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| 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 streamlit_feedback import streamlit_feedback | |
| from huggingface_hub import HfApi | |
| st.set_page_config(layout="wide") | |
| sticky_css = """ | |
| <style> | |
| .sidebar .sidebar-content { | |
| position: sticky; | |
| top: 0; | |
| } | |
| </style> | |
| """ | |
| # Inject the CSS style into the Streamlit app | |
| st.markdown(sticky_css, unsafe_allow_html=True) | |
| load_dotenv() | |
| Groq_Token = os.environ["GROQ_API_KEY"] | |
| hf_token = os.environ["HF_TOKEN"] | |
| models = {"llama3":"llama3-70b-8192","mixtral": "mixtral-8x7b-32768", "llama2": "llama2-70b-4096", "gemma": "gemma-7b-it"} | |
| shape_file = os.getenv("SHAPE_FILE") | |
| 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'] | |
| 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 | |
| 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}") | |
| 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(f"{shape_file}") | |
| 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)))} | |
| # {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'`. 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: | |
| 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 | |
| except Exception as e: | |
| error = 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, "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:[email protected]) | |
| (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) | |
| """ | |
| for _ in range(9): | |
| 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) |