Update app.py

app.py

@@ -1,200 +1,3 @@
-# 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", "llama2", "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
-
-# prompt = st.sidebar.selectbox("Select a Prompt:", questions)
-
-# # add a note "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"
-
-#             template = f"""```python
-# import pandas as pd
-# import matplotlib.pyplot as plt
-
-# df = pd.read_csv("Data.csv")
-# df["Timestamp"] = pd.to_datetime(df["Timestamp"])
-
-# # 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 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'`
-#                 * 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
-
-
-
 import streamlit as st
 import os
 import pandas as pd