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| import superimport | |
| import streamlit as st | |
| import os | |
| import pandas as pd | |
| import random | |
| from os.path import join | |
| from src import preprocess_and_load_df, load_agent, ask_agent, decorate_with_code, show_response, get_from_user, load_smart_df, ask_question | |
| from dotenv import load_dotenv | |
| from langchain_groq.chat_models import ChatGroq | |
| load_dotenv("Groq.txt") | |
| Groq_Token = os.environ["GROQ_API_KEY"] | |
| models = {"llama3":"llama3-70b-8192","mixtral": "mixtral-8x7b-32768", "llama2": "llama2-70b-4096", "gemma": "gemma-7b-it"} | |
| self_path = os.path.dirname(os.path.abspath(__file__)) | |
| # Using HTML and CSS to center the title | |
| st.write( | |
| """ | |
| <style> | |
| .title { | |
| text-align: center; | |
| color: #17becf; | |
| } | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| # Displaying the centered title | |
| st.markdown("<h2 class='title'>VayuBuddy</h2>", unsafe_allow_html=True) | |
| # os.environ["PANDASAI_API_KEY"] = "$2a$10$gbmqKotzJOnqa7iYOun8eO50TxMD/6Zw1pLI2JEoqncwsNx4XeBS2" | |
| # with open(join(self_path, "context1.txt")) as f: | |
| # context = f.read().strip() | |
| # agent = load_agent(join(self_path, "app_trial_1.csv"), context) | |
| # df = preprocess_and_load_df(join(self_path, "Data.csv")) | |
| # inference_server = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2" | |
| # inference_server = "https://api-inference.huggingface.co/models/codellama/CodeLlama-13b-hf" | |
| # inference_server = "https://api-inference.huggingface.co/models/pandasai/bamboo-llm" | |
| model_name = st.sidebar.selectbox("Select LLM:", ["llama3","mixtral", "gemma"]) | |
| questions = ('Custom Prompt', | |
| 'Plot the monthly average PM2.5 for the year 2023.', | |
| 'Which month has the highest average PM2.5 overall?', | |
| 'Which month has the highest PM2.5 overall?', | |
| 'Which month has the highest average PM2.5 in 2023 for Mumbai?', | |
| 'Plot and compare monthly timeseries of pollution for Mumbai and Bengaluru.', | |
| 'Plot the yearly average PM2.5.', | |
| 'Plot the monthly average PM2.5 of Delhi, Mumbai and Bengaluru for the year 2022.', | |
| 'Which month has the highest pollution?', | |
| 'Plot the monthly average PM2.5 of Delhi for the year 2022.', | |
| 'Which city has the highest PM2.5 level in July 2022?', | |
| 'Plot and compare monthly timeseries of PM2.5 for Mumbai and Bengaluru.', | |
| 'Plot and compare the monthly average PM2.5 of Delhi, Mumbai and Bengaluru for the year 2022.', | |
| 'Plot the monthly average PM2.5.', | |
| 'Plot the monthly average PM10 for the year 2023.', | |
| 'Which month has the highest PM2.5?', | |
| 'Plot the monthly average PM2.5 of Delhi for the year 2022.', | |
| 'Plot the monthly average PM2.5 of Bengaluru for the year 2022.', | |
| 'Plot the monthly average PM2.5 of Mumbai for the year 2022.', | |
| 'Which state has the highest average PM2.5?', | |
| 'Plot monthly PM2.5 in Gujarat for 2023.', | |
| 'What is the name of the month with the highest average PM2.5 overall?') | |
| waiting_lines = ("Thinking...", "Just a moment...", "Let me think...", "Working on it...", "Processing...", "Hold on...", "One moment...", "On it...") | |
| # agent = load_agent(df, context="", inference_server=inference_server, name=model_name) | |
| # Initialize chat history | |
| if "responses" not in st.session_state: | |
| st.session_state.responses = [] | |
| # Display chat responses from history on app rerun | |
| for response in st.session_state.responses: | |
| if not response["no_response"]: | |
| show_response(st, response) | |
| show = True | |
| if prompt := st.sidebar.selectbox("Select a Prompt:", questions): | |
| # add a note "select custom prompt to ask your own question" | |
| st.sidebar.info("Select 'Custom Prompt' to ask your own question.") | |
| if prompt == 'Custom Prompt': | |
| show = False | |
| # React to user input | |
| prompt = st.chat_input("Ask me anything about air quality!", key=10) | |
| if prompt : show = True | |
| if show : | |
| # Add user input to chat history | |
| response = get_from_user(prompt) | |
| response["no_response"] = False | |
| st.session_state.responses.append(response) | |
| # Display user input | |
| show_response(st, response) | |
| no_response = False | |
| # select random waiting line | |
| with st.spinner(random.choice(waiting_lines)): | |
| ran = False | |
| for i in range(5): | |
| llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0.1) | |
| df_check = pd.read_csv("Data.csv") | |
| df_check["Timestamp"] = pd.to_datetime(df_check["Timestamp"]) | |
| df_check = df_check.head(5) | |
| new_line = "\n" | |
| parameters = {"font.size": 18} | |
| template = f"""```python | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| plt.rcParams.update({parameters}) | |
| df = pd.read_csv("Data.csv") | |
| df["Timestamp"] = pd.to_datetime(df["Timestamp"]) | |
| def calculator(Pollutant, concentration): | |
| Calculator_index = Pollutant | |
| breakpoints_low = {{ | |
| "O3": [0, 50, 100, 168, 208, 748], | |
| "PM2.5": [0, 30, 60, 90, 120, 250], | |
| "PM10": [0, 50, 100, 250, 350, 430], | |
| "CO": [0, 1000, 2000, 10000, 17000, 34000], | |
| "SO2": [0, 40, 80, 380, 800, 1600], | |
| "NO2": [0, 40, 80, 180, 280, 400] | |
| }} | |
| breakpoints_high = {{ | |
| "O3": [50, 100, 168, 208, 748,1000], | |
| "PM2.5": [30, 60, 90, 120, 250,1000], | |
| "PM10": [50, 100, 250, 350, 430,1000], | |
| "CO": [1000, 2000, 10000, 17000, 34000,50000], | |
| "SO2": [40, 80, 380, 800, 1600,2000], | |
| "NO2": [ 40, 80, 180, 280, 400,1000] | |
| }} | |
| # Define corresponding AQI categories | |
| categories_low= [0, 50, 100, 200, 300, 400] | |
| categories_high = [50, 100, 200, 300, 400,500] | |
| # Find the appropriate AQI category based on concentration | |
| for i in range(len(breakpoints_high[Calculator_index])): | |
| if concentration <= breakpoints_high[Calculator_index][i]: | |
| BPHI = breakpoints_high[Calculator_index][i] | |
| IHI = categories_high[i] | |
| # Calculate AQI using India formula | |
| #AQI = ((categories[i] - categories[i-1]) / (breakpoints[Calculator_index][i] - breakpoints[Calculator_index][i-1])) * (concentration - breakpoints[Calculator_index][i-1]) + categories[i-1] | |
| #st.sidebar.write(f"The Air Quality Index (AQI) for {{Calculator_index}} is: {{AQI}}") | |
| break | |
| for i in range(len(breakpoints_low[Calculator_index])): | |
| if concentration >= breakpoints_low[Calculator_index][i]: | |
| BPLI = breakpoints_low[Calculator_index][i] | |
| ILI = categories_low[i] | |
| # Calculate AQI using India formula | |
| #AQI = ((categories[i] - categories[i-1]) / (breakpoints[Calculator_index][i] - breakpoints[Calculator_index][i-1])) * (concentration - breakpoints[Calculator_index][i-1]) + categories[i-1] | |
| #st.sidebar.write(f"The Air Quality Index (AQI) for {{Calculator_index}} is: {{AQI}}") | |
| break | |
| AQI = ((IHI - ILI) / (BPHI - BPLI)) * (round(concentration) - BPLI) + ILI | |
| return AQI | |
| # df.dtypes | |
| {new_line.join(map(lambda x: '# '+x, str(df_check.dtypes).split(new_line)))} | |
| # {prompt.strip()} | |
| # <your code here> | |
| ``` | |
| """ | |
| query = f"""I have a pandas dataframe data of PM2.5 and PM10. | |
| * Frequency of data is daily. | |
| * `pollution` generally means `PM2.5`. | |
| * You already have df, so don't read the csv file | |
| * Don't print, but save result in a variable `answer` and make it global. | |
| * Unless explicitly mentioned, don't consider the result as a plot. | |
| * PM2.5 guidelines: India: 60, WHO: 15. | |
| * PM10 guidelines: India: 100, WHO: 50. | |
| * If result is a plot, show the India and WHO guidelines in the plot. | |
| * If result is a plot make it in tight layout, save it and save path in `answer`. Example: `answer='plot.png'` | |
| * If result is not a plot, save it as a string in `answer`. Example: `answer='The city is Mumbai'` | |
| * Whenever you do any sort of aggregation, report the corresponding standard deviation, standard error and the number of data points for that aggregation. | |
| * Whenever you're reporting a floating point number, round it to 2 decimal places. | |
| * Always report the unit of the data. Example: `The average PM2.5 is 45.67 µg/m³` | |
| Complete the following code. | |
| {template} | |
| """ | |
| answer = llm.invoke(query) | |
| code = f""" | |
| {template.split("```python")[1].split("```")[0]} | |
| {answer.content.split("```python")[1].split("```")[0]} | |
| """ | |
| # update variable `answer` when code is executed | |
| try: | |
| exec(code) | |
| ran = True | |
| no_response = False | |
| except Exception as e: | |
| no_response = True | |
| exception = e | |
| response = {"role": "assistant", "content": answer, "gen_code": code, "ex_code": code, "last_prompt": prompt, "no_response": no_response} | |
| # Get response from agent | |
| # response = ask_question(model_name=model_name, question=prompt) | |
| # response = ask_agent(agent, prompt) | |
| if ran: | |
| break | |
| if no_response: | |
| st.error(f"Failed to generate right output due to the following error:\n\n{exception}") | |
| # Add agent response to chat history | |
| st.session_state.responses.append(response) | |
| # Display agent response | |
| if not no_response: | |
| show_response(st, response) | |
| del prompt |