Updated with feedback option

.gitignore

@@ -0,0 +1,2 @@
+*.pyc
+*.png

Groq.txt

@@ -1 +0,0 @@
-GROQ_API_KEY = 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

app.py

@@ -1,284 +1,19 @@
-# 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 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 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
+from huggingface_hub import HfApi
 
-# Set the token
-token = os.getenv("HF_TOKEN")  # Replace "YOUR_AUTHENTICATION_TOKEN" with your actual token
-shape_file = os.getenv("SHAPE_FILE")
-
-# 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:nipun.batra@iitgn.ac.in)
-- [Zeel B Patel](mailto:patel_zeel@iitgn.ac.in)
-- [Yash J Bachwana](mailto:yash.bachwana@iitgn.ac.in)
-"""
-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)
-
-# 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")
+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"}
 
 self_path = os.path.dirname(os.path.abspath(__file__))
@@ -312,83 +47,159 @@ st.markdown("<div style='text-align:center;'>Choose a query from <b>Select a pro
 # 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 = []
-
+model_name = st.sidebar.selectbox("Select LLM:", ["llama3","mixtral", "gemma"])
 
-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"])
+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)
 
-def fbcb(response):
-    """Update the history with feedback.
+# Initialize chat history
+if "responses" not in st.session_state:
+    st.session_state.responses = []
     
-    The question and answer are already saved in history.
-    Now we will add the feedback in that history entry.
-    """
+### 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}
     
-    display_answer()  # display hist
-
-    # Create a new feedback by changing the key of feedback component.
-    st.session_state.fbk = str(uuid.uuid4())
+    # 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: 
 
-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
-
+```py
+{code}
+```
 
+Error: {error}
 
+Feedback: {feedback['score']}
 
-# 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:
+Comments: {feedback['text']}
+        """
+        
+        print(template, file=f)
     
-    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
+    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}
+
+                template = f"""```python
 import pandas as pd
 import matplotlib.pyplot as plt
 
@@ -398,113 +209,89 @@ 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(f"{shape_file}") 
+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()}
+# {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]}
+                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}
+
                 """
-                # 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 = 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!!!"
-            
-            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)
+                
+                response = {"role": "assistant", "content": answer, "gen_code": code, "ex_code": code, "last_prompt": prompt, "error": error}
 
-            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'] = ""
+                # 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()
         
-        print("Adding response : ")
         
+# contact details
+contact_details = """
+**Feel free to reach out to us:**
+- [Yash J Bachwana](mailto:yash.bachwana@iitgn.ac.in) (Lead Developer)
+- [Zeel B Patel](mailto:patel_zeel@iitgn.ac.in) (PhD Student)
+- [Nipun Batra](mailto:nipun.batra@iitgn.ac.in) (Faculty)
+"""
+for _ in range(9):
+    st.sidebar.markdown(" ")
 
-        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!")
-
-
-
-
+# Display contact details with message
+st.sidebar.markdown("<hr>", unsafe_allow_html=True)
+st.sidebar.markdown(contact_details, unsafe_allow_html=True)

questions.txt

@@ -1,20 +1,20 @@
-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?
+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?

src.py

@@ -7,7 +7,7 @@ from pandasai.llm import HuggingFaceTextGen
 from dotenv import load_dotenv
 from langchain_groq.chat_models import ChatGroq
 
-load_dotenv("Groq.txt")
+load_dotenv()
 Groq_Token = os.environ["GROQ_API_KEY"]
 models = {"mixtral": "mixtral-8x7b-32768", "llama": "llama2-70b-4096", "gemma": "gemma-7b-it"}
 
@@ -74,6 +74,7 @@ def show_response(st, response):
             if "gen_code" in response:
                 st.markdown(decorate_with_code(response), unsafe_allow_html=True)
             st.image(image)
+            return {"is_image": True}
         except Exception as e:
             if "gen_code" in response:
                 display_content = decorate_with_code(response) + f"""</details>
@@ -82,6 +83,7 @@ def show_response(st, response):
             else:
                 display_content = response["content"]
             st.markdown(display_content, unsafe_allow_html=True)
+            return {"is_image": False}
 
 def ask_question(model_name, question):
     llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0.1)