import streamlit as st import os import json import pandas as pd import random from os.path import join from datetime import datetime 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 langchain_google_genai import GoogleGenerativeAI from streamlit_feedback import streamlit_feedback from huggingface_hub import HfApi st.set_page_config(layout="wide") # Load environment variables : Groq and Hugging Face API keys load_dotenv() Groq_Token = os.environ["GROQ_API_KEY"] hf_token = os.environ["HF_TOKEN"] gemini_token = os.environ["GEMINI_TOKEN"] models = {"llama3":"llama3-70b-8192","mixtral": "mixtral-8x7b-32768", "llama2": "llama2-70b-4096", "gemma": "gemma-7b-it", "gemini-pro": "gemini-pro"} self_path = os.path.dirname(os.path.abspath(__file__)) # Using HTML and CSS to center the title st.write( """ """, unsafe_allow_html=True, ) # Displaying the centered title st.markdown("
VayuBuddy makes pollution monitoring easier by bridging the gap between users and datasets.
No coding required—just meaningful insights at your fingertips!
", unsafe_allow_html=True) # Center-aligned instruction text with bold formatting st.markdown("
Choose a query from Select a prompt or type a query in the chat box, select a LLM (Large Language Model), and press enter to generate a response.
", 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" image_path = "IITGN_Logo.png" # Display images and text in three columns with specified ratios col1, col2, col3 = st.sidebar.columns((1.0, 2, 1.0)) with col2: st.image(image_path, use_column_width=True) st.markdown("

VayuBuddy

", unsafe_allow_html=True) model_name = st.sidebar.selectbox("Select LLM:", ["llama3","mixtral", "gemma", "gemini-pro"]) questions = ['Custom Prompt'] with open(join(self_path, "questions.txt")) as f: questions += f.read().split("\n") 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 = [] ### Old code for feedback # 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") def upload_feedback(): print("Uploading feedback") data = { "feedback": feedback['score'], "comment": feedback['text'], "error": error, "output": output, "prompt": last_prompt, "code": code} # generate a random file name based on current time-stamp: YYYY-MM-DD_HH-MM-SS random_folder_name = str(datetime.now()).replace(" ", "_").replace(":", "-").replace(".", "-") print("Random folder:", random_folder_name) save_path = f"/tmp/vayubuddy_feedback.md" path_in_repo = f"data/{random_folder_name}/feedback.md" with open(save_path, "w") as f: template = f"""Prompt: {last_prompt} Output: {output} Code: ```py {code} ``` Error: {error} Feedback: {feedback['score']} Comments: {feedback['text']} """ print(template, file=f) api = HfApi(token=hf_token) api.upload_file( path_or_fileobj=save_path, path_in_repo=path_in_repo, repo_id="SustainabilityLabIITGN/VayuBuddy_Feedback", repo_type="dataset", ) if status['is_image']: api.upload_file( path_or_fileobj=output, path_in_repo=f"data/{random_folder_name}/plot.png", repo_id="SustainabilityLabIITGN/VayuBuddy_Feedback", repo_type="dataset", ) print("Feedback uploaded successfully!") # Display chat responses from history on app rerun print("#"*10) for response_id, response in enumerate(st.session_state.responses): status = show_response(st, response) if response["role"] == "assistant": feedback_key = f"feedback_{int(response_id/2)}" print("response_id", response_id, "feedback_key", feedback_key) error = response["error"] output = response["content"] last_prompt = response["last_prompt"] code = response["gen_code"] if "feedback" in st.session_state.responses[response_id]: st.write("Feedback:", st.session_state.responses[response_id]["feedback"]) else: ## !!! This does on work on Safari !!! # feedback = streamlit_feedback(feedback_type="thumbs", # optional_text_label="[Optional] Please provide extra information", on_submit=upload_feedback, key=feedback_key) # Display thumbs up/down buttons for feedback thumbs = st.radio("We would appreciate your feedback!", ('👍', '👎'), index=None, key=feedback_key) if thumbs: # Text input for comments comments = st.text_area("[Optional] Please provide extra information", key=feedback_key+"_comments") feedback = {"score": thumbs, "text": comments} if st.button("Submit", on_click=upload_feedback, key=feedback_key+"_submit"): st.session_state.responses[response_id]["feedback"] = feedback st.success("Feedback uploaded successfully!") print("#"*10) show = True prompt = st.sidebar.selectbox("Select a Prompt:", questions, key="prompt_key") if prompt == 'Custom Prompt': show = False # React to user input prompt = st.chat_input("Ask me anything about air quality!", key=1000) if prompt : show = True else: # placeholder for chat input st.chat_input("Select 'Select a Prompt' -> 'Custom Prompt' in the sidebar to ask your own questions.", key=1000, disabled=True) if "last_prompt" in st.session_state: last_prompt = st.session_state["last_prompt"] last_model_name = st.session_state["last_model_name"] if (prompt == last_prompt) and (model_name == last_model_name): show = False if prompt: st.sidebar.info("Select 'Custom Prompt' to ask your own questions.") if show: # Add user input to chat history user_response = get_from_user(prompt) st.session_state.responses.append(user_response) # select random waiting line with st.spinner(random.choice(waiting_lines)): ran = False for i in range(1): print(f"Attempt {i+1}") if model_name == "gemini-pro": llm = GoogleGenerativeAI(model=models[model_name], google_api_key=os.getenv("GEMINI_TOKEN"), temperature=0) else: 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,"figure.dpi": 600} 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' import uuid # df.dtypes {new_line.join(map(lambda x: '# '+x, str(df_check.dtypes).split(new_line)))} {new_line.join(['# '+line for line in prompt.strip().split(new_line)])} """ 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'`. Use uuid to save the plot. * 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 error = None try: if model_name == "gemini-pro": answer = llm.invoke(query) else: answer = llm.invoke(query).content code = f""" {template.split("```python")[1].split("```")[0]} {answer.split("```python")[1].split("```")[0]} """ # update variable `answer` when code is executed exec(code) ran = True except Exception as e: error = e if code is not None: answer = f"Error executing the code...\n\n{e}" 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, "error": error} # Get response from agent # response = ask_question(model_name=model_name, question=prompt) # response = ask_agent(agent, prompt) if ran: break # Append agent response to chat history st.session_state.responses.append(response) st.session_state['last_prompt'] = prompt st.session_state['last_model_name'] = model_name st.rerun() # contact details contact_details = """ **Feel free to reach out to us:** - [Yash J Bachwana](mailto:yash.bachwana@iitgn.ac.in) (Lead Developer, IIT Gandhinagar) - [Zeel B Patel](https://patel-zeel.github.io/) (PhD Student, IIT Gandhinagar) - [Nipun Batra](https://nipunbatra.github.io/) (Faculty, IIT Gandhinagar) """ # Display contact details with message st.sidebar.markdown("
", unsafe_allow_html=True) st.sidebar.markdown(contact_details, unsafe_allow_html=True) st.markdown( """ """, unsafe_allow_html=True )