app.py

# 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)
# 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)

# # Center-aligned instruction text with bold formatting
# 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)
# # 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 in which year has the highest average PM2.5 overall?',
#              'Which month in which year 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?',
#              '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, year) 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(1):
#                 print(f"Attempt {i+1}")
#                 llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0)

#                 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": 12}

#                 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"])

# import geopandas as gpd
# india = gpd.read_file("https://gist.githubusercontent.com/jbrobst/56c13bbbf9d97d187fea01ca62ea5112/raw/e388c4cae20aa53cb5090210a42ebb9b765c0a36/india_states.geojson") 
# india.loc[india['ST_NM'].isin(['Ladakh', 'Jammu & Kashmir']), 'ST_NM'] = 'Jammu and Kashmir'

# # 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.
#                 * The columns are 'Timestamp', 'station', 'PM2.5', 'PM10', 'address', 'city', 'latitude', 'longitude',and 'state'.
#                 * Frequency of data is daily.
#                 * `pollution` generally means `PM2.5`.
#                 * You already have df, so don't read the csv file 
#                 * Don't print anything, 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 a plot, rotate x-axis tick labels by 45 degrees,
#                 * If result is not a plot, save it as a string in `answer`. Example: `answer='The city is Mumbai'`
#                 * I have a geopandas.geodataframe india containining the coordinates required to plot Indian Map with states.
#                 * 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)
#                 * If the query asks you to plot on India Map plot the India Map in Beige color 
#                 * 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 = None
#                 code = None
#                 try:
#                     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
#                     exec(code)
#                     ran = True
#                     no_response = False
#                 except Exception as e:
#                     no_response = True
#                     exception = e
#                     if code is not None:
#                         answer = f"!!!Faced an error while working on your query. Please try again!!!"
                
#                 if type(answer) != str:
#                     answer = f"!!!Faced an error while working on your query. Please try again!!!"
                
#                 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  
            
#         # Display agent response
#         if code is not None:
#             # Add agent response to chat history
#             print("Adding response")
            
#             st.session_state.responses.append(response)
#             show_response(st, response)

#         if no_response:
#             print("No response")
#             st.error(f"Failed to generate right output due to the following error:\n\n{exception}")

        

#         prompt = 'Custom Prompt'





####################################################Added User Feedback###################################################

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


from datasets import Dataset, load_dataset, concatenate_datasets
import streamlit as st
from streamlit_feedback import streamlit_feedback
import uuid

from huggingface_hub import login, HfFolder
import os

# Set the token
token = os.getenv("HF_TOKEN")  # Replace "YOUR_AUTHENTICATION_TOKEN" with your actual token

# Login using the token

login(token=token)

model_name = st.sidebar.selectbox("Select LLM:", ["llama3","mixtral", "gemma"])

contact_details = """
**Feel free to reach out to us:**
- [Nipun Batra](mailto:[email protected])
- [Zeel B Patel](mailto:[email protected])
- [Yash J Bachwana](mailto:[email protected])
"""
for _ in range(12):
    st.sidebar.markdown(" ")

# Display contact details with message
st.sidebar.markdown("<hr>", unsafe_allow_html=True)
st.sidebar.markdown(contact_details, unsafe_allow_html=True)

# Function to push feedback data to Hugging Face Hub dataset
def push_to_dataset(feedback, comments,output,code,error):
    # Load existing dataset or create a new one if it doesn't exist
    try:
        ds = load_dataset("YashB1/Feedbacks_eoc", split="evaluation")
    except FileNotFoundError:
        # If dataset doesn't exist, create a new one
        ds = Dataset.from_dict({"feedback": [], "comments": [], "error": [], "output": [], "code": []})

    # Add new feedback to the dataset
    new_data = {"feedback": [feedback], "comments": [comments], "error": [error], "output": [output], "code": [code]}  # Convert feedback and comments to lists
    new_data = Dataset.from_dict(new_data)  

    ds = concatenate_datasets([ds, new_data])

    # Push the updated dataset to Hugging Face Hub
    ds.push_to_hub("YashB1/Feedbacks_eoc", split="evaluation")



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)
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)

# Center-aligned instruction text with bold formatting
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)
# 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"])


if 'question_state' not in st.session_state:
    st.session_state.question_state = False

if 'fbk' not in st.session_state:
    st.session_state.fbk = str(uuid.uuid4())

if 'feedback' not in st.session_state:
    st.session_state.feedback = None

if "chat_history" not in st.session_state:
    st.session_state.chat_history = []


def display_answer():
    for entry in st.session_state.chat_history:
        with st.chat_message("human"):
            st.write(entry["question"])
        
            # st.write(entry["answer"])
            # print(entry["answer"])
        show_response(st, entry["answer"])



def fbcb(response):
    """Update the history with feedback.
    
    The question and answer are already saved in history.
    Now we will add the feedback in that history entry.
    """
    
    display_answer()  # display hist

    # Create a new feedback by changing the key of feedback component.
    st.session_state.fbk = str(uuid.uuid4())



question = st.chat_input(placeholder="Ask your question here .... !!!!")
if question:
    # We need this because of feedback. That question above
    # is a stopper. If user hits the feedback button, streamlit
    # reruns the code from top and we cannot enter back because
    # of that chat_input.
    st.session_state.prompt = question
    st.session_state.question_state = True




# We are now free because st.session_state.question_state is True.
# But there are consequences. We will have to handle
# the double runs of create_answer() and display_answer()
# just to get the user feedback. 
if st.session_state.question_state:
    
    waiting_lines = ("Thinking...", "Just a moment...", "Let me think...", "Working on it...", "Processing...", "Hold on...", "One moment...", "On it...")
    with st.spinner(random.choice(waiting_lines)):
        ran = False
        for i in range(5):
            print(f"Attempt {i+1}")
            llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0)

            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": 12}
            # If the query asks you to make a gif/animation, don't use savefig to save it. Instead use ani.save(answer, writer='pillow'). 
            # If the query asks you to make a gif/animation, don't use colormaps .

            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"])

import geopandas as gpd
file_path = "india_states.geojson"
india = gpd.read_file("https://gist.githubusercontent.com/jbrobst/56c13bbbf9d97d187fea01ca62ea5112/raw/e388c4cae20aa53cb5090210a42ebb9b765c0a36/india_states.geojson") 
india.loc[india['ST_NM'].isin(['Ladakh', 'Jammu & Kashmir']), 'ST_NM'] = 'Jammu and Kashmir'


# df.dtypes
{new_line.join(map(lambda x: '# '+x, str(df_check.dtypes).split(new_line)))}

# {st.session_state.prompt.strip()}
# <your code here>
```
"""

            query = f"""I have a pandas dataframe data of PM2.5 and PM10.
            * The columns are 'Timestamp', 'station', 'PM2.5', 'PM10', 'address', 'city', 'latitude', 'longitude',and 'state'.
            * Frequency of data is daily.
            * `pollution` generally means `PM2.5`.
            * You already have df, so don't read the csv file 
            * Don't print anything, 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 query asks to plot calendarmap, use library calmap.
            * 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 a plot, rotate x-axis tick labels by 45 degrees,
            * If result is not a plot, save it as a string in `answer`. Example: `answer='The city is Mumbai'`
            * I have a geopandas.geodataframe india containining the coordinates required to plot Indian Map with states.
            * 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)
            * If the query asks you to plot on India Map plot the India Map in Beige color 
            * 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 = None
            code = None
            exception = None
            try:
                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
                exec(code)
                ran = True
                no_response = False
            except Exception as e:
                no_response = True
                exception = e
                if code is not None:
                    answer = f"!!!Faced an error while working on your query. Please try again!!!"
            
            if type(answer) != str:
                answer = f"!!!Faced an error while working on your query. Please try again!!!"
            
            response = {"role": "assistant", "content": answer, "gen_code": code, "ex_code": code, "last_prompt": st.session_state.prompt, "no_response": no_response,"exception": exception}
            # print(response)

            if ran:
                break  
        
    # Display agent response
    if code is not None:
        # Add agent response to chat history
        if response['content'][-4:] == ".gif" : 
            # Provide a button to show the gif, we don't want it to run forever
            st.image(response['content'], use_column_width=True)
            response['content'] = ""
            
        
        print("Adding response : ")
        

        message_id = len(st.session_state.chat_history)
        st.session_state.chat_history.append({
            "question": st.session_state.prompt,
            "answer": response,
            "message_id": message_id,
        })
        display_answer()


    if no_response:
        print("No response")
        st.error(f"Failed to generate right output due to the following error:\n\n{exception}")


    # display_answer()
    # Pressing a button in feedback reruns the code.
    st.session_state.feedback = streamlit_feedback(
        feedback_type="thumbs",
        optional_text_label="[Optional]",
        align="flex-start",
        key=st.session_state.fbk,
        on_submit=fbcb
    )
    print("FeedBack",st.session_state.feedback)
    if st.session_state.feedback : 
        push_to_dataset(st.session_state.feedback['score'],st.session_state.feedback['text'],answer,code,exception)
        st.success("Feedback submitted successfully!")