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| import streamlit as st | |
| # import wikipedia | |
| from streamlit_lottie import st_lottie | |
| import regex as re | |
| from streamlit_js_eval import streamlit_js_eval | |
| # from common.utils import * | |
| from data_collector import * | |
| # from langchain_community.llms import Ollama | |
| # from langchain_community.llms import Ollama | |
| import pandas as pd | |
| import json | |
| st.set_page_config(layout="wide") | |
| screen_width = streamlit_js_eval(label="screen.width",js_expressions='screen.width') | |
| screen_height = streamlit_js_eval(label="screen.height",js_expressions='screen.height') | |
| condition_capture = st.session_state | |
| if 'schema' not in condition_capture: | |
| condition_capture['schema'] = {} | |
| if 'prompt' not in condition_capture: | |
| condition_capture['prompt'] = "" | |
| if "count" not in condition_capture: | |
| condition_capture['count'] = 0 | |
| if "prev_schema" not in condition_capture: | |
| condition_capture['prev_schema'] = {} | |
| if "textual_value" not in condition_capture: | |
| condition_capture['textual_value'] = {} | |
| textual_value=None | |
| schema=condition_capture['schema'] | |
| column1,column2 = st.columns(2) | |
| with column2: | |
| if len(condition_capture['schema'])!=0 and len(condition_capture['textual_value'])==0: | |
| # condition_capture['prev_schema'] = condition_capture['schema'] | |
| condition_capture['textual_value']=relevent_value(str(condition_capture['schema']).lower(),50) | |
| if len(condition_capture['schema'])!=0: | |
| html_page = condition_capture['textual_value'][1] | |
| textual_value = condition_capture['textual_value'][0] | |
| st.write("<br>",unsafe_allow_html=True) | |
| with st.container(border=True,height=int(screen_height/2.3)): | |
| st.header("Wikipedia insights") | |
| updated_schema = st.button("Start processing") | |
| selector=st.empty() | |
| start_page=my_bar = st.progress(0, text="Press start processing .") | |
| write =st.empty() | |
| # ) | |
| with column1: | |
| if str(schema)!=str({}): | |
| tabs = st.tabs(["Schema","Data Generation"]) | |
| with tabs[0]: | |
| if str(schema)!=str({}): | |
| schema_column1,schema_column2 = st.columns(2) | |
| with schema_column1: | |
| edited_df = st.data_editor([str(i) for index,i in enumerate(schema)],hide_index=True,use_container_width=True,num_rows='dynamic',height=int(screen_height/3)) | |
| with schema_column2: | |
| number = st.number_input("Number of rows",min_value=1,max_value=1000,value=10) | |
| if number!=condition_capture['count'] and updated_schema: | |
| condition_capture['count'] = number | |
| with open("/home/user/app/animation/edit_file.json") as animate: | |
| url_json=json.load(animate) | |
| st_lottie(url_json,height = int(screen_height/3)) | |
| with tabs[1]: | |
| with open("/home/user/app/animation/no data animation.json") as animate: | |
| url_json=json.load(animate) | |
| dataframe=st.empty() | |
| if condition_capture['count']==0: | |
| st_lottie(url_json,height = int(screen_height/3)) | |
| else: | |
| smart_append=[] | |
| if condition_capture['prev_schema'] != condition_capture['schema']: | |
| condition_capture['prev_schema'] = condition_capture['schema'] | |
| condition_capture['current_append']={} | |
| for text_indexing,store in enumerate(actual_value(textual_value,schema)): | |
| dummy_value =dictionary_formatting(store) | |
| for keys in dummy_value: | |
| while len(dummy_value[keys])>=2: | |
| dummy_value[keys].pop(0) | |
| dummy_value = dictionary_formatting(dummy_value) | |
| if dummy_value != None: | |
| smart_append.append(dummy_value) | |
| print(dummy_value) | |
| for keys in dummy_value: | |
| if keys not in condition_capture['current_append']: | |
| condition_capture['current_append'][str(keys)]=[] | |
| condition_capture['current_append'][str(keys)].append(str([i for i in dummy_value[keys]])) | |
| dataframe.dataframe(condition_capture['current_append']) | |
| if len(condition_capture['current_append'][[i for i in condition_capture['current_append']][-1]])>=condition_capture['count']: | |
| start_page.progress(100, text="Operation completed.") | |
| break | |
| else: | |
| start_page.progress((text_indexing + 1)*(100//condition_capture['count']), text="Operation in progress. Please wait.") | |
| write.write(html_page[[i for i in html_page][text_indexing]],unsafe_allow_html=True) | |
| # print(dummy_value) | |
| # if smart_check(dummy_value)!=True: | |
| # smart_value=verification(dummy_value) | |
| # if statement(condition_capture['schema'],smart_value): | |
| # st.dataframe(smart_value) | |
| condition_capture['current_append']={} | |
| if len(smart_append)==0: | |
| ranger=len(condition_capture['current_append'][[i for i in condition_capture['current_append']][0]]) | |
| for indexing in range(ranger): | |
| working_dict = {} | |
| for j in condition_capture['current_append']: | |
| working_dict[j]=condition_capture['current_append'][j][indexing][0] | |
| smart_append.append(working_dict) | |
| smart_movement = sorting(smart_append) | |
| for keys in smart_movement: | |
| value=eval(keys) | |
| for keys in value: | |
| if keys not in condition_capture['current_append']: | |
| condition_capture['current_append'][str(keys)]=[] | |
| condition_capture['current_append'][str(keys)].append([str(i) for i in value[keys]]) | |
| dataframe.dataframe(condition_capture['current_append']) | |
| for indexing,j in enumerate(smart_movement): | |
| try: | |
| # Convert string to dictionary | |
| dummy_value = eval(j) | |
| # Process dictionary values | |
| for key in dummy_value: | |
| while len(dummy_value[key]) >= 2: | |
| dummy_value[key].pop(0) | |
| # Format dictionary | |
| formatted = dictionary_formatting(dummy_value) | |
| print(formatted) | |
| # Verify and store result | |
| verification_result = verification(formatted) if formatted else None | |
| for j in verification_result: | |
| if j in condition_capture['current_append']: | |
| condition_capture['current_append'][j][indexing]=[str(i) for i in verification_result[j]] | |
| dataframe.dataframe(condition_capture['current_append']) | |
| except: | |
| pass | |
| if len(condition_capture['schema'])==0: | |
| st.markdown( | |
| """ | |
| <div style="text-align: center; font-size: 50px; font-weight: bold; margin-top: 20px;"> | |
| Generate high-quality, realistic data effortlessly. | |
| </div> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| newline_column1,newline_column2 = st.columns(2) | |
| with newline_column2: | |
| with open("/home/user/app/animation/Data_rocket.json") as animate: | |
| url_json=json.load(animate) | |
| st_lottie(url_json,height = int(screen_height/4)) | |
| with newline_column1: | |
| # Instructions for the user | |
| st.markdown(""" | |
| ### How it Works: | |
| - **Step 1:** Enter a prompt to generate data. | |
| - **step 2:** Select the number of rows you want to generate and press start processing. | |
| - **Step 3:** AI will use Wikipedia insights to create realistic outputs. | |
| - **Key Feature:** Less hallucination, more accurate data. | |
| """) | |
| prompt = st.text_input(label="Please use prompt to generate data",value=condition_capture['prompt']) | |
| if prompt != str(condition_capture['prompt']): | |
| condition_capture['prompt'] = prompt | |
| schema = schema_generator(prompt) | |
| condition_capture['schema'] = schema | |
| condition_capture['current_append']={} | |
| st.rerun() |