Upload folder using huggingface_hub

app.py

@@ -46,15 +46,15 @@ PDF_DIR = "usedpdfs"  # Replace with your directory path
 DEFAULT_PDF = "s41597-024-03770-7.pdf"  # Replace with your actual PDF filename
 
 
+
 # Ensure the PDF_DIR exists
 if not os.path.isdir(PDF_DIR):
-    raise ValueError(
-        f"The directory '{PDF_DIR}' does not exist. Please check the path."
-    )
+    raise ValueError(f"The directory '{PDF_DIR}' does not exist. Please check the path.")
+
 
 
 # Get list of PDF files in the directory
-pdf_files = [f for f in os.listdir(PDF_DIR) if f.lower().endswith(".pdf")]
+pdf_files = [f for f in os.listdir(PDF_DIR) if f.lower().endswith('.pdf')]
 
 if DEFAULT_PDF not in pdf_files:
     raise ValueError(f"Default PDF '{DEFAULT_PDF}' not found in '{PDF_DIR}'.")
@@ -63,7 +63,6 @@ if DEFAULT_PDF not in pdf_files:
 if not pdf_files:
     raise ValueError(f"No PDF files found in the directory '{PDF_DIR}'.")
 
-
 def display_pdf(selected_file):
     """
     Given the selected file name, return the full path to display in the PDF viewer.
@@ -72,6 +71,9 @@ def display_pdf(selected_file):
     return file_path
 
 
+
+
+
 def web_search(query: str) -> str:
     """
     Performs a web search using the Tavily API and returns the context string.
@@ -95,20 +97,18 @@ def web_search(query: str) -> str:
         return f"Error performing web search: {str(e)}"
 
 
+
 # Ensure the PDF_DIR exists
 if not os.path.isdir(PDF_DIR):
-    raise ValueError(
-        f"The directory '{PDF_DIR}' does not exist. Please check the path."
-    )
+    raise ValueError(f"The directory '{PDF_DIR}' does not exist. Please check the path.")
 
 # Get list of PDF files in the directory
-pdf_files = [f for f in os.listdir(PDF_DIR) if f.lower().endswith(".pdf")]
+pdf_files = [f for f in os.listdir(PDF_DIR) if f.lower().endswith('.pdf')]
 
 # Check if there are PDF files in the directory
 if not pdf_files:
     raise ValueError(f"No PDF files found in the directory '{PDF_DIR}'.")
 
-
 def display_pdf(selected_file):
     """
     Given the selected file name, return the full path to display in the PDF viewer.
@@ -117,6 +117,7 @@ def display_pdf(selected_file):
     return file_path
 
 
+
 # Function to generate a date range
 def generate_date_range(start_date, end_date, freq="D"):
     return pd.date_range(start=start_date, end=end_date, freq=freq)
@@ -188,21 +189,20 @@ synthetic_dataset["time"] = [
 ]
 
 # something whcky happened with the vector store. i don't know what the fuck happened.
-# have to create a new assistant.
+# have to create a new assistant. 
 
 # you need to have system instructions ilke this
-# You are a helpful assistant and expert at ansewring building automation questions. Always carry out a file search for the desired information. You can augment that information with your general knowledge, but alwasy carry out a file seaach with every query first to see if the relevant information is there, and then add to that afterwards.
+# You are a helpful assistant and expert at ansewring building automation questions. Always carry out a file search for the desired information. You can augment that information with your general knowledge, but alwasy carry out a file seaach with every query first to see if the relevant information is there, and then add to that afterwards. 
 
 # name : Building Energy and Efficiency Expert
 
 # And also added repitiion of the instructions in the thread / run creation.
 
-VECTOR_STORE_ID = os.environ[
-    "VECTOR_STORE_ID"
-]  # will need to be updated. what the hell happened??
+VECTOR_STORE_ID = os.environ["VECTOR_STORE_ID"] # will need to be updated. what the hell happened??
 ASSISTANT_ID = os.environ["ASSISTANT_ID"]
 
 
+
 # small medium offices are waht is relevant to this dataset.
 
 # Initialize the client
@@ -228,43 +228,37 @@ class EventHandler(AssistantEventHandler):
     def on_text_delta(self, delta, snapshot):
         text = delta.value
         self.response_queue.put(text)
-
+    
     @override
     def on_event(self, event):
-        # Retrieve events that are denoted with 'requires_action'
-        # since these will have our tool_calls
-        if event.event == "thread.run.requires_action":
-            run_id = event.data.id  # Retrieve the run ID from the event data
-            self.handle_requires_action(event.data, run_id)
-
+      # Retrieve events that are denoted with 'requires_action'
+      # since these will have our tool_calls
+      if event.event == 'thread.run.requires_action':
+        run_id = event.data.id  # Retrieve the run ID from the event data
+        self.handle_requires_action(event.data, run_id)
+ 
     def handle_requires_action(self, data, run_id):
-        tool_outputs = []
-
-        for tool in data.required_action.submit_tool_outputs.tool_calls:
-            if tool.function.name == "update_weather_forecast":
-                print(tool.function.arguments)
-                args = json.loads(tool.function.arguments)
-                loc = args["location"]
-                tool_outputs.append(
-                    {"tool_call_id": tool.id, "output": update_weather_forecast(loc)}
-                )
-            elif tool.function.name == "update_weather":
-                print(tool.function.arguments)
-                args = json.loads(tool.function.arguments)
-                loc = args["location"]
-                tool_outputs.append(
-                    {"tool_call_id": tool.id, "output": update_weather(loc)}
-                )
-            elif tool.function.name == "web_search":
-                print(tool.function.arguments)
-                args = json.loads(tool.function.arguments)
-                query = args["query"]
-                tool_outputs.append(
-                    {"tool_call_id": tool.id, "output": web_search(query)}
-                )
-
-        # Submit all tool_outputs at the same time
-        self.submit_tool_outputs(tool_outputs, run_id)
+      tool_outputs = []
+        
+      for tool in data.required_action.submit_tool_outputs.tool_calls:
+        if tool.function.name == "update_weather_forecast":
+            print(tool.function.arguments)
+            args = json.loads(tool.function.arguments)
+            loc = args["location"]
+            tool_outputs.append({"tool_call_id": tool.id, "output": update_weather_forecast(loc)})
+        elif tool.function.name == "update_weather":
+            print(tool.function.arguments)
+            args = json.loads(tool.function.arguments)
+            loc = args["location"]
+            tool_outputs.append({"tool_call_id": tool.id, "output": update_weather(loc)})
+        elif tool.function.name == "web_search":
+            print(tool.function.arguments)
+            args = json.loads(tool.function.arguments)
+            query = args["query"]
+            tool_outputs.append({"tool_call_id": tool.id, "output": web_search(query)})
+        
+      # Submit all tool_outputs at the same time
+      self.submit_tool_outputs(tool_outputs, run_id)
 
     def submit_tool_outputs(self, tool_outputs, run_id):
         # Use the submit_tool_outputs_stream helper
@@ -307,7 +301,7 @@ def chat(usr_message, history):
         with client.beta.threads.runs.stream(
             thread_id=thread_id,
             assistant_id=ASSISTANT_ID,
-            tool_choice="required",
+            tool_choice = "required",
             event_handler=EventHandler(response_queue),
         ) as stream:
             stream.until_done()
@@ -351,10 +345,8 @@ def update_weather(location):
     visibility = weather_data["visibility"]
     wind_speed = weather_data["wind"]["speed"]
     wind_deg = weather_data["wind"]["deg"]
-    sunrise = datetime.fromtimestamp(weather_data["sys"]["sunrise"]).strftime(
-        "%H:%M:%S"
-    )
-    sunset = datetime.fromtimestamp(weather_data["sys"]["sunset"]).strftime("%H:%M:%S")
+    sunrise = datetime.fromtimestamp(weather_data["sys"]["sunrise"]).strftime('%H:%M:%S')
+    sunset = datetime.fromtimestamp(weather_data["sys"]["sunset"]).strftime('%H:%M:%S')
     temp = weather_data["main"]["temp"]
     humidity = weather_data["main"]["humidity"]
     condition = weather_data["weather"][0]["description"]
@@ -371,8 +363,9 @@ def update_weather(location):
 - **Sunrise:** {sunrise}, **Sunset:** {sunset}"""
 
 
+
 def update_weather_forecast(location: str) -> str:
-    """Fetches the weather forecast for a given location and returns a formatted string
+    """ Fetches the weather forecast for a given location and returns a formatted string
     Parameters:
     - location: the search term to find weather information
     Returns:
@@ -385,7 +378,7 @@ def update_weather_forecast(location: str) -> str:
         "q": location,
         "appid": api_key,
         "units": "imperial",
-        "cnt": 40,  # Request 40 data points (5 days * 8 three-hour periods)
+        "cnt": 40  # Request 40 data points (5 days * 8 three-hour periods)
     }
     response = requests.get(base_url, params=params)
     weather_data = response.json()
@@ -394,36 +387,32 @@ def update_weather_forecast(location: str) -> str:
 
     # Organize forecast data per date
     forecast_data = {}
-    for item in weather_data["list"]:
-        dt_txt = item["dt_txt"]  # 'YYYY-MM-DD HH:MM:SS'
-        date_str = dt_txt.split(" ")[0]  # 'YYYY-MM-DD'
-        time_str = dt_txt.split(" ")[1]  # 'HH:MM:SS'
+    for item in weather_data['list']:
+        dt_txt = item['dt_txt']  # 'YYYY-MM-DD HH:MM:SS'
+        date_str = dt_txt.split(' ')[0]  # 'YYYY-MM-DD'
+        time_str = dt_txt.split(' ')[1]  # 'HH:MM:SS'
         forecast_data.setdefault(date_str, [])
-        forecast_data[date_str].append(
-            {
-                "time": time_str,
-                "temp": item["main"]["temp"],
-                "feels_like": item["main"]["feels_like"],
-                "humidity": item["main"]["humidity"],
-                "pressure": item["main"]["pressure"],
-                "wind_speed": item["wind"]["speed"],
-                "wind_deg": item["wind"]["deg"],
-                "condition": item["weather"][0]["description"],
-                "visibility": item.get(
-                    "visibility", "N/A"
-                ),  # sometimes visibility may be missing
-            }
-        )
+        forecast_data[date_str].append({
+            'time': time_str,
+            'temp': item['main']['temp'],
+            'feels_like': item['main']['feels_like'],
+            'humidity': item['main']['humidity'],
+            'pressure': item['main']['pressure'],
+            'wind_speed': item['wind']['speed'],
+            'wind_deg': item['wind']['deg'],
+            'condition': item['weather'][0]['description'],
+            'visibility': item.get('visibility', 'N/A'),  # sometimes visibility may be missing
+        })
 
     # Process data to create daily summaries
     daily_summaries = {}
     for date_str, forecasts in forecast_data.items():
-        temps = [f["temp"] for f in forecasts]
-        feels_likes = [f["feels_like"] for f in forecasts]
-        humidities = [f["humidity"] for f in forecasts]
-        pressures = [f["pressure"] for f in forecasts]
-        wind_speeds = [f["wind_speed"] for f in forecasts]
-        conditions = [f["condition"] for f in forecasts]
+        temps = [f['temp'] for f in forecasts]
+        feels_likes = [f['feels_like'] for f in forecasts]
+        humidities = [f['humidity'] for f in forecasts]
+        pressures = [f['pressure'] for f in forecasts]
+        wind_speeds = [f['wind_speed'] for f in forecasts]
+        conditions = [f['condition'] for f in forecasts]
 
         min_temp = min(temps)
         max_temp = max(temps)
@@ -438,18 +427,18 @@ def update_weather_forecast(location: str) -> str:
         most_common_condition = condition_counts.most_common(1)[0][0]
 
         daily_summaries[date_str] = {
-            "min_temp": min_temp,
-            "max_temp": max_temp,
-            "avg_temp": avg_temp,
-            "avg_feels_like": avg_feels_like,
-            "avg_humidity": avg_humidity,
-            "avg_pressure": avg_pressure,
-            "avg_wind_speed": avg_wind_speed,
-            "condition": most_common_condition,
+            'min_temp': min_temp,
+            'max_temp': max_temp,
+            'avg_temp': avg_temp,
+            'avg_feels_like': avg_feels_like,
+            'avg_humidity': avg_humidity,
+            'avg_pressure': avg_pressure,
+            'avg_wind_speed': avg_wind_speed,
+            'condition': most_common_condition,
         }
 
     # Build the formatted string
-    city_name = weather_data["city"]["name"]
+    city_name = weather_data['city']['name']
     ret_str = f"**5-Day Weather Forecast for {city_name}:**\n"
 
     for date_str in sorted(daily_summaries.keys()):
@@ -466,14 +455,14 @@ def update_weather_forecast(location: str) -> str:
     return ret_str
 
 
-llmmodel = OpenAI(api_token=os.environ["OPENAI_API_KEY"], model="gpt-4o")
+llmmodel = OpenAI(api_token=os.environ["OPENAI_API_KEY"], model='gpt-4o')
 
 # Load dataframes
 dfcleaned = pd.read_csv("dfcleaned.csv")
-dfcleaned["Timestamp"] = pd.to_datetime(dfcleaned["Timestamp"])
-dfcleaned["off-nominal"] = dfcleaned["off-nominal"].apply(str)
+dfcleaned['Timestamp'] = pd.to_datetime(dfcleaned['Timestamp'])
+dfcleaned['off-nominal'] = dfcleaned['off-nominal'].apply(str)
 dfshaps = pd.read_csv("shaps.csv")
-dfshaps["Timestamp"] = pd.to_datetime(dfshaps["Timestamp"])
+dfshaps['Timestamp'] = pd.to_datetime(dfshaps['Timestamp'])
 
 # Initialize Agent
 agent = Agent([dfcleaned, dfshaps], config={"llm": llmmodel})
@@ -482,10 +471,11 @@ sdfshaps = SmartDataframe(dfshaps, config={"llm": llmmodel})
 sdfcleaned = SmartDataframe(dfcleaned, config={"llm": llmmodel})
 
 
+
 def process_query(query):
     response = agent.chat(query)  # Replace with your actual agent chat implementation
     print(response)
-
+    
     # Initialize outputs and visibility flags
     text_output = None
     image_output = None
@@ -493,27 +483,28 @@ def process_query(query):
     text_visible = False
     image_visible = False
     dataframe_visible = False
-
+    
     if isinstance(response, str) and ".png" not in response:
         text_output = response
         text_visible = True
     elif isinstance(response, str) and ".png" in response:
-        image_output = response  # Assuming response is a filepath or URL to the image
-        image_visible = True
+            image_output = response  # Assuming response is a filepath or URL to the image
+            image_visible = True
     elif isinstance(response, pd.DataFrame):
         dataframe_output = response
         dataframe_visible = True
-
+    
     return (
         text_output,
         image_output,
         dataframe_output,
         gr.update(visible=text_visible),
         gr.update(visible=image_visible),
-        gr.update(visible=dataframe_visible),
+        gr.update(visible=dataframe_visible)
     )
 
 
+
 def gradio_app():
     iface = gr.Interface(
         fn=process_query,
@@ -521,33 +512,24 @@ def gradio_app():
         outputs=[
             gr.Textbox(label="Response"),
             gr.Image(label="Plot"),
-            gr.DataFrame(label="Dataframe"),
+            gr.DataFrame(label="Dataframe")
         ],
         title="pandasai Query Processor",
-        description="Enter your query related to the csv data files.",
+        description="Enter your query related to the csv data files."
     )
     return iface
 
-
 with gr.Blocks(
-    #   theme=gr.themes.Monochrome(primary_hue="green"),
-    theme=gr.themes.Soft(),
+ #   theme=gr.themes.Monochrome(primary_hue="green"), 
+    theme = gr.themes.Soft(),
 ) as demo:
     with gr.Row():  # Combine the two weather functions into a single row
         with gr.Column():
-            location1 = gr.Textbox(
-                label="Enter location for weather (e.g., Rio Rancho, New Mexico)",
-                value="Cambridge, Massachusetts",
-            )
+            location1 = gr.Textbox(label="Enter location for weather (e.g., Rio Rancho, New Mexico)",
+                                  value="Cambridge, Massachusetts")
             weather_button = gr.Button("Get Weather")
-            #   output1 = gr.Markdown(label="Weather Information")
-            output1 = gr.Textbox(
-                label="Weather Information",
-                lines=8,
-                max_lines=8,
-                show_label=True,
-                show_copy_button=True,
-            )
+         #   output1 = gr.Markdown(label="Weather Information")
+            output1 = gr.Textbox(label="Weather Information", lines=8, max_lines=8, show_label=True, show_copy_button=True)
             weather_button.click(
                 fn=update_weather,
                 inputs=location1,
@@ -555,19 +537,12 @@ with gr.Blocks(
                 api_name="update_weather",
             )
         with gr.Column():
-            location2 = gr.Textbox(
-                label="Enter location for weather forecast (e.g., Rio Rancho, New Mexico)",
-                value="Cambridge, Massachusetts",
-            )
+            location2 = gr.Textbox(label="Enter location for weather forecast (e.g., Rio Rancho, New Mexico)",
+                                  value="Cambridge, Massachusetts")
             weather_forecast_button = gr.Button("Get 5-Day Weather Forecast")
-            #  output2 = gr.Markdown(label="Weather Forecast Information")
-            output2 = gr.Textbox(
-                label="Weather 5-Day Forecast Information",
-                lines=8,
-                max_lines=8,
-                show_label=True,
-                show_copy_button=True,
-            )
+          #  output2 = gr.Markdown(label="Weather Forecast Information")
+            output2 = gr.Textbox(label="Weather 5-Day Forecast Information", lines=8, max_lines=8,
+                                show_label=True, show_copy_button=True)
             weather_forecast_button.click(
                 fn=update_weather_forecast,
                 inputs=location2,
@@ -576,7 +551,7 @@ with gr.Blocks(
             )
     gr.Markdown("# 📄 PDF Viewer Section")
     gr.Markdown("Select a PDF from the dropdown below to view it.")
-
+    
     with gr.Accordion("Open PDF Selection", open=False):
         with gr.Row():
             # Assign a larger scale to the dropdown
@@ -584,20 +559,22 @@ with gr.Blocks(
                 choices=pdf_files,
                 label="Select a PDF",
                 value=DEFAULT_PDF,  # Set a default value
-                scale=1,  # This component takes twice the space
+                scale=1  # This component takes twice the space
             )
             # Assign a smaller scale to the PDF viewer
             pdf_viewer = PDF(
                 label="PDF Viewer",
                 interactive=True,
-                scale=3,
-                value=display_pdf(
-                    DEFAULT_PDF
-                ),  # This component takes half the space compared to dropdown
+                scale=3 ,
+                value=display_pdf(DEFAULT_PDF)# This component takes half the space compared to dropdown
             )
-
+    
         # Set up the event: when dropdown changes, update the PDF viewer
-        dropdown.change(fn=display_pdf, inputs=dropdown, outputs=pdf_viewer)
+        dropdown.change(
+            fn=display_pdf,
+            inputs=dropdown,
+            outputs=pdf_viewer
+        )
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown("# Building Automation Assistant")
@@ -611,17 +588,13 @@ with gr.Blocks(
             # Update the ChatInterface to handle streaming
             chat_interface = gr.ChatInterface(
                 chat,
-                # show_label=True,
-                #  show_copy_button=True,
-                chatbot=gr.Chatbot(
-                    height=750,
-                    show_copy_button=True,
-                    show_copy_all_button=True,
-                    avatar_images=("user_avatar.png", "assistant_avatar.png"),
-                ),
+                #show_label=True,
+              #  show_copy_button=True,
+                chatbot=gr.Chatbot(height=750, show_copy_button=True, show_copy_all_button=True,
+                                    avatar_images=("user_avatar.png", "assistant_avatar.png")),
                 title="Ask Me Anything",
-                examples_per_page=5,
-                #   theme="soft", # glass
+                examples_per_page= 5,
+             #   theme="soft", # glass
                 description="Type your question about building automation here.",
                 examples=[
                     "Give the weather forecast for Cambridge, MA",
@@ -634,20 +607,20 @@ with gr.Blocks(
                     "What are the most common maintenance challenges faced by net-zero energy buildings?",
                     "How does the Uponor Climate Control Network System contribute to building maintenance?",
                     "What role do smart systems play in the maintenance of sustainable buildings like HouseZero?",
-                    "Can you provide data on the energy performance of HouseZero over the past year?",
-                    "Tell me about the HouseZero dataset. Retrieve information from the publication you have access to. Use your file retrieval tool.",
+                    "Can you provide data on the energy performance of HouseZero over the past year?",                    
+                     "Tell me about the HouseZero dataset. Retrieve information from the publication you have access to. Use your file retrieval tool.",
                     "Describe in detail the relationshp between the columns and values in the uploaded CSV files and the information you have access to regarding the HouseZero dataset. Be verbose. Use your file retrieval tool.",
                     "Please comment on the zone relative humidity features, specifically if they indicate a problem withthe building",
                     "Give me in great detail any advice you have to maintain a small to midsize office building, like the HouseZero data corresponds to. Be verbose. Use your file retrieval tool.",
                     "Is there any information in the datafiles that indicates a problem with the building?",
                     "Show Massachusetts electricity billing rates during the same time span as the CSV data",
                     "Use those rates and the relevant columns in the CSV files to estimate how much it costs to operate this building per month.",
-                    "What is the estimated average electricity cost for operating the building using massachusetts energy rates. use your file retrieval tool. use data csv files for building data. Limit your response to 140 characters. Use your file retrieval tool.",
+                     "What is the estimated average electricity cost for operating the building using massachusetts energy rates. use your file retrieval tool. use data csv files for building data. Limit your response to 140 characters. Use your file retrieval tool.",
                     "Based on the data in these CSV files, can you assign an EnergyIQ score from 1-10 that reflects how well the building is operating? Explain the reason for your score and provide any recommendations on actions to take that can improve it in the future. Be verbose. Use your file retrieval tool.",
                     "Please summarize information concerning sensor networks that may be leading to faulty meaurements.",
                     "Tell me how to properly install the PVC sky lights.",
                     "Based on data and insights, what specific changes should be made to HouseZero's maintenance protocols?",
-                    "what recommendations do you have to mitigate against high relative humidity zone measurements in structures like the housezero building?",
+                    "what recommendations do you have to mitigate against high relative humidity zone measurements in structures like the housezero building?"
                 ],
                 fill_height=True,
             )
@@ -658,13 +631,13 @@ with gr.Blocks(
             #    with gr.Column():
             # Define the three ScatterPlot components
             anomaly_plot = gr.ScatterPlot(
-                dfcleaned,
-                x="Timestamp",
-                y="Z5_RH",
+                dfcleaned, 
+                x="Timestamp", 
+                y="Z5_RH", 
                 color="off-nominal",
-                title="Anomaly Score",
+                title="Anomaly Score"
             )
-
+        
             zone3_plot = gr.ScatterPlot(
                 dfcleaned,
                 x="Timestamp",
@@ -680,8 +653,8 @@ with gr.Blocks(
                 color="off-nominal",
                 title="Zone 4 Relative Humidity",
             )
-
-            # Group all plots into a list for easy management
+    
+    # Group all plots into a list for easy management
             plots = [anomaly_plot, zone3_plot, zone4_plot]
 
             def select_region(selection: gr.SelectData):
@@ -696,9 +669,9 @@ with gr.Blocks(
                 """
                 if selection is None or selection.index is None:
                     return [gr.Plot.update() for _ in plots]
-
+        
                 min_x, max_x = selection.index
-                # Update the x_lim for each plot
+        # Update the x_lim for each plot
                 return [gr.ScatterPlot(x_lim=(min_x, max_x)) for _ in plots]
 
             def reset_region():
@@ -710,35 +683,37 @@ with gr.Blocks(
                 """
                 return [gr.ScatterPlot(x_lim=None) for _ in plots]
 
-            # Attach event listeners to each plot
+    # Attach event listeners to each plot
             for plot in plots:
                 plot.select(
-                    select_region, inputs=None, outputs=plots  # Update all plots
+                    select_region, 
+                    inputs=None, 
+                    outputs=plots  # Update all plots
                 )
                 plot.double_click(
-                    reset_region, inputs=None, outputs=plots  # Reset all plots
+                    reset_region, 
+                    inputs=None, 
+                    outputs=plots  # Reset all plots
                 )
 
-        # plots = [plt, first_plot, second_plot]
-
-        # def select_region(selection: gr.SelectData):
-        #     min_w, max_w = selection.index
-        #     return gr.ScatterPlot(x_lim=(min_w, max_w))
+           # plots = [plt, first_plot, second_plot]
 
-        # for p in plots:
-        #     p.select(select_region, None, plots)
-        #     p.double_click(lambda: [gr.LinePlot(x_lim=None)] * len(plots), None, plots)
+           # def select_region(selection: gr.SelectData):
+           #     min_w, max_w = selection.index
+           #     return gr.ScatterPlot(x_lim=(min_w, max_w)) 
 
-    # second_plot.select(select_second_region, None, plt)
-    # second_plot.double_click(lambda: gr.ScatterPlot(x_lim=None), None, plt)
-    #  gr.Column([anomaly_plot, first_plot, second_plot])
+           # for p in plots:
+           #     p.select(select_region, None, plots)
+           #     p.double_click(lambda: [gr.LinePlot(x_lim=None)] * len(plots), None, plots)
+        
+       # second_plot.select(select_second_region, None, plt)
+       # second_plot.double_click(lambda: gr.ScatterPlot(x_lim=None), None, plt)
+      #  gr.Column([anomaly_plot, first_plot, second_plot])
 
-    # anomaly_info = gr.Markdown("Anomaly detected around October 15, 2023")
+       # anomaly_info = gr.Markdown("Anomaly detected around October 15, 2023")
     with gr.Column():
-        query = gr.Textbox(
-            label="Enter your question about the data",
-            value="Plot the anomaly_score as a function of time and highlight the highest 20 values",
-        )
+        query = gr.Textbox(label="Enter your question about the data",
+                           value="Plot the anomaly_score as a function of time and highlight the highest 20 values")
         query_button = gr.Button("Submit Data Query")
         with gr.Row():
             with gr.Column(visible=False) as output_col1:
@@ -751,21 +726,22 @@ with gr.Blocks(
             fn=process_query,
             inputs=query,
             outputs=[
-                out1,  # Text output
-                out2,  # Image output
-                out3,  # DataFrame output
-                output_col1,  # Visibility for Text output
-                output_col2,  # Visibility for Image output
-                output_col3,  # Visibility for DataFrame output
+                out1,        # Text output
+                out2,        # Image output
+                out3,        # DataFrame output
+                output_col1, # Visibility for Text output
+                output_col2, # Visibility for Image output
+                output_col3  # Visibility for DataFrame output
             ],
-            api_name="process_query",
+            api_name="process_query"
         )
-
+       
     # hide visibility until its ready
-
+    
+        
     # Weather input
-#  with gr.Row():
-#      iface = gradio_app()
+  #  with gr.Row():
+  #      iface = gradio_app()
 
 
-demo.launch(share=True)
+demo.launch(share=False)