Updated with feedback option
Browse files- .gitignore +2 -0
- Groq.txt +0 -1
- app.py +215 -428
- questions.txt +20 -20
- src.py +3 -1
.gitignore
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*.pyc
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*.png
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Groq.txt
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GROQ_API_KEY = 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
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app.py
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# import streamlit as st
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# import os
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# import pandas as pd
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# import random
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# from os.path import join
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# from src import preprocess_and_load_df, load_agent, ask_agent, decorate_with_code, show_response, get_from_user, load_smart_df, ask_question
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# from dotenv import load_dotenv
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# from langchain_groq.chat_models import ChatGroq
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# load_dotenv("Groq.txt")
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# Groq_Token = os.environ["GROQ_API_KEY"]
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# models = {"llama3":"llama3-70b-8192","mixtral": "mixtral-8x7b-32768", "llama2": "llama2-70b-4096", "gemma": "gemma-7b-it"}
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# self_path = os.path.dirname(os.path.abspath(__file__))
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# # Using HTML and CSS to center the title
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# st.write(
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# """
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# <style>
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# .title {
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# text-align: center;
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# color: #17becf;
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# }
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# """,
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# unsafe_allow_html=True,
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# )
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# # Displaying the centered title
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# st.markdown("<h2 class='title'>VayuBuddy</h2>", unsafe_allow_html=True)
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# st.markdown("<div style='text-align:center; padding: 20px;'>VayuBuddy makes pollution monitoring easier by bridging the gap between users and datasets.<br>No coding required—just meaningful insights at your fingertips!</div>", unsafe_allow_html=True)
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# # Center-aligned instruction text with bold formatting
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# st.markdown("<div style='text-align:center;'>Choose a query from <b>Select a prompt</b> or type a query in the <b>chat box</b>, select a <b>LLM</b> (Large Language Model), and press enter to generate a response.</div>", unsafe_allow_html=True)
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# # os.environ["PANDASAI_API_KEY"] = "$2a$10$gbmqKotzJOnqa7iYOun8eO50TxMD/6Zw1pLI2JEoqncwsNx4XeBS2"
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# # with open(join(self_path, "context1.txt")) as f:
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# # context = f.read().strip()
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# # agent = load_agent(join(self_path, "app_trial_1.csv"), context)
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# # df = preprocess_and_load_df(join(self_path, "Data.csv"))
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# # inference_server = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
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# # inference_server = "https://api-inference.huggingface.co/models/codellama/CodeLlama-13b-hf"
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# # inference_server = "https://api-inference.huggingface.co/models/pandasai/bamboo-llm"
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# model_name = st.sidebar.selectbox("Select LLM:", ["llama3","mixtral", "gemma"])
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# questions = ('Custom Prompt',
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# 'Plot the monthly average PM2.5 for the year 2023.',
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# 'Which month in which year has the highest average PM2.5 overall?',
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# 'Which month in which year has the highest PM2.5 overall?',
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# 'Which month has the highest average PM2.5 in 2023 for Mumbai?',
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# 'Plot and compare monthly timeseries of pollution for Mumbai and Bengaluru.',
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# 'Plot the yearly average PM2.5.',
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# 'Plot the monthly average PM2.5 of Delhi, Mumbai and Bengaluru for the year 2022.',
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# 'Which month has the highest pollution?',
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# 'Which city has the highest PM2.5 level in July 2022?',
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# 'Plot and compare monthly timeseries of PM2.5 for Mumbai and Bengaluru.',
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# 'Plot and compare the monthly average PM2.5 of Delhi, Mumbai and Bengaluru for the year 2022.',
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# 'Plot the monthly average PM2.5.',
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# 'Plot the monthly average PM10 for the year 2023.',
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# 'Which (month, year) has the highest PM2.5?',
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# 'Plot the monthly average PM2.5 of Delhi for the year 2022.',
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# 'Plot the monthly average PM2.5 of Bengaluru for the year 2022.',
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# 'Plot the monthly average PM2.5 of Mumbai for the year 2022.',
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# 'Which state has the highest average PM2.5?',
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# 'Plot monthly PM2.5 in Gujarat for 2023.',
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# 'What is the name of the month with the highest average PM2.5 overall?')
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# waiting_lines = ("Thinking...", "Just a moment...", "Let me think...", "Working on it...", "Processing...", "Hold on...", "One moment...", "On it...")
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# # agent = load_agent(df, context="", inference_server=inference_server, name=model_name)
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# # Initialize chat history
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# if "responses" not in st.session_state:
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# st.session_state.responses = []
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# # Display chat responses from history on app rerun
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# for response in st.session_state.responses:
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# if not response["no_response"]:
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# show_response(st, response)
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# show = True
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# if prompt := st.sidebar.selectbox("Select a Prompt:", questions):
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# # add a note "select custom prompt to ask your own question"
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# st.sidebar.info("Select 'Custom Prompt' to ask your own question.")
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# if prompt == 'Custom Prompt':
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# show = False
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# # React to user input
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# prompt = st.chat_input("Ask me anything about air quality!", key=10)
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# if prompt : show = True
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# if show :
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# # Add user input to chat history
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# response = get_from_user(prompt)
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# response["no_response"] = False
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# st.session_state.responses.append(response)
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# # Display user input
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# show_response(st, response)
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# no_response = False
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# # select random waiting line
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# with st.spinner(random.choice(waiting_lines)):
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# ran = False
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# for i in range(1):
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# print(f"Attempt {i+1}")
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# llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0)
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# df_check = pd.read_csv("Data.csv")
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# df_check["Timestamp"] = pd.to_datetime(df_check["Timestamp"])
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# df_check = df_check.head(5)
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# new_line = "\n"
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# parameters = {"font.size": 12}
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# template = f"""```python
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# import pandas as pd
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# import matplotlib.pyplot as plt
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# # plt.rcParams.update({parameters})
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# df = pd.read_csv("Data.csv")
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# df["Timestamp"] = pd.to_datetime(df["Timestamp"])
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# import geopandas as gpd
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# india = gpd.read_file("https://gist.githubusercontent.com/jbrobst/56c13bbbf9d97d187fea01ca62ea5112/raw/e388c4cae20aa53cb5090210a42ebb9b765c0a36/india_states.geojson")
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# india.loc[india['ST_NM'].isin(['Ladakh', 'Jammu & Kashmir']), 'ST_NM'] = 'Jammu and Kashmir'
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# # df.dtypes
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# {new_line.join(map(lambda x: '# '+x, str(df_check.dtypes).split(new_line)))}
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# # {prompt.strip()}
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# # <your code here>
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# ```
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# """
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# query = f"""I have a pandas dataframe data of PM2.5 and PM10.
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# * The columns are 'Timestamp', 'station', 'PM2.5', 'PM10', 'address', 'city', 'latitude', 'longitude',and 'state'.
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# * Frequency of data is daily.
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# * `pollution` generally means `PM2.5`.
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# * You already have df, so don't read the csv file
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# * Don't print anything, but save result in a variable `answer` and make it global.
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# * Unless explicitly mentioned, don't consider the result as a plot.
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# * PM2.5 guidelines: India: 60, WHO: 15.
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# * PM10 guidelines: India: 100, WHO: 50.
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# * If result is a plot, show the India and WHO guidelines in the plot.
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# * If result is a plot make it in tight layout, save it and save path in `answer`. Example: `answer='plot.png'`
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# * If result is a plot, rotate x-axis tick labels by 45 degrees,
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# * If result is not a plot, save it as a string in `answer`. Example: `answer='The city is Mumbai'`
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# * I have a geopandas.geodataframe india containining the coordinates required to plot Indian Map with states.
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# * If the query asks you to plot on India Map, use that geodataframe to plot and then add more points as per the requirements using the similar code as follows : v = ax.scatter(df['longitude'], df['latitude']). If the colorbar is required, use the following code : plt.colorbar(v)
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# * If the query asks you to plot on India Map plot the India Map in Beige color
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# * Whenever you do any sort of aggregation, report the corresponding standard deviation, standard error and the number of data points for that aggregation.
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# * Whenever you're reporting a floating point number, round it to 2 decimal places.
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# * Always report the unit of the data. Example: `The average PM2.5 is 45.67 µg/m³`
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# Complete the following code.
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# {template}
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# """
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# answer = None
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# code = None
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# try:
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# answer = llm.invoke(query)
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# code = f"""
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# {template.split("```python")[1].split("```")[0]}
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# {answer.content.split("```python")[1].split("```")[0]}
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# """
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# # update variable `answer` when code is executed
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# exec(code)
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# ran = True
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# no_response = False
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# except Exception as e:
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# no_response = True
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# exception = e
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# if code is not None:
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# answer = f"!!!Faced an error while working on your query. Please try again!!!"
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# if type(answer) != str:
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# answer = f"!!!Faced an error while working on your query. Please try again!!!"
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# response = {"role": "assistant", "content": answer, "gen_code": code, "ex_code": code, "last_prompt": prompt, "no_response": no_response}
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# # Get response from agent
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# # response = ask_question(model_name=model_name, question=prompt)
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# # response = ask_agent(agent, prompt)
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# if ran:
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# break
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# # Display agent response
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# if code is not None:
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# # Add agent response to chat history
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# print("Adding response")
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# st.session_state.responses.append(response)
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# show_response(st, response)
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# if no_response:
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# print("No response")
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# st.error(f"Failed to generate right output due to the following error:\n\n{exception}")
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# prompt = 'Custom Prompt'
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####################################################Added User Feedback###################################################
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import streamlit as st
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import os
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import pandas as pd
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import random
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from os.path import join
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from src import preprocess_and_load_df, load_agent, ask_agent, decorate_with_code, show_response, get_from_user, load_smart_df, ask_question
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from dotenv import load_dotenv
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from langchain_groq.chat_models import ChatGroq
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from datasets import Dataset, load_dataset, concatenate_datasets
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import streamlit as st
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from streamlit_feedback import streamlit_feedback
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import
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from huggingface_hub import login, HfFolder
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import os
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token = os.getenv("HF_TOKEN") # Replace "YOUR_AUTHENTICATION_TOKEN" with your actual token
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shape_file = os.getenv("SHAPE_FILE")
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# Login using the token
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login(token=token)
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model_name = st.sidebar.selectbox("Select LLM:", ["llama3","mixtral", "gemma"])
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contact_details = """
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**Feel free to reach out to us:**
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- [Nipun Batra](mailto:nipun.batra@iitgn.ac.in)
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- [Zeel B Patel](mailto:patel_zeel@iitgn.ac.in)
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- [Yash J Bachwana](mailto:yash.bachwana@iitgn.ac.in)
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"""
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for _ in range(9):
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st.sidebar.markdown(" ")
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# Display contact details with message
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st.sidebar.markdown("<hr>", unsafe_allow_html=True)
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st.sidebar.markdown(contact_details, unsafe_allow_html=True)
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# Function to push feedback data to Hugging Face Hub dataset
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def push_to_dataset(feedback, comments,output,code,error):
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# Load existing dataset or create a new one if it doesn't exist
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try:
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ds = load_dataset("YashB1/Feedbacks_eoc", split="evaluation")
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except FileNotFoundError:
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# If dataset doesn't exist, create a new one
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ds = Dataset.from_dict({"feedback": [], "comments": [], "error": [], "output": [], "code": []})
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# Add new feedback to the dataset
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new_data = {"feedback": [feedback], "comments": [comments], "error": [error], "output": [output], "code": [code]} # Convert feedback and comments to lists
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new_data = Dataset.from_dict(new_data)
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ds = concatenate_datasets([ds, new_data])
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# Push the updated dataset to Hugging Face Hub
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ds.push_to_hub("YashB1/Feedbacks_eoc", split="evaluation")
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load_dotenv("Groq.txt")
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Groq_Token = os.environ["GROQ_API_KEY"]
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models = {"llama3":"llama3-70b-8192","mixtral": "mixtral-8x7b-32768", "llama2": "llama2-70b-4096", "gemma": "gemma-7b-it"}
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self_path = os.path.dirname(os.path.abspath(__file__))
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# inference_server = "https://api-inference.huggingface.co/models/codellama/CodeLlama-13b-hf"
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# inference_server = "https://api-inference.huggingface.co/models/pandasai/bamboo-llm"
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if 'question_state' not in st.session_state:
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st.session_state.question_state = False
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if 'fbk' not in st.session_state:
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st.session_state.fbk = str(uuid.uuid4())
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if 'feedback' not in st.session_state:
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st.session_state.feedback = None
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if "chat_history" not in st.session_state:
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st.session_state.chat_history = []
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st.write(entry["question"])
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# st.write(entry["answer"])
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# print(entry["answer"])
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show_response(st, entry["answer"])
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# is a stopper. If user hits the feedback button, streamlit
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# reruns the code from top and we cannot enter back because
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# of that chat_input.
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st.session_state.prompt = question
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st.session_state.question_state = True
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if st.session_state.question_state:
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import pandas as pd
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import matplotlib.pyplot as plt
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@@ -398,113 +209,89 @@ df = pd.read_csv("Data.csv")
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df["Timestamp"] = pd.to_datetime(df["Timestamp"])
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import geopandas as gpd
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-
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india = gpd.read_file(f"{shape_file}")
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india.loc[india['ST_NM'].isin(['Ladakh', 'Jammu & Kashmir']), 'ST_NM'] = 'Jammu and Kashmir'
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-
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# df.dtypes
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{new_line.join(map(lambda x: '# '+x, str(df_check.dtypes).split(new_line)))}
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-
# {
|
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# <your code here>
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```
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"""
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"""
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answer = None
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code = None
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exception = None
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try:
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answer = llm.invoke(query)
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code = f"""
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{template.split("```python")[1].split("```")[0]}
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{answer.content.split("```python")[1].split("```")[0]}
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"""
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answer = f"!!!Faced an error while working on your query. Please try again!!!"
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answer = f"!!!Faced an error while working on your query. Please try again!!!"
|
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-
response = {"role": "assistant", "content": answer, "gen_code": code, "ex_code": code, "last_prompt": st.session_state.prompt, "no_response": no_response,"exception": exception}
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# print(response)
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if response['content'][-4:] == ".gif" :
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# Provide a button to show the gif, we don't want it to run forever
|
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st.image(response['content'], use_column_width=True)
|
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response['content'] = ""
|
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print("Adding response : ")
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"answer": response,
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"message_id": message_id,
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})
|
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-
display_answer()
|
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-
|
488 |
-
|
489 |
-
if no_response:
|
490 |
-
print("No response")
|
491 |
-
st.error(f"Failed to generate right output due to the following error:\n\n{exception}")
|
492 |
-
|
493 |
-
|
494 |
-
# display_answer()
|
495 |
-
# Pressing a button in feedback reruns the code.
|
496 |
-
st.session_state.feedback = streamlit_feedback(
|
497 |
-
feedback_type="thumbs",
|
498 |
-
optional_text_label="[Optional]",
|
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align="flex-start",
|
500 |
-
key=st.session_state.fbk,
|
501 |
-
on_submit=fbcb
|
502 |
-
)
|
503 |
-
print("FeedBack",st.session_state.feedback)
|
504 |
-
if st.session_state.feedback :
|
505 |
-
push_to_dataset(st.session_state.feedback['score'],st.session_state.feedback['text'],answer,code,exception)
|
506 |
-
st.success("Feedback submitted successfully!")
|
507 |
-
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|
1 |
import streamlit as st
|
2 |
import os
|
3 |
+
import json
|
4 |
import pandas as pd
|
5 |
import random
|
6 |
from os.path import join
|
7 |
+
from datetime import datetime
|
8 |
from src import preprocess_and_load_df, load_agent, ask_agent, decorate_with_code, show_response, get_from_user, load_smart_df, ask_question
|
9 |
from dotenv import load_dotenv
|
10 |
from langchain_groq.chat_models import ChatGroq
|
|
|
|
|
|
|
|
|
11 |
from streamlit_feedback import streamlit_feedback
|
12 |
+
from huggingface_hub import HfApi
|
|
|
|
|
|
|
13 |
|
14 |
+
load_dotenv()
|
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|
15 |
Groq_Token = os.environ["GROQ_API_KEY"]
|
16 |
+
hf_token = os.environ["HF_TOKEN"]
|
17 |
models = {"llama3":"llama3-70b-8192","mixtral": "mixtral-8x7b-32768", "llama2": "llama2-70b-4096", "gemma": "gemma-7b-it"}
|
18 |
|
19 |
self_path = os.path.dirname(os.path.abspath(__file__))
|
|
|
47 |
# inference_server = "https://api-inference.huggingface.co/models/codellama/CodeLlama-13b-hf"
|
48 |
# inference_server = "https://api-inference.huggingface.co/models/pandasai/bamboo-llm"
|
49 |
|
50 |
+
model_name = st.sidebar.selectbox("Select LLM:", ["llama3","mixtral", "gemma"])
|
|
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|
51 |
|
52 |
+
questions = ['Custom Prompt']
|
53 |
+
with open(join(self_path, "questions.txt")) as f:
|
54 |
+
questions += f.read().split("\n")
|
|
|
|
|
|
|
|
|
|
|
55 |
|
56 |
+
waiting_lines = ("Thinking...", "Just a moment...", "Let me think...", "Working on it...", "Processing...", "Hold on...", "One moment...", "On it...")
|
57 |
|
58 |
+
# agent = load_agent(df, context="", inference_server=inference_server, name=model_name)
|
59 |
|
60 |
+
# Initialize chat history
|
61 |
+
if "responses" not in st.session_state:
|
62 |
+
st.session_state.responses = []
|
63 |
|
64 |
+
### Old code for feedback
|
65 |
+
# def push_to_dataset(feedback, comments,output,code,error):
|
66 |
+
# # Load existing dataset or create a new one if it doesn't exist
|
67 |
+
# try:
|
68 |
+
# ds = load_dataset("YashB1/Feedbacks_eoc", split="evaluation")
|
69 |
+
# except FileNotFoundError:
|
70 |
+
# # If dataset doesn't exist, create a new one
|
71 |
+
# ds = Dataset.from_dict({"feedback": [], "comments": [], "error": [], "output": [], "code": []})
|
72 |
+
|
73 |
+
# # Add new feedback to the dataset
|
74 |
+
# new_data = {"feedback": [feedback], "comments": [comments], "error": [error], "output": [output], "code": [code]} # Convert feedback and comments to lists
|
75 |
+
# new_data = Dataset.from_dict(new_data)
|
76 |
+
|
77 |
+
# ds = concatenate_datasets([ds, new_data])
|
78 |
+
|
79 |
+
# # Push the updated dataset to Hugging Face Hub
|
80 |
+
# ds.push_to_hub("YashB1/Feedbacks_eoc", split="evaluation")
|
81 |
+
|
82 |
+
def upload_feedback():
|
83 |
+
print("Uploading feedback")
|
84 |
+
data = {
|
85 |
+
"feedback": feedback['score'],
|
86 |
+
"comment": feedback['text'], "error": error, "output": output, "prompt": last_prompt, "code": code}
|
87 |
|
88 |
+
# generate a random file name based on current time-stamp: YYYY-MM-DD_HH-MM-SS
|
89 |
+
random_folder_name = str(datetime.now()).replace(" ", "_").replace(":", "-").replace(".", "-")
|
90 |
+
print("Random folder:", random_folder_name)
|
91 |
+
save_path = f"/tmp/vayubuddy_feedback.md"
|
92 |
+
path_in_repo = f"data/{random_folder_name}/feedback.md"
|
93 |
+
with open(save_path, "w") as f:
|
94 |
+
template = f"""Prompt: {last_prompt}
|
95 |
|
96 |
+
Output: {output}
|
97 |
|
98 |
+
Code:
|
99 |
|
100 |
+
```py
|
101 |
+
{code}
|
102 |
+
```
|
|
|
|
|
|
|
|
|
|
|
|
|
103 |
|
104 |
+
Error: {error}
|
105 |
|
106 |
+
Feedback: {feedback['score']}
|
107 |
|
108 |
+
Comments: {feedback['text']}
|
109 |
+
"""
|
110 |
+
|
111 |
+
print(template, file=f)
|
|
|
112 |
|
113 |
+
api = HfApi(token=hf_token)
|
114 |
+
api.upload_file(
|
115 |
+
path_or_fileobj=save_path,
|
116 |
+
path_in_repo=path_in_repo,
|
117 |
+
repo_id="SustainabilityLabIITGN/VayuBuddy_Feedback",
|
118 |
+
repo_type="dataset",
|
119 |
+
)
|
120 |
+
if status['is_image']:
|
121 |
+
api.upload_file(
|
122 |
+
path_or_fileobj=output,
|
123 |
+
path_in_repo=f"data/{random_folder_name}/plot.png",
|
124 |
+
repo_id="SustainabilityLabIITGN/VayuBuddy_Feedback",
|
125 |
+
repo_type="dataset",
|
126 |
+
)
|
127 |
+
|
128 |
+
print("Feedback uploaded successfully!")
|
129 |
+
|
130 |
+
# Display chat responses from history on app rerun
|
131 |
+
print("#"*10)
|
132 |
+
for response_id, response in enumerate(st.session_state.responses):
|
133 |
+
status = show_response(st, response)
|
134 |
+
if response["role"] == "assistant":
|
135 |
+
feedback_key = f"feedback_{int(response_id/2)}"
|
136 |
+
print("response_id", response_id, "feedback_key", feedback_key)
|
137 |
+
|
138 |
+
error = response["error"]
|
139 |
+
output = response["content"]
|
140 |
+
last_prompt = response["last_prompt"]
|
141 |
+
code = response["gen_code"]
|
142 |
+
|
143 |
+
if "feedback" in st.session_state.responses[response_id]:
|
144 |
+
st.write("Feedback:", st.session_state.responses[response_id]["feedback"])
|
145 |
+
else:
|
146 |
+
## !!! This does on work on Safari !!!
|
147 |
+
# feedback = streamlit_feedback(feedback_type="thumbs",
|
148 |
+
# optional_text_label="[Optional] Please provide extra information", on_submit=upload_feedback, key=feedback_key)
|
149 |
+
|
150 |
+
# Display thumbs up/down buttons for feedback
|
151 |
+
thumbs = st.radio("We would appreciate your feedback!", ('👍', '👎'), index=None, key=feedback_key)
|
152 |
+
|
153 |
+
if thumbs:
|
154 |
+
# Text input for comments
|
155 |
+
comments = st.text_area("[Optional] Please provide extra information", key=feedback_key+"_comments")
|
156 |
+
feedback = {"score": thumbs, "text": comments}
|
157 |
+
if st.button("Submit", on_click=upload_feedback, key=feedback_key+"_submit"):
|
158 |
+
st.session_state.responses[response_id]["feedback"] = feedback
|
159 |
+
st.success("Feedback uploaded successfully!")
|
160 |
+
|
161 |
+
|
162 |
+
print("#"*10)
|
163 |
+
|
164 |
+
show = True
|
165 |
+
prompt = st.sidebar.selectbox("Select a Prompt:", questions, key="prompt_key")
|
166 |
+
if prompt == 'Custom Prompt':
|
167 |
+
show = False
|
168 |
+
# React to user input
|
169 |
+
prompt = st.chat_input("Ask me anything about air quality!", key=1000)
|
170 |
+
if prompt :
|
171 |
+
show = True
|
172 |
+
|
173 |
+
if "last_prompt" in st.session_state:
|
174 |
+
last_prompt = st.session_state["last_prompt"]
|
175 |
+
last_model_name = st.session_state["last_model_name"]
|
176 |
+
if (prompt == last_prompt) and (model_name == last_model_name):
|
177 |
+
show = False
|
178 |
+
|
179 |
+
if prompt:
|
180 |
+
st.sidebar.info("Select 'Custom Prompt' to ask your own questions.")
|
181 |
+
|
182 |
+
if show:
|
183 |
+
# Add user input to chat history
|
184 |
+
user_response = get_from_user(prompt)
|
185 |
+
st.session_state.responses.append(user_response)
|
186 |
+
|
187 |
+
# select random waiting line
|
188 |
+
with st.spinner(random.choice(waiting_lines)):
|
189 |
+
ran = False
|
190 |
+
for i in range(1):
|
191 |
+
print(f"Attempt {i+1}")
|
192 |
+
llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0)
|
193 |
+
|
194 |
+
df_check = pd.read_csv("Data.csv")
|
195 |
+
df_check["Timestamp"] = pd.to_datetime(df_check["Timestamp"])
|
196 |
+
df_check = df_check.head(5)
|
197 |
+
|
198 |
+
new_line = "\n"
|
199 |
+
|
200 |
+
parameters = {"font.size": 12}
|
201 |
+
|
202 |
+
template = f"""```python
|
203 |
import pandas as pd
|
204 |
import matplotlib.pyplot as plt
|
205 |
|
|
|
209 |
df["Timestamp"] = pd.to_datetime(df["Timestamp"])
|
210 |
|
211 |
import geopandas as gpd
|
212 |
+
india = gpd.read_file("https://gist.githubusercontent.com/jbrobst/56c13bbbf9d97d187fea01ca62ea5112/raw/e388c4cae20aa53cb5090210a42ebb9b765c0a36/india_states.geojson")
|
|
|
213 |
india.loc[india['ST_NM'].isin(['Ladakh', 'Jammu & Kashmir']), 'ST_NM'] = 'Jammu and Kashmir'
|
214 |
|
|
|
215 |
# df.dtypes
|
216 |
{new_line.join(map(lambda x: '# '+x, str(df_check.dtypes).split(new_line)))}
|
217 |
|
218 |
+
# {prompt.strip()}
|
219 |
# <your code here>
|
220 |
```
|
221 |
"""
|
222 |
|
223 |
+
query = f"""I have a pandas dataframe data of PM2.5 and PM10.
|
224 |
+
* The columns are 'Timestamp', 'station', 'PM2.5', 'PM10', 'address', 'city', 'latitude', 'longitude',and 'state'.
|
225 |
+
* Frequency of data is daily.
|
226 |
+
* `pollution` generally means `PM2.5`.
|
227 |
+
* You already have df, so don't read the csv file
|
228 |
+
* Don't print anything, but save result in a variable `answer` and make it global.
|
229 |
+
* Unless explicitly mentioned, don't consider the result as a plot.
|
230 |
+
* PM2.5 guidelines: India: 60, WHO: 15.
|
231 |
+
* PM10 guidelines: India: 100, WHO: 50.
|
232 |
+
* If result is a plot, show the India and WHO guidelines in the plot.
|
233 |
+
* If result is a plot make it in tight layout, save it and save path in `answer`. Example: `answer='plot.png'`
|
234 |
+
* If result is a plot, rotate x-axis tick labels by 45 degrees,
|
235 |
+
* If result is not a plot, save it as a string in `answer`. Example: `answer='The city is Mumbai'`
|
236 |
+
* I have a geopandas.geodataframe india containining the coordinates required to plot Indian Map with states.
|
237 |
+
* If the query asks you to plot on India Map, use that geodataframe to plot and then add more points as per the requirements using the similar code as follows : v = ax.scatter(df['longitude'], df['latitude']). If the colorbar is required, use the following code : plt.colorbar(v)
|
238 |
+
* If the query asks you to plot on India Map plot the India Map in Beige color
|
239 |
+
* Whenever you do any sort of aggregation, report the corresponding standard deviation, standard error and the number of data points for that aggregation.
|
240 |
+
* Whenever you're reporting a floating point number, round it to 2 decimal places.
|
241 |
+
* Always report the unit of the data. Example: `The average PM2.5 is 45.67 µg/m³`
|
242 |
+
|
243 |
+
Complete the following code.
|
244 |
+
|
245 |
+
{template}
|
246 |
+
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
247 |
"""
|
248 |
+
answer = None
|
249 |
+
code = None
|
250 |
+
error = None
|
251 |
+
try:
|
252 |
+
answer = llm.invoke(query)
|
253 |
+
code = f"""
|
254 |
+
{template.split("```python")[1].split("```")[0]}
|
255 |
+
{answer.content.split("```python")[1].split("```")[0]}
|
256 |
+
"""
|
257 |
+
# update variable `answer` when code is executed
|
258 |
+
exec(code)
|
259 |
+
ran = True
|
260 |
+
except Exception as e:
|
261 |
+
error = e
|
262 |
+
if code is not None:
|
263 |
+
answer = f"!!!Faced an error while working on your query. Please try again!!!"
|
264 |
+
|
265 |
+
if type(answer) != str:
|
266 |
answer = f"!!!Faced an error while working on your query. Please try again!!!"
|
267 |
+
|
268 |
+
response = {"role": "assistant", "content": answer, "gen_code": code, "ex_code": code, "last_prompt": prompt, "error": error}
|
|
|
|
|
|
|
|
|
269 |
|
270 |
+
# Get response from agent
|
271 |
+
# response = ask_question(model_name=model_name, question=prompt)
|
272 |
+
# response = ask_agent(agent, prompt)
|
273 |
+
|
274 |
+
if ran:
|
275 |
+
break
|
|
|
|
|
|
|
|
|
276 |
|
277 |
+
# Append agent response to chat history
|
278 |
+
st.session_state.responses.append(response)
|
279 |
+
|
280 |
+
st.session_state['last_prompt'] = prompt
|
281 |
+
st.session_state['last_model_name'] = model_name
|
282 |
+
st.rerun()
|
283 |
|
|
|
284 |
|
285 |
+
# contact details
|
286 |
+
contact_details = """
|
287 |
+
**Feel free to reach out to us:**
|
288 |
+
- [Yash J Bachwana](mailto:yash.bachwana@iitgn.ac.in) (Lead Developer)
|
289 |
+
- [Zeel B Patel](mailto:patel_zeel@iitgn.ac.in) (PhD Student)
|
290 |
+
- [Nipun Batra](mailto:nipun.batra@iitgn.ac.in) (Faculty)
|
291 |
+
"""
|
292 |
+
for _ in range(9):
|
293 |
+
st.sidebar.markdown(" ")
|
294 |
|
295 |
+
# Display contact details with message
|
296 |
+
st.sidebar.markdown("<hr>", unsafe_allow_html=True)
|
297 |
+
st.sidebar.markdown(contact_details, unsafe_allow_html=True)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
questions.txt
CHANGED
@@ -1,20 +1,20 @@
|
|
1 |
-
|
2 |
-
|
3 |
-
|
4 |
-
|
5 |
-
|
6 |
-
|
7 |
-
|
8 |
-
|
9 |
-
|
10 |
-
|
11 |
-
|
12 |
-
|
13 |
-
|
14 |
-
|
15 |
-
|
16 |
-
|
17 |
-
|
18 |
-
|
19 |
-
|
20 |
-
|
|
|
1 |
+
Plot the monthly average PM2.5 for the year 2023.
|
2 |
+
Which month in which year has the highest average PM2.5 overall?
|
3 |
+
Which month in which year has the highest PM2.5 overall?
|
4 |
+
Which month has the highest average PM2.5 in 2023 for Mumbai?
|
5 |
+
Plot and compare monthly timeseries of pollution for Mumbai and Bengaluru.
|
6 |
+
Plot the yearly average PM2.5.
|
7 |
+
Plot the monthly average PM2.5 of Delhi, Mumbai and Bengaluru for the year 2022.
|
8 |
+
Which month has the highest pollution?
|
9 |
+
Which city has the highest PM2.5 level in July 2022?
|
10 |
+
Plot and compare monthly timeseries of PM2.5 for Mumbai and Bengaluru.
|
11 |
+
Plot and compare the monthly average PM2.5 of Delhi, Mumbai and Bengaluru for the year 2022.
|
12 |
+
Plot the monthly average PM2.5.
|
13 |
+
Plot the monthly average PM10 for the year 2023.
|
14 |
+
Which (month, year) has the highest PM2.5?
|
15 |
+
Plot the monthly average PM2.5 of Delhi for the year 2022.
|
16 |
+
Plot the monthly average PM2.5 of Bengaluru for the year 2022.
|
17 |
+
Plot the monthly average PM2.5 of Mumbai for the year 2022.
|
18 |
+
Which state has the highest average PM2.5?
|
19 |
+
Plot monthly PM2.5 in Gujarat for 2023.
|
20 |
+
What is the name of the month with the highest average PM2.5 overall?
|
src.py
CHANGED
@@ -7,7 +7,7 @@ from pandasai.llm import HuggingFaceTextGen
|
|
7 |
from dotenv import load_dotenv
|
8 |
from langchain_groq.chat_models import ChatGroq
|
9 |
|
10 |
-
load_dotenv(
|
11 |
Groq_Token = os.environ["GROQ_API_KEY"]
|
12 |
models = {"mixtral": "mixtral-8x7b-32768", "llama": "llama2-70b-4096", "gemma": "gemma-7b-it"}
|
13 |
|
@@ -74,6 +74,7 @@ def show_response(st, response):
|
|
74 |
if "gen_code" in response:
|
75 |
st.markdown(decorate_with_code(response), unsafe_allow_html=True)
|
76 |
st.image(image)
|
|
|
77 |
except Exception as e:
|
78 |
if "gen_code" in response:
|
79 |
display_content = decorate_with_code(response) + f"""</details>
|
@@ -82,6 +83,7 @@ def show_response(st, response):
|
|
82 |
else:
|
83 |
display_content = response["content"]
|
84 |
st.markdown(display_content, unsafe_allow_html=True)
|
|
|
85 |
|
86 |
def ask_question(model_name, question):
|
87 |
llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0.1)
|
|
|
7 |
from dotenv import load_dotenv
|
8 |
from langchain_groq.chat_models import ChatGroq
|
9 |
|
10 |
+
load_dotenv()
|
11 |
Groq_Token = os.environ["GROQ_API_KEY"]
|
12 |
models = {"mixtral": "mixtral-8x7b-32768", "llama": "llama2-70b-4096", "gemma": "gemma-7b-it"}
|
13 |
|
|
|
74 |
if "gen_code" in response:
|
75 |
st.markdown(decorate_with_code(response), unsafe_allow_html=True)
|
76 |
st.image(image)
|
77 |
+
return {"is_image": True}
|
78 |
except Exception as e:
|
79 |
if "gen_code" in response:
|
80 |
display_content = decorate_with_code(response) + f"""</details>
|
|
|
83 |
else:
|
84 |
display_content = response["content"]
|
85 |
st.markdown(display_content, unsafe_allow_html=True)
|
86 |
+
return {"is_image": False}
|
87 |
|
88 |
def ask_question(model_name, question):
|
89 |
llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0.1)
|