred while reading the PDF: {e}")
    return text

# Function to classify the extracted text using the LLM
def classification_LLM(text):


    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful classification assistant. You understand engineering concepts. You will be given some text which mostly describes a problem. You have to classify the problem according to a list of choices. More than one choice can also be applicable. Return as a array of applicable CHOICES only. Only return the choices that you are very sure about\n\n#CHOICES\n\n2D Measurement: Diameter, thickness, etc.\n\nAnomaly Detection: Scratches, dents, corrosion\n\nPrint Defect: Smudging, misalignment\n\nCounting: Individual components, features\n\n3D Measurement: Volume, surface area\n\nPresence/Absence: Missing components, color deviations\n\nOCR: Optical Character Recognition, Font types and sizes to be recognized, Reading speed and accuracy requirements\n\nCode Reading: Types of codes to read (QR, Barcode)\n\nMismatch Detection: Specific features to compare for mismatches, Component shapes, color mismatches\n\nClassification: Categories of classes to be identified, Features defining each class\n\nAssembly Verification: Checklist of components or features to verify, Sequence of assembly to be followed\n\nColor Verification: Color standards or samples to match\n"
            },
            {
                "role": "user",
                "content": text
            }
        ],
        temperature=0.21,
        max_tokens=2048,
        top_p=1,
        stream=True,
        stop=None,
    )

    answer = ""
    for chunk in completion:
        answer += chunk.choices[0].delta.content or ""
    return answer

def obsjsoncreate(json_template,text,ogtext):
    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant. You will be given a text snippet. You will also be given a JSON where some of the fields match with the bullet points in the text. I want you return a JSON where only the fields and subproperties mentioned in the text are present. DONT OUTPUT ANYTHING OTHER THAN THE JSON\n"
            },
            {
                "role": "user",
                "content": "JSON:"+str(json_template)+"\nText:"+text
            }
        ],
        temperature=0.21,
        max_tokens=8000,
        top_p=1,
        stream=True,
        stop=None,
    )
    cutjson=""
    for chunk in completion:
        cutjson += chunk.choices[0].delta.content or ""
    
    completion2 = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful classification assistant. You understand engineering concepts. You will be given a JSON where there are properties and their descriptions. You need to fill up the JSON subproperty \"USer Answer\" from the details given in the text. If you are not sure of any field, leave the \"User Answer\" as TBD. Give the JSON output with the filled fields only. ENSURE THE JSON IS VALID AND PROPERLY FORMATTED. DO NOT OUTPUT ANYTHING OTHER THAN THE JSON."
            },
            {
                "role": "user",
                "content": "JSON: "+cutjson+"\n Text: "+ogtext
            }
        ],
        temperature=0.21,
        max_tokens=8000,
        top_p=1,
        stream=True,
        stop=None,
    )
    answer = ""
    for chunk in completion2:
        answer += chunk.choices[0].delta.content or ""
    return answer

def bizobjjsoncreate(json_template,text):
    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    completion2 = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful classification assistant. You understand engineering concepts. You will be given a JSON where there are properties and their descriptions. You need to fill up the JSON subproperty \"USer Answer\" from the details given in the text. If you are not sure of any field, leave the \"User Answer\" as TBD. Make sure you dont leave out a single field of the JSON. ENSURE THE JSON IS VALID AND PROPERLY FORMATTED. DO NOT OUTPUT ANYTHING OTHER THAN THE JSON."
            },
            {
                "role": "user",
                "content": "JSON: "+str(json_template)+"\n Text: "+text
            }
        ],
        temperature=0.21,
        max_tokens=8000,
        top_p=1,
        stream=True,
        stop=None,
    )
    answer = ""
    for chunk in completion2:
        answer += chunk.choices[0].delta.content or ""
    return answer

def question_create(json_template):

    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant. You will be given a JSON where some subproperties labelled \"User Answer\" are marked as \"TBD\". I want you to create questions that you as an assistant would ask the user in order to fill up the User Answer field. Return all the questions for the user in an array. DONT OUTPUT ANYTHING OTHER THAN THE QUESTION ARRAY."
            },
            {
                "role": "user",
                "content": str(json_template)
            }
        ],
        temperature=0.21,
        max_tokens=2048,
        top_p=1,
        stream=True,
        stop=None,
    )

    answer = ""
    for chunk in completion:
        answer += chunk.choices[0].delta.content or ""

    # print(answer)
    client = Groq()
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are an experienced writer. You will be given an array of questions. \nSome questions will ask to upload images. Ignore any of these type of questions.\nSome questions ask about different identities or descriptions of the same thing. I want you to merge the questions so as to ask input from them once.\nConvert all questions so that more of a professional is maintained. AVOID REDUNDANCY. DONT RETURN MORE THAN 15 QUESTIONS.\nRETURN AN ARRAY OF THE QUESTIONS ONLY. DO NOT RETURN ANYTHING ELSE. "
            },
            {
                "role": "user",
                "content": answer
            }
        ],
        temperature=0.45,
        max_tokens=4240,
        top_p=1,
        stream=True,
        stop=None,
    )
    final=""
    for chunk in completion:
        final+=chunk.choices[0].delta.content or ""

    return final

def qapair_create(script):

    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant. You will be given two arrays, questions and answer. I want you to create a question answer pair. For example, \n#INPUT\nQuestion=['What is my name?', 'What is your age?']\nAnswer=['Mohan','69']\n\n#OUTPUT\n['Question:What is my name? Answer:Mohan','What is your age? Answer:69']\n\nDONT RETURN ANYTHING OTHER THAN THE FINAL ARRAY"
            },
            {
                "role": "user",
                "content": str(script)
            }
        ],
        temperature=0.5,
        max_tokens=4048,
        top_p=1,
        stream=True,
        stop=None,
    )

    qapair = ""
    for chunk in completion:
        qapair += chunk.choices[0].delta.content or ""
    return qapair
    # print(qapair)
    # print(obs_json_template+bizobj_json_template)
    # print("Question Answer:"+str(qapair)+"\nJSON:\n"+str(obs_json_template+bizobj_json_template))
    # print(str(obs_json_template+bizobj_json_template))
def conflict_detect(json1,json2):

    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are an experienced copywriter who understands engineering concepts. You will be given 2 JSONs. I want you to compare the \"User Answer\" field between the 2 JSONs, and then check for conflicts. Check for any conflicts within numerical values. If you find a conflict, mark the field as CONFLICT and always give the reasoning behind the conflict. However be careful to check if the 2 fields to be compared essentially mean the same thing, in that case return either answer. If either field says TBD, ignore the test and output the test where user answer s not TBD.\nFor example:\nUser Answer 1: \"TBD\"\nUser Answer 2: \"The budget is 15000\"\nResult: \"The budget is 15000\"\n\nUser Answer 1: \"The expected ROI is 5 years\"\nUser Answer 2:\"The ROI is to be within 15 years\"\nResult:\"CONFLICT - The first answer mentions ROI as 5 years while the second answer mentions ROI as 15 years\"\n\nUser Answer 1: \"The KPIs to be measured are speed and accuracy\"\nUser Answer 2: \"TBD\"\nResult: \"The KPIs to be measured are speed and accuracy\"\n\nUser Answer 1: \"There will be 1GB Internet Connection\"\nUser Answer 2: \"There will be EthernetIP present\"\nResult: \"There will be EthernetIP with 1GB internet connection present\"\n\nIn the end only return the filled JSON. DONT RETURN ANYTHING OTHER THAN THE JSON."
            },
            {
                "role": "user",
                "content": "JSON 1:\n"+str(json1)+"\nJSON 2:\n"+str(json2)
            }
        ],
        temperature=0.24,
        max_tokens=5220,
        top_p=1,
        stream=True,
        stop=None,
    )

    filled_json=""
    for chunk in completion:
        filled_json+=chunk.choices[0].delta.content or ""
    
    # completion2 = client.chat.completions.create(
    # model="llama-3.1-70b-versatile",
    # messages=[
    #     {
    #         "role": "system",
    #         "content": "You are a helpful assistant. You will be given a JSON where some subproperties marked as \"User Answer\" which have fields marked as \"CONFLICT\". \nFor these fields, there is also a reason. In a professional tone, formulate a question asking as to what the correct answer should be while explaining the reason clearly. Return an array of all these questions. DONT OUTPUT ANYTHING OTHER THAN THE ARRAY.\n"
    #     },
    #     {
    #         "role": "user",
    #         "content": filled_json
    #                 }
    # ],
    # temperature=0.24,
    # max_tokens=5220,
    # top_p=1,
    # stream=True,
    # stop=None,
    # )
    # questions=""
    # for chunk in completion2:
    #     questions+=chunk.choices[0].delta.content or ""
    return filled_json

def answer_refill(qapair,json_template):
    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    completion2 = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant. You will be given a Question-answer pair. You will be given a json. Some subproperties in the JSONs labelled \"User Answer\" are marked as TBD. Based on the question answer pair, I want you to fill the Answer of the question answer pair as it is into the \"User answer\" subproperty. Check the description of the field against the question and be sure to fill the correct field for the correct question. Make sure you return the full JSON, without missing any field. After filling, merge the two filled JSONs. Then return the final completely filled JSON. DONT OUTPUT ANYTHING OTHER THAN THE JSONS."
            },
            {
                "role": "user",
                "content": "Question Answer:"+str(qapair)+"\nJSON:\n"+str(json_template)
            }
        ],
        temperature=1,
        max_tokens=8000,
        top_p=1,
        stream=True,
        stop=None,
    )
    filled_json=""
    for chunk in completion2:
        filled_json+=chunk.choices[0].delta.content or ""
    # print(filled_json)
    return filled_json

def response_filter(record,key,opt):
    if opt==1:
        client = Groq(api_key=os.getenv("GROQ_API_KEY_1"))
    else:
        client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    completion = client.chat.completions.create(
    model="llama-3.1-70b-versatile",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant. You will be given a big JSON. I want to only extract one key out of that. RETURN THE VARIABLE ASKED FOR ONLY"
        },
        {
            "role": "user",
            "content": "Var="+str(key)+"\nJSON:\n"+str(record)
        }
    ],
    temperature=0.38,
    max_tokens=7830,
    top_p=1,
    stream=True,
    stop=None,
    )
    filled_json=""
    for chunk in completion:
        filled_json+=chunk.choices[0].delta.content or ""
    # print(filled_json)
    return filled_json
def executive_summary_complete(json_template):


    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a professional copyrighter. You will be given a JSON, I want you to create a complete executive summary with headers and subheaders. It should be a structured document. \"User Answer\" are what are the answers you have to focus on. Dont skip any of the Fields in both JSONs. Use the Description to frame the User answer. DONT OUTPUT ANYTHING OTHER THAN THE SUMMARY."
            },
            {
                "role": "user",
                "content": str(json_template)
            }
        ],
        temperature=0.73,
        max_tokens=5610,
        top_p=1,
        stream=True,
        stop=None,
    )
    final_summ=""
    for chunk in completion:
        final_summ+=chunk.choices[0].delta.content or ""
    return final_summ

def executive_summary(questions):


    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant. You will be given an array of engineering questions. There maybe some repetitions in the questions. Remove them.\n\nNow create a professional mail with this approach:\nyou thank the recipient for the last meeting. Then after talking with your team you have a series of questions. You then list out the questions in a bullet point fashion."
            },
            {
                "role": "user",
                "content": str(questions)
            }
        ],
        temperature=0.65,
        max_tokens=5220,
        top_p=1,
        stream=True,
        stop=None,
    )
    final_summ=""
    for chunk in completion:
        final_summ+=chunk.choices[0].delta.content or ""
    return final_summ

def conflict_summary(json):


    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant. You will be given a JSON. The fields where \"User Answer\" is marked as \"CONFLICT\", I want you to make questions asking the difference only for these fields. If either User Answer is marked as TBD, DONT CREATE QUESTIONS FOR THAT. Remember to always include the reason for conflict. The final output should be a mail which outlines all the questions. The mail should say how after discussion with the team you have come up with these questions. Maintain a professional tone. Refer to JSON1 as initial communication. Refer to JSON2 as latest communication. Dont mention the words \"JSON1\" and \"JSON2\" anywhere"
            },
            {
                "role": "user",
                "content": str(json)
            }
        ],
        temperature=0.65,
        max_tokens=7220,
        top_p=1,
        stream=True,
        stop=None,
    )
    final_summ=""
    for chunk in completion:
        final_summ+=chunk.choices[0].delta.content or ""
    return final_summ

def airtable_retrieve(identifier):
    base_id = 'appcl0egQeE4pP5ID'
    table_name = 'tblfQBynpcfdDUywV'
    api_key = os.getenv("AIRTABLE_KEY")

    # API endpoint
    base_url = f'https://api.airtable.com/v0/{base_id}/{table_name}'

    # Headers for authentication
    headers = {
        'Authorization': f'Bearer {api_key}',
        'Content-Type': 'application/json'
    }

    def get_record_by_identifier(identifier):
        try:
            # Construct the filter formula
            filter_formula = f"{{Identifier}} = '{identifier}'"
            
            # Make the API request
            response = requests.get(
                base_url,
                headers=headers,
                params={'filterByFormula': filter_formula}
            )
            
            # Check if the request was successful
            response.raise_for_status()
            
            # Parse the JSON response
            data = response.json()
            
            if data['records']:
                if len(data['records']) > 1:
                    print(f"Warning: Multiple records found for identifier '{identifier}'. Returning the first one.")
                return data['records'][-1]['fields']
            else:
                raise ValueError(f"No record found for identifier '{identifier}'")
        
        except requests.exceptions.RequestException as e:
            print(f"An error occurred while making the request: {str(e)}")
            return None
        except ValueError as e:
            print(str(e))
            return None
        except Exception as e:
            print(f"An unexpected error occurred: {str(e)}")
            return None

    # Example usage
    
    record = get_record_by_identifier(identifier)
    # print(record)
    if record:
        # print("Record found:")
        # for field, value in record.items():
        #     print(f"{field}: {value}")
        return record
    else:
        return "404"

def combine_json_files(directory):
    combined_data = {}
    for filename in os.listdir(directory):
        if filename.endswith('.json'):
            file_path = os.path.join(directory, filename)
            key = os.path.splitext(filename)[0]  # Use filename without extension as key
            with open(file_path, 'r') as file:
                combined_data[key] = json.load(file)
    return combined_data

def prepare_json_string(json_data):
    # Convert the JSON data to a string, escaping any problematic characters
    return json.dumps(json.dumps(json_data))

def airtable_write_main(json_strings,id):
    API_KEY = os.getenv("AIRTABLE_KEY")
    BASE_ID = 'appcl0egQeE4pP5ID'
    TABLE_ID = 'tblfQBynpcfdDUywV'
    
    url = f'https://api.airtable.com/v0/{BASE_ID}/{TABLE_ID}'

    headers = {
        'Authorization': f'Bearer {API_KEY}',
        'Content-Type': 'application/json'
    }

    # Prepare the record for Airtable
    record = {
        "fields": {
            "Identifier": id,  # You may want to generate a unique identifier here
            "BIZ_OBS_JSON": json_strings.get("BIZ_OBS_JSON", "[]"),
            "PROD_VAR_INFO_JSON": json_strings.get("PROD_VAR_INFO_JSON", "[]"),
            "CUSTOMER_DEPENDENCY_JSON": json_strings.get("CUSTOMER_DEPENDENCY_JSON", "[]"),
            "MATERIAL_HANDLING_JSON": json_strings.get("MATERIAL_HANDLING_JSON", "[]"),
            "SOFTWARE_JSON": json_strings.get("SOFTWARE_JSON", "[]"),
            "ACCEPTANCE_JSON": json_strings.get("ACCEPTANCE_JSON", "[]"),
            "OBS_JSON": json_strings.get("OBS_JSON", "[]")
        }
    }

    # Prepare the payload for Airtable
    payload = {
        "records": [record]
    }

    # Make the POST request to add the record
    response = requests.post(url, headers=headers, data=json.dumps(payload))

    # Check if the request was successful
    if response.status_code == 200:
        print("Record added successfully!")
    else:
        print(f"Failed to add record. Status code: {response.status_code}, Error: {response.text}")

def chunk_data(data, chunk_size=10):
    if isinstance(data, dict):
        # If data is a dictionary, convert it to a list of key-value pairs
        items = list(data.items())
    elif isinstance(data, list):
        items = data
    else:
        raise TypeError("Data must be either a dictionary or a list")
    
    return [dict(items[i:i + chunk_size]) for i in range(0, len(items), chunk_size)]

def airtable_write(json_template):

    client = Groq(api_key=os.getenv("GROQ_API_KEY"))

    # Groq inference
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content":  "You are a helpful assistant. You will be given a unstructured JSON. I want you to convert it into a fully structured JSON which will become a structured CSV. The headings of the CSV are to be \\\"Category\\\",\\\"Sub-category\\\",\\\"Description\\\" and \\\"User Answer\\\". So shuffle around the fields accordingly. \nFields marked \"Category\" are to be directly picked as the \"Category\" for the CSV. If there is \"Observation type\", then that becomes the Category.  \nDONT LEAVE ANY FIELD. MAKE SURE ALL FIELDS ARE INCLUDED IN THE RESULT. DONT OUTPUT ANYTHING OTHER THAN THE JSON. ONLY OUTPUT THE JSON.\n"
            },
            {
                "role": "user",
                "content": json_template
            }
        ],
        temperature=0.25,
        max_tokens=8000,
        top_p=1,
        stream=True,
        # response_format={"type": "json_object"},
        stop=None,
    )
    content=""
    for chunk in completion:
        content+=chunk.choices[0].delta.content or ""
    # Get the structured JSON from Groq
    groq_json = json.loads(content)
    with open("groq_json.json", "w") as file:
        json.dump(groq_json, file, indent=4)
    API_KEY = os.getenv("AIRTABLE_KEY")
    BASE_ID = 'appcl0egQeE4pP5ID'
    TABLE_ID = 'tbl2AaOSxyBv6ObR5'
    url = f'https://api.airtable.com/v0/{BASE_ID}/{TABLE_ID}'

    headers = {
        'Authorization': f'Bearer {API_KEY}',
        'Content-Type': 'application/json'
    }

    # Chunk the data into batches of 10
    def chunk_data(data, chunk_size=10):
        for i in range(0, len(data), chunk_size):
            yield data[i:i + chunk_size]

    # Process each chunk and send it to Airtable
    for batch in chunk_data(groq_json):
        # Format the current batch for Airtable API
        airtable_data = {
            "records": [
                {
                    "fields": {
                        "Category": item["Category"],
                        "Sub-category": item["Sub-category"],
                        "Description": item["Description"],
                        "User Answer": item["User Answer"]
                    }
                } for item in batch
            ]
        }
        
        # Make the POST request to add records
        response = requests.post(url, headers=headers, data=json.dumps(airtable_data))
        
        # Check if the request was successful
        if response.status_code == 200:
            print(f"Batch of {len(batch)} records added successfully!")
        else:
            print(f"Failed to add batch. Status code: {response.status_code}, Error: {response.text}")

def process_new_customer(unique_id):
    st.write(f"Processing new customer with ID: {unique_id}")
    st.write("Please upload the first communication:")
    uploaded_file = st.file_uploader("Upload a PDF document", type="pdf")
    if uploaded_file is not None:
        button1(uploaded_file,unique_id)
    # # Add your document processing steps for new customers here
    # # For example:
    # uploaded_file = st.file_uploader("Upload a PDF document", type="pdf")
def button1(uploaded_file,unique_id):   
    st.write("Parsing Document ...")
    st.session_state.text = extract_text_from_pdf(uploaded_file)
    st.write("Running Classification Algorithm...")
    st.session_state.classification_result = classification_LLM(st.session_state.text)
    json_path='observationsJSON.json'
    with open(json_path, 'r') as file:
        obs_json_template = json.load(file)
    final_obs_json = obsjsoncreate(obs_json_template, st.session_state.classification_result, st.session_state.text)
    st.session_state.obs = final_obs_json

    json_path='Biz_Obj.json'
    with open(json_path, 'r') as file:
        bizobj_json = json.load(file)
    final_bizobj_json = bizobjjsoncreate(bizobj_json, st.session_state.text)

    json_path='Prod_var_info.json'
    with open(json_path, 'r') as file:
        prodvarinfo_json = json.load(file)
    final_prodvarinfo_json = bizobjjsoncreate(prodvarinfo_json, st.session_state.text)

    json_path='Material_handling.json'
    with open(json_path, 'r') as file:
        materialhandling_json = json.load(file)
    final_materialhandling_json = bizobjjsoncreate(materialhandling_json, st.session_state.text)

    json_path='software.json'
    with open(json_path, 'r') as file:
        software_json = json.load(file)
    final_software_json = bizobjjsoncreate(software_json, st.session_state.text)

    json_path='Customer_dependency.json'
    with open(json_path, 'r') as file:
        customerdependency_json = json.load(file)
    final_customerdependency_json = bizobjjsoncreate(customerdependency_json, st.session_state.text)

    json_path='acceptance.json'
    with open(json_path, 'r') as file:
        acceptance_json = json.load(file)
    final_acceptance_json = bizobjjsoncreate(acceptance_json, st.session_state.text)

    st.session_state.bizobj = final_bizobj_json
    st.session_state.prodvarinfo = final_prodvarinfo_json
    st.session_state.materialhandling = final_materialhandling_json
    st.session_state.software = final_software_json
    st.session_state.customerdependency = final_customerdependency_json
    st.session_state.acceptance = final_acceptance_json
    st.write("Creating Questions...")
    questionobs = question_create(final_obs_json)
    question_bizobj = question_create(final_bizobj_json)
    question_prodvarinfo = question_create(final_prodvarinfo_json)
    question_materialhandling = question_create(final_materialhandling_json)
    question_software = question_create(final_software_json)
    question_customerdependency = question_create(final_customerdependency_json)
    question_acceptance = question_create(final_acceptance_json)
    totquestions=questionobs+question_bizobj+question_prodvarinfo+question_materialhandling+question_software+question_customerdependency+question_acceptance
    st.write("Creating Question Email...")
    exec_questions=executive_summary(totquestions)
    final_json=final_bizobj_json+final_prodvarinfo_json+final_materialhandling_json+final_software_json+final_customerdependency_json+final_acceptance_json+final_obs_json
    exec_summ=executive_summary_complete(final_json)
    st.write(exec_summ)
    st.write("Here is the Composed Mail for the customers ->")
    st.write(exec_questions)
    json_strings = {
                        "BIZ_OBS_JSON": st.session_state.bizobj,
                        "PROD_VAR_INFO_JSON": st.session_state.prodvarinfo,
                        "CUSTOMER_DEPENDENCY_JSON": st.session_state.customerdependency,
                        "MATERIAL_HANDLING_JSON": st.session_state.materialhandling,
                        "SOFTWARE_JSON": st.session_state.software,
                        "ACCEPTANCE_JSON": st.session_state.acceptance,
                        "OBS_JSON": st.session_state.obs
                } 
    airtable_write_main(json_strings,unique_id)


def process_registered_customer(unique_id):
    st.write(f"Processing registered customer with ID: {unique_id}")
    # Add your document processing steps for registered customers here
    # For example:
    record=airtable_retrieve(unique_id)
    if record == '404':
        st.write("Record Not Found. Please restart and check identifier")
        return
    json_data=record
    # st.write(record)
    # parsed_data=json.loads(str(record))
    obs_json = json_data.get("OBS_JSON")
    biz_obs_json = json_data.get("BIZ_OBS_JSON")
    software_json = json_data.get("SOFTWARE_JSON")
    customer_dependency_json = json_data.get("CUSTOMER_DEPENDENCY_JSON")
    prod_var_info_json = json_data.get("PROD_VAR_INFO_JSON")
    material_handling_json = json_data.get("MATERIAL_HANDLING_JSON")
    acceptance_json = json_data.get("ACCEPTANCE_JSON")
    # st.write(biz_obs_json)
    # biz_obs_json = response_filter(record,"BIZ_OBS_JSON",1)
    # prod_var_info_json = response_filter(record,"PROD_VAR_INFO_JSON",2)
    # material_handling_json = response_filter(record,"MATERIAL_HANDLING_JSON",1)
    # software_json = response_filter(record,"SOFTWARE_JSON",2)
    # customer_dependency_json = response_filter(record,"CUSTOMER_DEPENDENCY_JSON",1)
    # acceptance_json = response_filter(record,"ACCEPTANCE_JSON",2)
    # obs_json = response_filter(record,"OBS_JSON",1)
    st.write("Records Retrieved. Please enter the Questions and Answers:")
    qa=st.chat_input("Please enter the questions given to customer and their answers:")
    if qa:
        qapair=qapair_create(qa)
        # st.write(qapair)
        json_path='Biz_Obj.json'
        with open(json_path, 'r') as file:
            new_bizobj_json = json.load(file)
        filled_bizobj = answer_refill(qapair,new_bizobj_json)

        json_path='Prod_var_info.json'
        with open(json_path, 'r') as file:
            new_prodvarinfo_json = json.load(file)
        filled_prodvar = answer_refill(qapair,new_prodvarinfo_json)

        json_path='Material_handling.json'
        with open(json_path, 'r') as file:
            new_materialhandling_json = json.load(file)
        filled_material_handling = answer_refill(qapair,new_materialhandling_json)

        json_path='software.json'
        with open(json_path, 'r') as file:
            new_software_json = json.load(file)
        filled_software = answer_refill(qapair,new_software_json)

        json_path='Customer_dependency.json'
        with open(json_path, 'r') as file:
            new_customerdependency_json = json.load(file)
        filled_customer_dependency = answer_refill(qapair,new_customerdependency_json)

        json_path='acceptance.json'
        with open(json_path, 'r') as file:
            new_acceptance_json = json.load(file)
        filled_acceptance = answer_refill(qapair,new_acceptance_json)

        # filled_bizobj=answer_refill(qapair,biz_obs_json)
        # filled_prodvar=answer_refill(qapair,prod_var_info_json)
        # filled_material_handling=answer_refill(qapair,material_handling_json)
        # filled_software=answer_refill(qapair,software_json)
        # filled_customer_dependency=answer_refill(qapair,customer_dependency_json)
        filled_obs=answer_refill(qapair,obs_json)
        # filled_acceptance=answer_refill(qapair,acceptance_json)
        # st.write("Biz_obs_JSON")
        # st.write(biz_obs_json)
        # st.write("Filled_JSON")
        # st.write(filled_bizobj)
        print("reached")
        # st.write(filled_bizobj)
        st.write("Checking for Conflicts...")
        conquest_bizobj=conflict_detect(biz_obs_json,filled_bizobj)
        conquest_prodvar=conflict_detect(prod_var_info_json,filled_prodvar)
        conquest_material_handling=conflict_detect(material_handling_json,filled_material_handling)
        conquest_software=conflict_detect(software_json,filled_software)
        conquest_customer_dependency=conflict_detect(customer_dependency_json,filled_customer_dependency)
        conquest_acceptance=conflict_detect(acceptance_json,filled_acceptance)
        conquest_obs=conflict_detect(obs_json,filled_obs)
        # st.write(conquest_bizobj)
        st.write("Executive summary ....")

        st.write("Creating Question Email...")
        totquestions=conquest_obs+conquest_bizobj+conquest_prodvar+conquest_material_handling+conquest_software+conquest_customer_dependency+conquest_acceptance
        final_json=filled_bizobj+filled_prodvar+filled_material_handling+filled_software+filled_customer_dependency+filled_acceptance+filled_obs
        exec_summ=conflict_summary(totquestions)
        # exec_summ=executive_summary(totquestions)
        st.write(exec_summ)
        # final_json=filled_bizobj+filled_prodvar+filled_material_handling+filled_software+filled_customer_dependency+filled_acceptance+filled_obs
        exec_summ=executive_summary_complete(final_json)
        st.write(exec_summ)
        json_strings = {
                        "BIZ_OBS_JSON": filled_bizobj,
                        "PROD_VAR_INFO_JSON": filled_prodvar,
                        "CUSTOMER_DEPENDENCY_JSON": filled_customer_dependency,
                        "MATERIAL_HANDLING_JSON": filled_material_handling,
                        "SOFTWARE_JSON": filled_software,
                        "ACCEPTANCE_JSON": filled_acceptance,
                        "OBS_JSON": filled_obs
                } 
        airtable_write_main(json_strings,unique_id)



def main():
    st.title("Qualitas Sales Chatbot")

    col1, col2 = st.columns(2)

    with col1:
        new_customer = st.button("New Customer")
    with col2:
        registered_customer = st.button("Registered Customer")

    if new_customer:
        st.session_state.workflow = "new"
        st.session_state.step = "id_input"
    elif registered_customer:
        st.session_state.workflow = "registered"
        st.session_state.step = "id_input"

    if "workflow" in st.session_state:
        if st.session_state.step == "id_input":
            st.write("Please Enter a Unique Identifier:")
            unique_id = st.chat_input("Please enter a unique identifier:")
            if unique_id:
                st.session_state.unique_id = unique_id
                st.session_state.step = "processing"
                st.rerun()

        if st.session_state.step == "processing":
            if st.session_state.workflow == "new":
                process_new_customer(st.session_state.unique_id)
                
            else:
                process_registered_customer(st.session_state.unique_id)

            # st.session_state.clear()

if __name__ == "__main__":
    main()