Update app.py

app.py

@@ -1,3 +1,221 @@
+# 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
@@ -7,6 +225,60 @@ from src import preprocess_and_load_df, load_agent, ask_agent, decorate_with_cod
 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 st_img_pastebutton import paste
+from io import BytesIO
+import base64
+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"}
@@ -42,83 +314,83 @@ 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"])
+# 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)
+
+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"])
         
-        # Display user input
-        show_response(st, response)
+            # st.write(entry["answer"])
+            # print(entry["answer"])
+        show_response(st, entry["answer"])
+
 
-        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)
+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())
 
-                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}
+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
 
-                template = f"""```python
+
+
+
+# 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
 
@@ -128,84 +400,113 @@ 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)))}
 
-# {prompt.strip()}
+# {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 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}
-
+            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]}
                 """
-                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:
+                # 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!!!"
-                
-                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")
+            if type(answer) != str:
+                answer = f"!!!Faced an error while working on your query. Please try again!!!"
             
-            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}")
+            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!")
+
+
+
 
-        prompt = 'Custom Prompt'