Added AQI Calculator in the chat directly and made some tine changes

app.py

@@ -26,7 +26,7 @@ st.write(
 )
 
 # Displaying the centered title
-st.markdown("<h2 class='title'>VayuBuddy</h2>", unsafe_allow_html=True)
+st.markdown("<h2 class='title'>GovBuddy</h2>", unsafe_allow_html=True)
 
 # os.environ["PANDASAI_API_KEY"] = "$2a$10$gbmqKotzJOnqa7iYOun8eO50TxMD/6Zw1pLI2JEoqncwsNx4XeBS2"
 
@@ -80,49 +80,97 @@ for response in st.session_state.responses:
 
 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 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)
 
-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
 
-    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)
 
-    # 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)
 
-            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"
 
-            new_line = "\n"
+                parameters = {"font.size": 18}
 
-            template = f"""```python
+                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"])
 
+def calculator(Pollutant, concentration):
+    Calculator_index = Pollutant
+    breakpoints_low = {{
+        "O3": [0, 50, 100, 168, 208, 748],
+        "PM2.5": [0, 30, 60, 90, 120, 250],
+        "PM10": [0, 50, 100, 250, 350, 430],
+        "CO": [0, 1000, 2000, 10000, 17000, 34000],
+        "SO2": [0, 40, 80, 380, 800, 1600],
+        "NO2": [0, 40, 80, 180, 280, 400]
+    }}
+    
+    breakpoints_high = {{
+        "O3": [50, 100, 168, 208, 748,1000],
+        "PM2.5": [30, 60, 90, 120, 250,1000],
+        "PM10": [50, 100, 250, 350, 430,1000],
+        "CO": [1000, 2000, 10000, 17000, 34000,50000],
+        "SO2": [40, 80, 380, 800, 1600,2000],
+        "NO2": [ 40, 80, 180, 280, 400,1000]
+    }}
+    # Define corresponding AQI categories
+    categories_low= [0, 50, 100, 200, 300, 400]
+    categories_high = [50, 100, 200, 300, 400,500] 
+
+    # Find the appropriate AQI category based on concentration
+
+    for i in range(len(breakpoints_high[Calculator_index])):
+        if concentration <= breakpoints_high[Calculator_index][i]:
+            BPHI = breakpoints_high[Calculator_index][i]
+            IHI = categories_high[i]
+            # Calculate AQI using India formula
+            #AQI = ((categories[i] - categories[i-1]) / (breakpoints[Calculator_index][i] - breakpoints[Calculator_index][i-1])) * (concentration - breakpoints[Calculator_index][i-1]) + categories[i-1]
+            #st.sidebar.write(f"The Air Quality Index (AQI) for {{Calculator_index}} is: {{AQI}}")
+            break
+
+    for i in range(len(breakpoints_low[Calculator_index])):
+        if concentration >= breakpoints_low[Calculator_index][i]:
+            BPLI = breakpoints_low[Calculator_index][i]
+            ILI = categories_low[i]
+            # Calculate AQI using India formula
+            #AQI = ((categories[i] - categories[i-1]) / (breakpoints[Calculator_index][i] - breakpoints[Calculator_index][i-1])) * (concentration - breakpoints[Calculator_index][i-1]) + categories[i-1]
+            #st.sidebar.write(f"The Air Quality Index (AQI) for {{Calculator_index}} is: {{AQI}}")
+            break
+
+    AQI = ((IHI - ILI) / (BPHI - BPLI)) * (round(concentration) - BPLI) + ILI 
+    return AQI
+
 # df.dtypes
 {new_line.join(map(lambda x: '# '+x, str(df_check.dtypes).split(new_line)))}
 
@@ -131,117 +179,57 @@ df["Timestamp"] = pd.to_datetime(df["Timestamp"])
 ```
 """
 
-            query = f"""I have a pandas dataframe data of PM2.5 and PM10.
-            * Frequency of data is daily. 
-            * Number of stations in a city is determined by finding the unique stations in the dataset along with their city
-            * `pollution` generally means `PM2.5`.
-            * PM2.5 guidelines: India: 60, WHO: 25.
-            * PM10 guidelines: India: 100, WHO: 50.
-            * 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.
-            * If result is a plot make it in tight layout, save it and save path in `answer`. Example: `answer='plot.png'`
-            * If result is not a plot, save it as a string in `answer`. Example: `answer='The city is Mumbai'`
-            * If result is not a plot, return a csv file containing the data and the corresponding answer, as well as the data samples used 
-            * If result is a plot, show the India and WHO guidelines in the plot.
-            * Whenever you do an aggregation, do it via mean and report the standard deviation and standard error, report the number of data points.
-            * 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`
-            * If the result is a plot, make it using tableau 20 colour scheme and big font size.
-            * Consider station and sensor synonymously. 
-            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
-
-
-
-st.sidebar.info("\nCalculator")
-Pollutant = ["O3", "PM2.5", "PM10", "CO", "SO2", "NO2"]
-Calculator_index = st.sidebar.selectbox("Select a Prompt:", Pollutant)
-
-if Calculator_index:
-    concentration = st.sidebar.number_input(f"Enter {Calculator_index} concentration (µg/m³):")
-    calculate_button = st.sidebar.button("Calculate")
-    if concentration:        
-        if calculate_button:
-            # Define breakpoints and AQI categories for the selected pollutant
-            breakpoints_low = {
-                "O3": [0, 50, 100, 168, 208, 748],
-                "PM2.5": [0, 30, 60, 90, 120, 250],
-                "PM10": [0, 50, 100, 250, 350, 430],
-                "CO": [0, 1000, 2000, 10000, 17000, 34000],
-                "SO2": [0, 40, 80, 380, 800, 1600],
-                "NO2": [0, 40, 80, 180, 280, 400]
-            }
-            
-            breakpoints_high = {
-                "O3": [50, 100, 168, 208, 748,1000],
-                "PM2.5": [30, 60, 90, 120, 250,1000],
-                "PM10": [50, 100, 250, 350, 430,1000],
-                "CO": [1000, 2000, 10000, 17000, 34000,50000],
-                "SO2": [40, 80, 380, 800, 1600,2000],
-                "NO2": [ 40, 80, 180, 280, 400,1000]
-            }
-            # Define corresponding AQI categories
-            categories_low= [0, 50, 100, 200, 300, 400]
-            categories_high = [50, 100, 200, 300, 400,500] 
-
-            # Find the appropriate AQI category based on concentration
-
-            for i in range(len(breakpoints_high[Calculator_index])):
-                if concentration <= breakpoints_high[Calculator_index][i]:
-                    BPHI = breakpoints_high[Calculator_index][i]
-                    IHI = categories_high[i]
-                    # Calculate AQI using India formula
-                    #AQI = ((categories[i] - categories[i-1]) / (breakpoints[Calculator_index][i] - breakpoints[Calculator_index][i-1])) * (concentration - breakpoints[Calculator_index][i-1]) + categories[i-1]
-                    #st.sidebar.write(f"The Air Quality Index (AQI) for {Calculator_index} is: {AQI}")
-                    break
-
-            for i in range(len(breakpoints_low[Calculator_index])):
-                if concentration >= breakpoints_low[Calculator_index][i]:
-                    BPLI = breakpoints_low[Calculator_index][i]
-                    ILI = categories_low[i]
-                    # Calculate AQI using India formula
-                    #AQI = ((categories[i] - categories[i-1]) / (breakpoints[Calculator_index][i] - breakpoints[Calculator_index][i-1])) * (concentration - breakpoints[Calculator_index][i-1]) + categories[i-1]
-                    #st.sidebar.write(f"The Air Quality Index (AQI) for {Calculator_index} is: {AQI}")
+                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 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.
+                * 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
-
-            AQI = ((IHI - ILI) / (BPHI - BPLI)) * (round(concentration) - BPLI) + ILI 
-            st.sidebar.write(f"The Air Quality Index (AQI) for {Calculator_index} is: {AQI}")
-            
+            
+        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