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app.py

@@ -1,20 +1,55 @@
 # -*- coding: utf-8 -*-
+
 # Default dependencies to run
 import os
 # from dotenv import load_dotenv
 # load_dotenv()
+import logging
+import logging
+import os
+import re
+import requests
+import math
+import time
+import folium
+import base64
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from PIL import Image
+from io import BytesIO
+from tqdm import tqdm
+from datetime import datetime
+from geopy.geocoders import Nominatim
+
+import openai
+from openai import OpenAI, AsyncOpenAI
+
+import langchain_community.embeddings.huggingface
+from langchain_community.embeddings.huggingface import HuggingFaceBgeEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.docstore.document import Document
+
+#from dotenv import load_dotenv
+#load_dotenv()
 
-# @title ⚙️ Configure Tokens
+## new async
+import asyncio
+import aiohttp
 
-#oa_token_secret = 'oa_yewey'  # @param {type: "string"}
-#lta_token_secret = 'lta_token'  # @param {type: "string"}
 
-#os.environ["OPENAI_API_KEY"] = userdata.get(oa_token_secret)
-#os.environ["DATAMALL_API_KEY"] = userdata.get(lta_token_secret)
 
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
+
+if not OPENAI_API_KEY:
+    raise Exception("No OpenAI API Key found!")
+
+client = OpenAI(api_key=OPENAI_API_KEY)
+a_client = AsyncOpenAI(api_key=OPENAI_API_KEY)
 
 import logging
-import docx
+# import docx
 import os
 import re
 import requests
@@ -48,18 +83,42 @@ import gradio as gr
 
 ########################## Initialise API keys ##############################
 
-OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
+#OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
 
-if not OPENAI_API_KEY:
-    raise Exception("No OpenAI API Key found!")
+#if not OPENAI_API_KEY:
+#    raise Exception("No OpenAI API Key found!")
 
 DATAMALL_API_KEY = os.environ.get("DATAMALL_API_KEY")
 
 if not DATAMALL_API_KEY:
     raise Exception("No Datamall API Key found!")
 
+########################## init base variables ##############################
+
+## vector stores
+
+model_name = "bge-large-en-v1.5"
+model_kwargs = {"device": "cpu"}
+encode_kwargs = {"normalize_embeddings": True}
+bge = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs = model_kwargs,
+    encode_kwargs = encode_kwargs)
+
+store_dict = {}
+
+def get_store(index_name, embeddings = bge, rerun = False):
+    if not store_dict.get(index_name, None) or rerun:
+        store_dict[index_name] = FAISS.load_local(index_name, embeddings, allow_dangerous_deserialization=True)
+    return store_dict[index_name]
+
+# synchronous
+for x in ["SCDF", "LTA", "Traffic Police"]:
+    get_store(f"index/{x}").as_retriever(search_type="similarity", search_kwargs={"k":3})
+
 ########################## Audio to Summary functions ##############################
 client = OpenAI(api_key=OPENAI_API_KEY)
+a_client = AsyncOpenAI(api_key=OPENAI_API_KEY)
 
 def get_transcript_from_audio(audio_file_path):
     """
@@ -84,9 +143,9 @@ def get_summary_from_transcript(transcript):
         raise Exception(f"Wrong type of transcript. Expected type str, got type {type(transcript)}")
 
     prompt = f"""You are provided with the following audio transcript of one or more calls between incident reporters and emergency responders in Singapore.
-    Provide a concise and detailed summary (1) based on the transcript.
+    Provide a concise and detailed summary (1) based on the transcript. Road names in the transcript may be wrong and should be edited to reflect real roads in Singapore.
     Separately provide the following information (2) with labels in [] strictly following the format {{[label]: info}} (3) based on the generated audio transcript (2) [!important: Do not include details not found in the audio transcript]:
-    [who], [what], ([where, direction]), closest single [landmark] phrase given by reporter, closest !single [road] phrase given by reporter, [when], [why], and [how] strictly based on the generated transcript. Example: {{[road]: road_name}}
+    [who], [what], ([where, direction]), closest single [landmark] phrase given by reporter, closest !single [road] phrase given by reporter, [when], [why], and [how] strictly based on the generated transcript. Example: {{[landmark]: landmark_name, \n [road]: road_name}}
     \n\n----------------\n\nTranscript:\n{transcript}\n\n(1)\n\n"""
 
     completion = client.completions.create(
@@ -129,6 +188,8 @@ def extract_location_from_summary(summary):
         landmark = re.search(landmark_pattern, summary).group(1)
 
         # Split the string by commas
+        if " and " in landmark:
+            landmark= landmark.replace(" and ", ',')
         landmark_list = landmark.split(",")
 
         # Trim whitespace from each element and append to a list
@@ -139,8 +200,10 @@ def extract_location_from_summary(summary):
     try:
         road_pattern = r'\[road\]:\s*(.*?)\n'
         road = re.search(road_pattern, summary).group(1)
-
+        print(road)
         # Split the string by commas
+        if " and " in road:
+            road= road.replace(" and ", ',')
         road_list = road.split(",")
 
         # Trim whitespace from each element and append to a list
@@ -184,6 +247,7 @@ def get_latlong_from_summary(summary):
     lat, lng = get_latlong(location_list)
     print(f"Estimated lat, lng: ({lat}, {lng})\n")
 
+
     return lat, lng
 
 def call_api(api_url):
@@ -267,6 +331,7 @@ def get_nearest_camera(latlong, num):
 
     # Extract camera location and imagelink via Datamall API
     cameraimg_df = call_api('http://datamall2.mytransport.sg/ltaodataservice/Traffic-Imagesv2?long=')
+
     cameraimg_df['CameraID'] = cameraimg_df['CameraID'].astype('int64')
 
     # Extract additional camera information from database
@@ -282,25 +347,60 @@ def get_nearest_camera(latlong, num):
 
     # Append encoded image and time retrieved into dataframe
     img_list, camera_coords, encoded_img, datetime_list = [], [], [], []
+    current_time = datetime.now().strftime('%d/%m/%Y %I:%M:%S:%f %p')
 
     for idx in closest_cam.index:
         response = requests.get(closest_cam["ImageLink"][idx])
 
-        # # Plot images of cameras
-        # print(f"{nearest_cams['Description'][idx]} ({nearest_cams['Latitude'][idx]}, {nearest_cams['Longitude'][idx]})")
-        # img = Image.open(BytesIO(response.content))
-        # plt.imshow(img)
-        # plt.show(img)
-        # print("\n")
-
         img_list.append(closest_cam["ImageLink"][idx])
         encoded_img.append(encode_image_to_base64(response))
-        datetime_list.append(datetime.now().strftime('%d/%m/%Y %I:%M %p'))
+        print('time after embed image:', datetime.now().strftime('%I:%M:%S:%f %p'))
+        datetime_list.append(current_time)
 
     closest_cam["encoded_img"] = encoded_img
     closest_cam["time_retrieved"] = datetime_list
+    return closest_cam, cameraimg_df
 
-    return closest_cam
+async def a_get_nearest_camera(latlong, num):
+    """
+    Retrieve the information of "num" nearest Traffic Cameras based on specified lat, lng
+    """
+
+    if not num:
+        return
+    lat, lng = latlong
+    cameraimg_df = call_api('http://datamall2.mytransport.sg/ltaodataservice/Traffic-Imagesv2?long=')
+    cameraimg_df['CameraID'] = cameraimg_df['CameraID'].astype('int64')
+    camerainfo_df = pd.read_csv("data/traffic_images.csv")
+
+    merged_df = pd.merge(cameraimg_df, camerainfo_df[["CameraID", "Description", "Section"]], on='CameraID', how='inner')
+    cameraimg_df = merged_df
+
+    # Calculate distances
+    cameraimg_df['Distance'] = haversine(lat,lng, cameraimg_df['Latitude'], cameraimg_df['Longitude'])
+    closest_cam = cameraimg_df.sort_values(by='Distance').head(num)
+
+    # Append encoded image and time retrieved into dataframe
+    img_list, camera_coords, encoded_img, datetime_list = [], [], [], []
+    current_time = datetime.now().strftime('%d/%m/%Y %I:%M:%S:%f %p')
+
+    async def fetch_image_base64(session, url):
+        async with session.get(url) as response:
+            res = await response.read()
+            return base64.b64encode(res).decode('utf-8')
+
+    async with aiohttp.ClientSession() as session:
+        tasks = []
+        for idx in closest_cam.index:
+            task = fetch_image_base64(session, closest_cam["ImageLink"][idx])
+            tasks.append(task)
+
+            img_list.append(closest_cam["ImageLink"][idx])
+            datetime_list.append(current_time)
+        encoded_img = await asyncio.gather(*tasks)
+    closest_cam["encoded_img"] = encoded_img
+    closest_cam["time_retrieved"] = datetime_list
+    return closest_cam, cameraimg_df
 
 def get_firestation_from_latlong(latlong, num):
     """
@@ -313,7 +413,7 @@ def get_firestation_from_latlong(latlong, num):
     lat,lng = latlong
 
     civil_df = pd.read_excel("data/fire_hosp.xlsx")
-    civil_df = civil_df[civil_df["category"].isin(["Firestation"])]
+    civil_df = civil_df[civil_df["category"].isin(["Firestation", "Firepost"])]
 
     # Calculate distances
     civil_df['Distance'] = haversine(lat,lng, civil_df['lat'], civil_df['long'])
@@ -331,7 +431,7 @@ def get_hospital_from_latlong(latlong, num):
     lat,lng = latlong
 
     civil_df = pd.read_excel("data/fire_hosp.xlsx")
-    civil_df = civil_df[civil_df["category"].isin(["Hospital", "Firestation", "Firepost"])]
+    civil_df = civil_df[civil_df["category"].isin(["Hospital"])]
 
     # Calculate distances
     civil_df['Distance'] = haversine(lat,lng, civil_df['lat'], civil_df['long'])
@@ -345,32 +445,89 @@ def get_map_from_summary(summary_txt):
     Provide a Folium Map showing the location of the incident and the "num" nearest traffic
     cameras, fire stations and ambulance sites.
     """
-
     lat, lng = get_latlong_from_summary(summary_txt)
 
     if pd.isna(lat) and pd.isna(lng):
         print("Lat, Lng cannot be determined. Please try again")
         return None
     else:
-        cameraimg_df = call_api('http://datamall2.mytransport.sg/ltaodataservice/Traffic-Imagesv2?long=')
+        # cameraimg_df = call_api('http://datamall2.mytransport.sg/ltaodataservice/Traffic-Imagesv2?long=')
+        print('nearest cam')
+        nearest_cam_df, cameraimg_df= get_nearest_camera((lat,lng), 3)
+        print('ok. nearest fire')
+        nearest_fire_df = get_firestation_from_latlong((lat,lng), 3)
+        print('ok. nearest hosp')
+        nearest_hosp_df = get_hospital_from_latlong((lat,lng), 3)
 
         avg_lat = np.mean(cameraimg_df["Latitude"])
         avg_lng = np.mean(cameraimg_df["Longitude"])
-
+        print('ok. folium map')
         map = folium.Map(location=[avg_lat, avg_lng], zoom_start=12)
-
         folium.Marker(location=[float(lat), float(lng)],
                       icon=folium.Icon(color='red'),
                       popup="Incident"
                      ).add_to(map)
+        print('ok')
+
+        for idx in tqdm(cameraimg_df.index, desc="Processing Traffic Cameras"):
+            if cameraimg_df["CameraID"][idx] in list(nearest_cam_df["CameraID"]):
+                print('added nearby camera', nearest_cam_df["Description"][idx])
+                html = '<h1>{}</h1><div>{}</div>&nbsp;<img style="width:100%; height:100%;" src="data:image/jpeg;base64,{}">'.format(
+                                                                                                                nearest_cam_df["Description"][idx],
+                                                                                                                nearest_cam_df["time_retrieved"][idx],
+                                                                                                                nearest_cam_df["encoded_img"][idx]
+                                                                                                                )
+                iframe = folium.IFrame(html, width=632+20, height=500+20)
+
+                popup = folium.Popup(iframe, max_width=2650)
+
+                folium.Marker(location=[nearest_cam_df["Latitude"][idx], nearest_cam_df["Longitude"][idx]],
+                                       icon=folium.Icon(color='blue'),
+                                       popup=popup).add_to(map)
+            else:
+                # Add marker for the camera with the specified color
+                folium.Marker(location=[cameraimg_df["Latitude"][idx], cameraimg_df["Longitude"][idx]], icon=folium.Icon(color='gray')).add_to(map)
+
+        for idx in tqdm(nearest_fire_df.index, desc="Processing Fire Stations"):
+            folium.Marker(location=[nearest_fire_df["lat"][idx], nearest_fire_df["long"][idx]],
+                              icon=folium.Icon(color='orange'),
+                             popup=nearest_fire_df["name"][idx]).add_to(map)
+
+        for idx in tqdm(nearest_hosp_df.index, desc="Processing Hospitals"):
+            folium.Marker(location=[nearest_hosp_df["lat"][idx], nearest_hosp_df["long"][idx]],
+                              icon=folium.Icon(color='green'),
+                             popup=nearest_hosp_df["name"][idx]).add_to(map)
 
-        nearest_cam_df = get_nearest_camera((lat,lng), 3)
+        return map
+
+async def a_get_map_from_summary(summary_txt, get_num_cameras=3):
+    """
+    Provide a Folium Map showing the location of the incident and the "num" nearest traffic
+    cameras, fire stations and ambulance sites.
+    """
+    lat, lng = get_latlong_from_summary(summary_txt)
+
+    if pd.isna(lat) and pd.isna(lng):
+        print("Lat, Lng cannot be determined. Please try again")
+        return None
+    else:
+        # cameraimg_df = call_api('http://datamall2.mytransport.sg/ltaodataservice/Traffic-Imagesv2?long=')
+
+        nearest_cam_df, cameraimg_df= await a_get_nearest_camera((lat,lng), get_num_cameras)
         nearest_fire_df = get_firestation_from_latlong((lat,lng), 3)
         nearest_hosp_df = get_hospital_from_latlong((lat,lng), 3)
 
-        for idx in tqdm(cameraimg_df.index, desc="Processing Traffic Cameras"):
-            if cameraimg_df["CameraID"][idx] in list(nearest_cam_df["CameraID"].astype(str)):
+        avg_lat = np.mean(cameraimg_df["Latitude"])
+        avg_lng = np.mean(cameraimg_df["Longitude"])
+        map = folium.Map(location=[avg_lat, avg_lng], zoom_start=12)
+        folium.Marker(location=[float(lat), float(lng)],
+                      icon=folium.Icon(color='red'),
+                      popup="Incident"
+                     ).add_to(map)
 
+        for idx in tqdm(cameraimg_df.index, desc="Processing Traffic Cameras"):
+            if cameraimg_df["CameraID"][idx] in list(nearest_cam_df["CameraID"]):
+                print('added nearby camera', nearest_cam_df["Description"][idx])
                 html = '<h1>{}</h1><div>{}</div>&nbsp;<img style="width:100%; height:100%;" src="data:image/jpeg;base64,{}">'.format(
                                                                                                                 nearest_cam_df["Description"][idx],
                                                                                                                 nearest_cam_df["time_retrieved"][idx],
@@ -402,58 +559,53 @@ def get_map_from_summary(summary_txt):
 ########################## RAG to Recommendations functions ##############################
 
 action_prompt = """\
+**Traffic Incident Response Assistant**
+
 You are a dispatching agent for traffic conditions. You will be provided with the Standard Operating Procedures (SOPs) of various departments, with a description of what each department's roles and responsibilities are. From these information, you are a well-versed dispatcher that can recommend the corresponding actions accurately for various scenarios.
 
-You will be provided the following information:
-1. The stakeholder and its roles and responsibilities.
-2. An incident report summary.
-3. A list of the SOPs for the stakeholder {stakeholder}.
+**Your Task**
+Your task is to analyze the provided information and generate a short and sweet summary for the stakeholder: {stakeholder}. This summary should extract key steps from the relevant SOPs, tailored to the specific incident scenario.
 
-Your task is to provide a short and sweet summary for the {stakeholder}. This should extract all the key points from the relevant SOPs, catered to the specific scenario.
+**Information Provided:**
+You will be provided the following information:
+1. **Roles and Responsibilities**: The stakeholder and its roles and responsibilities.
+2. **Incident Report Summary**: A concise description of the incident location and nature.
+3. **Standard Operating Procedures**: A list of relevant sections from various SOPs for {stakeholder}.
 
 ----------------------------------------------------------------
-
+**Roles and Responsibilities**
 Here is the description of the stakeholder, and a description of its roles and responsibilities.
 
-<stakeholder> {stakeholder} </stakeholder>
+Stakeholder: {stakeholder}
 
-<roles-and-responsibilities>
+**Roles and Responsibilities**:
 
 {stakeholder_role}
 
-</roles-and-responsibilities>
-
 ----------------------------------------------------------------
 Below is the incident summary, and location of the incident.
 
-<location> {location} </location>
+Location: {location}
 
-<incident-summary>
+**Incident Summary**:
 
 {summary}
 
-</incident-summary>
-
 ----------------------------------------------------------------
 Below is some relevant standard operating procedures.
-You will be provided with a list of SOPs, that might be relevant to the incident.. They will be split with ==============.
-The filename of the document and the document contents will be provided below.
-
-<standard-operating-procedures>
+You will be provided with a list of SOPs, that are possibly relevant to the incident. They will be split with ==============.
+The filename of the document and the contents will be provided below.
 
 {ref_content}
 
-</standard-operating-procedures>
-
 ----------------------------------------------------------------
 
 Given the situation above and the relevant SOPs, provide in detail the relevant procedure recommendations for the stakeholder {stakeholder}.
 
-** Important: If there are no relevant actions for the stakeholder found, state that "No Relevant SOPs found for current incident". Recommend to redirect to a human agent for confirmation. **
-
-Remember to keep the action plan short and sweet, while incorporating only the necessary action plans from the relevant SOP.
+**Important**
+* Remember to keep the action plan concise short and sweet. Incorporate only the necessary action plans from the relevant SOPs.
 
-Your response:
+**Your Response**:
 
 """
 
@@ -467,17 +619,16 @@ stakeholder_roles_gpt35 = {
 
 stakeholder_roles = stakeholder_roles_gpt35
 
-model_name = "bge-large-en-v1.5"
-model_kwargs = {"device": "cpu"}
-encode_kwargs = {"normalize_embeddings": True}
-bge = HuggingFaceBgeEmbeddings(
-    model_name=model_name,
-    model_kwargs = model_kwargs,
-    encode_kwargs = encode_kwargs)
+def retrieve_stakeholder_sop_from_summary(stakeholder, summary_txt, top_k = 3):
+    # print('getting sop for', stakeholder)
+    retriever = get_store(f"index/{stakeholder}").as_retriever(search_type="similarity", search_kwargs={"k":top_k})
+    selected_sops = retriever.invoke(summary_txt)
+
+    ref_content = [sop.page_content for sop in selected_sops]
+    ref_filename = [str(sop.metadata) for sop in selected_sops]
+    # print('retrieved sop for', stakeholder)
+    return (ref_content, ref_filename)
 
-def get_store(index_name, embeddings = bge):
-    store = FAISS.load_local(index_name, embeddings, allow_dangerous_deserialization=True)
-    return store
 
 def retrieve_sop_from_summary(summary_txt,
                               stakeholders = ["SCDF", "LTA", "Traffic Police"],
@@ -485,16 +636,11 @@ def retrieve_sop_from_summary(summary_txt,
                               ):
     sops_retrieved = {}
     for stakeholder in stakeholders:
-        retriever = get_store(f"index/{stakeholder}").as_retriever(search_type="similarity", search_kwargs={"k":top_k})
-        selected_sops = retriever.invoke(summary_txt)
-
-        ref_content = [sop.page_content for sop in selected_sops]
-        ref_filename = [str(sop.metadata) for sop in selected_sops]
-
-        sops_retrieved[stakeholder] = (ref_content, ref_filename)
+        sops_retrieved[stakeholder] = retrieve_stakeholder_sop_from_summary(stakeholder, summary_txt, top_k)
 
     return sops_retrieved
 
+
 def get_actions_from_summary(summary_txt, location = None,
                              stakeholders = ["SCDF", "LTA", "Traffic Police"],
                              top_k = 3):
@@ -518,25 +664,104 @@ def get_actions_from_summary(summary_txt, location = None,
             stakeholder=stakeholder,
             stakeholder_role = stakeholder_roles[stakeholder])
 
-        # print(stakeholder_action_prompt)
-
-        # completion = client.completions.create(
-        #   model="gpt-3.5-turbo-instruct",
-        #   max_tokens=1000,
-        #   prompt=stakeholder_action_prompt.format()
-        # )
         completion = client.chat.completions.create(
           model = "gpt-3.5-turbo-1106",
+          temperature=0.,
           max_tokens=1000,
           messages=[{
             "role": "system",
             "content": stakeholder_action_prompt.format()}]
             )
 
-        # print(stakeholder,"\n", completion.choices[0].text)
+        results[stakeholder] = ({
+            "stakeholder": stakeholder,
+            # "result_sop": completion.choices[0].text,
+            "actionables": completion.choices[0].message.content,
+            "ref_content": ref_content,
+            "ref_filename" : ref_filename,
+            # "images": images,
+            # "ambulance_needed": "1",
+            # "fire_truck_needed": "1",
+        })
+    return results
+
+async def a_retrieve_stakeholder_sop_from_summary(stakeholder, summary_txt, top_k = 3):
+    # print('getting sop for', stakeholder)
+    retriever = get_store(f"index/{stakeholder}").as_retriever(search_type="similarity", search_kwargs={"k":top_k})
+
+    ## async
+    selected_sops = await retriever.ainvoke(summary_txt)
+
+    ref_content = [sop.page_content for sop in selected_sops]
+    ref_filename = [str(sop.metadata) for sop in selected_sops]
+    # print('retrieved sop for', stakeholder)
+    return (ref_content, ref_filename)
+
+async def a_retrieve_sop_from_summary(summary_txt,
+                              stakeholders = ["SCDF", "LTA", "Traffic Police"],
+                              top_k = 3
+                              ):
+    sops_retrieved = {}
+    tasks = []
+    async def run_tasks():
+        for stakeholder in stakeholders:
+            tasks.append(a_retrieve_stakeholder_sop_from_summary(stakeholder, summary_txt, top_k))
+        results = await asyncio.gather(*tasks)
+        return results
 
+    results = await run_tasks()
+    for stakeholder, result in zip(stakeholders, results):
+        sops_retrieved[stakeholder] = result
+
+    return sops_retrieved
+
+async def a_get_actions_from_summary(summary_txt, location = None,
+                             stakeholders = ["SCDF", "LTA", "Traffic Police"],
+                             top_k = 3):
+    """
+    Provides a json output of the SOPs for the relevant stakeholders based on the Summary + 5W1H
+    processed from the transcript.
+    """
+
+    # sops_retrieved = await a_retrieve_sop_from_summary(summary_txt, top_k = top_k)
+
+    results = {}
+    for stakeholder in stakeholders:
+        # ref_content, ref_filename = sops_retrieved[stakeholder]
         results[stakeholder] = ({
             "stakeholder": stakeholder,
+            # "actionables": completion.choices[0].message.content,
+            # "ref_content": ref_content,
+            # "ref_filename" : ref_filename,
+        })
+
+    # for stakeholder in stakeholders:
+    async def a_get_stakeholder_actions_from_summary(stakeholder):
+        # ref_content, ref_filename = sops_retrieved[stakeholder]
+        ref_content, ref_filename = await a_retrieve_stakeholder_sop_from_summary(stakeholder, summary_txt, top_k)
+
+        stakeholder_action_prompt = action_prompt.format(
+            summary=summary_txt,
+            location=location,
+            # ref_content=ref_content,
+            # ref_filename=ref_filename,
+            ref_content = ("\n"+'='*20+"\n").join(f"{i}" for i, j in zip(ref_content,ref_filename)),
+            stakeholder=stakeholder,
+            stakeholder_role = stakeholder_roles[stakeholder])
+
+        completion = await a_client.chat.completions.create(
+          model = "gpt-3.5-turbo-1106",
+          temperature=0.,
+          max_tokens=1000,
+          messages=[{
+            "role": "system",
+            "content": stakeholder_action_prompt.format()}]
+            )
+
+        # print(stakeholder,"\n", completion.choices[0].text)
+
+        return ({
+            # "stakeholder": stakeholder,
             # "result_sop": completion.choices[0].text,
             "actionables": completion.choices[0].message.content,
             "ref_content": ref_content,
@@ -545,6 +770,18 @@ def get_actions_from_summary(summary_txt, location = None,
             # "ambulance_needed": "1",
             # "fire_truck_needed": "1",
         })
+
+    tasks = []
+    async def run_tasks():
+        for stakeholder in stakeholders:
+            tasks.append(a_get_stakeholder_actions_from_summary(stakeholder))
+        return await asyncio.gather(*tasks)
+
+    actions_results = await run_tasks()
+    for stakeholder, action_result in zip(stakeholders, actions_results):
+        results[stakeholder].update(action_result)
+
+
     return results
 
 ########################## Final Output function ##############################
@@ -565,13 +802,19 @@ def disseminate_actions(smry):
     #     stakeholder = action.get('stakeholder')
     #     ## TODO: Dissmeniate based on where the stakeholder is supposed to be
 
-########################## gradio code ##############################
+async def a_disseminate_actions(smry):
+    location = extract_location_for_prompt(smry)
+    tasks = [a_get_actions_from_summary(smry, location), a_get_map_from_summary(smry)]
+    actionables, folium_map = await asyncio.gather(*tasks)
+    return actionables, folium_map
 
-def change_accordion(x):
+########################## gradio code ##############################
+import gradio as gr
+async def change_accordion(x):
     # print("debug: accordion")
     if len(x) >0 and x!= 'Summary':
         isOpen = True
-        actionables, folium_map = disseminate_actions(x)
+        actionables, folium_map = await a_disseminate_actions(x)
         if folium_map == None:
             return (gr.Accordion("Recommendations output (NOTE: unable to determine incident location)", visible=isOpen), gr.Textbox(visible=False) , \
                     folium.Map(location=[1.2879, 103.8517], zoom_start=12), \
@@ -631,12 +874,10 @@ with gr.Blocks(js=js) as demo:
             fn=get_transcript_from_audio,
             inputs=input_audio,
             outputs=trans_textbox,
-            examples=[
-                "audio/call_711.mp3",
-                "audio/ex1.m4a",
-                "audio/ex2.m4a",
-                "audio/ex3.m4a"
-            ],
+            examples=[\
+            # ],
+                      # "audio/call_711.mp3", \
+                      "audio/ex1.m4a", "audio/ex2.m4a", "audio/ex3.m4a", "audio/ex3.m4a"], "audio/ex3.m4a"],
             cache_examples=True,
             allow_flagging = 'never'
         )
@@ -650,5 +891,5 @@ with gr.Blocks(js=js) as demo:
     # trans_textbox.change(lambda x: gr.Tab("Summarisation from Audio"), inputs=[trans_textbox], outputs=[tab2], scroll_to_output = True)
     summ_textbox.change(change_accordion, inputs=[summ_textbox], outputs=[img, outs_textbox, map_gr, recc_textbox1,recc_textbox2, recc_textbox3], scroll_to_output = True)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch(debug = True)
+

app_v2.py

@@ -1,636 +0,0 @@
-# -*- coding: utf-8 -*-
-# Default dependencies to run
-import os
-# from dotenv import load_dotenv
-# load_dotenv()
-
-import logging
-import docx
-import os
-import re
-import requests
-import math
-import time
-import base64
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-
-from PIL import Image
-from io import BytesIO
-from tqdm import tqdm
-from datetime import datetime
-from geopy.geocoders import Nominatim
-import folium
-
-import openai
-from openai import OpenAI
-
-import langchain_community.embeddings.huggingface
-from langchain_community.embeddings.huggingface import HuggingFaceBgeEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.docstore.document import Document
-
-from gradio_folium import Folium
-import gradio as gr
-
-# from dotenv import load_dotenv
-# load_dotenv()
-
-########################## Initialise API keys ##############################
-
-OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
-
-if not OPENAI_API_KEY:
-    raise Exception("No OpenAI API Key found!")
-
-DATAMALL_API_KEY = os.environ.get("DATAMALL_API_KEY")
-
-if not DATAMALL_API_KEY:
-    raise Exception("No Datamall API Key found!")
-
-########################## init base variables ##############################
-
-## vector stores
-model_name = "bge-large-en-v1.5"
-model_kwargs = {"device": "cpu"}
-encode_kwargs = {"normalize_embeddings": True}
-bge = HuggingFaceBgeEmbeddings(
-    model_name=model_name,
-    model_kwargs = model_kwargs,
-    encode_kwargs = encode_kwargs)
-
-
-store_dict = {}
-
-def get_store(index_name, embeddings = bge, rerun = False):
-    if not store_dict.get(index_name, None) or rerun:
-        store_dict[index_name] = FAISS.load_local(index_name, embeddings, allow_dangerous_deserialization=True)
-    return store_dict[index_name]
-
-for x in ["SCDF", "LTA", "Traffic Police"]:
-    get_store(f"index/{x}").as_retriever(search_type="similarity", search_kwargs={"k":3})
-
-########################## Audio to Summary functions ##############################
-client = OpenAI(api_key=OPENAI_API_KEY)
-
-def get_transcript_from_audio(audio_file_path):
-    """
-    Provides transcript from audio file.
-    """
-
-    with open(audio_file_path, "rb") as f:
-        transcript = client.audio.translations.create(
-            model="whisper-1",
-            file=f
-        )
-    return transcript.text
-
-def get_summary_from_transcript(transcript):
-    """
-    Provides summary with 5W1H from transcripted text.
-    """
-
-    if type(transcript) == openai.types.audio.translation.Translation:
-        transcript = transcript.text
-    if type(transcript) is not str:
-        raise Exception(f"Wrong type of transcript. Expected type str, got type {type(transcript)}")
-
-    prompt = f"""You are provided with the following audio transcript of one or more calls between incident reporters and emergency responders in Singapore.
-    Provide a concise and detailed summary (1) based on the transcript. Road names in the transcript may be wrong and should be edited to reflect real roads in Singapore.
-    Separately provide the following information (2) with labels in [] strictly following the format {{[label]: info}} (3) based on the generated audio transcript (2) [!important: Do not include details not found in the audio transcript]:
-    [who], [what], ([where, direction]), closest single [landmark] phrase given by reporter, closest !single [road] phrase given by reporter, [when], [why], and [how] strictly based on the generated transcript. Example: {{[landmark]: landmark_name, [road]: road_name}}
-    \n\n----------------\n\nTranscript:\n{transcript}\n\n(1)\n\n"""
-
-    completion = client.completions.create(
-        model="gpt-3.5-turbo-instruct",
-        max_tokens=1000,
-        prompt=prompt,
-        temperature=0
-    )
-    summary = completion.choices[0].text
-    return summary
-
-########################## Summary to Location retrieval (fire station, images, hospitals) functions ##############################
-
-def extract_location_for_prompt(summary):
-    """
-    Provides location for GPT prompt
-    """
-
-    try:
-        location_pattern = r'\[where, direction]:\s*(.*?)\n'
-        location = re.search(location_pattern, summary).group(1)
-
-         # Split the string by commas
-        location_list = location.split(",")
-
-        # Trim whitespace from each element and append to a list
-        location_list = [item.strip() for item in location_list]
-    except:
-        location_list = extract_location_from_summary(summary)
-
-    return location_list
-
-def extract_location_from_summary(summary):
-    """
-    Provides a list of places identified from Summary + 5W1H
-    """
-
-    try:
-        landmark_pattern = r'\[landmark\]:\s*(.*?)\n'
-        landmark = re.search(landmark_pattern, summary).group(1)
-
-        # Split the string by commas
-        landmark_list = landmark.split(",")
-
-        # Trim whitespace from each element and append to a list
-        landmark_list = [item.strip() for item in landmark_list]
-    except:
-        landmark_list = []
-
-    try:
-        road_pattern = r'\[road\]:\s*(.*?)\n'
-        road = re.search(road_pattern, summary).group(1)
-
-        # Split the string by commas
-        road_list = road.split(",")
-
-        # Trim whitespace from each element and append to a list
-        road_list = [item.strip() for item in road_list]
-    except:
-        road_list = []
-
-    return landmark_list + road_list
-
-def get_latlong(location_list):
-    """
-    Approximates the location based on a list of places.
-    """
-
-    geolocator = Nominatim(user_agent="user_agent")
-
-    lat_list, lng_list = [], []
-
-    for location in location_list:
-        try:
-            identified_location = geolocator.geocode(f"{location}, Singapore")
-            print(f"- Identified '{identified_location.address}' from '{location}'")
-
-            lat_list.append(identified_location.latitude)
-            lng_list.append(identified_location.longitude)
-        except:
-            print(f"- Unable to identify '{location}'")
-
-    return np.mean(lat_list), np.mean(lng_list)
-
-def get_latlong_from_summary(summary):
-    """
-    Gets the approximated location of the incident based on Summary + 5W1H
-    """
-
-    # Get a list of locations from the summary
-    location_list = extract_location_from_summary(summary)
-    print(f"\nLocations identified: {location_list}")
-
-    # Get approximated location of the incident
-    lat, lng = get_latlong(location_list)
-    print(f"Estimated lat, lng: ({lat}, {lng})\n")
-
-
-    return lat, lng
-
-def call_api(api_url):
-    """
-    Makes Datamall API request
-    """
-
-    # Make sure to add any necessary headers for your API request here
-    headers = {
-        'AccountKey': DATAMALL_API_KEY,
-        'accept': 'application/json'   # Example header, adjust as necessary
-    }
-
-    # Call the API
-    response = requests.get(api_url, headers=headers)
-
-    # Check if the response was successful
-    if response.status_code == 200:
-        # Parse the JSON response
-        data = response.json()
-
-        # Extracting the list of incidents from the 'value' key
-        df = pd.DataFrame(data['value'])
-    else:
-        print("Failed to retrieve data. Status code:", response.status_code)
-
-    return df
-
-# Function to calculate distance using Haversine formula
-def haversine(lat1, lon1, lat2, lon2):
-    """
-    Calculates the distance between 2 entities.
-    """
-
-    # Radius of the Earth in km
-    R = 6371.0
-
-    # Convert latitude and longitude from degrees to radians
-    lat1 = np.radians(lat1)
-    lon1 = np.radians(lon1)
-    lat2 = np.radians(lat2)
-    lon2 = np.radians(lon2)
-
-    # Calculate the change in coordinates
-    dlat = lat2 - lat1
-    dlon = lon2 - lon1
-
-    # Haversine formula
-    a = np.sin(dlat / 2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2)**2
-    c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
-
-    # Distance
-    distance = R * c
-
-    return distance
-
-def encode_image_to_base64(response):
-    """
-    Encodes HTTP request and decodes it as a UTF-8 encoded string.
-    """
-
-    encoded_string = base64.b64encode(response.content).decode('utf-8')
-    return encoded_string
-
-def decode_base64_to_image(encoded_string):
-    """
-    Decodes an encoded string into binary data.
-    """
-
-    return base64.b64decode(encoded_string)
-
-def get_nearest_camera(latlong, num, cameraimg_df):
-    """
-    Retrieve the information of "num" nearest Traffic Cameras based on specified lat, lng
-    """
-
-    if not num:
-        return
-
-    lat, lng = latlong
-
-    # Extract camera location and imagelink via Datamall API
-    # cameraimg_df = call_api('http://datamall2.mytransport.sg/ltaodataservice/Traffic-Imagesv2?long=')
-
-    cameraimg_df['CameraID'] = cameraimg_df['CameraID'].astype('int64')
-
-    # Extract additional camera information from database
-    camerainfo_df = pd.read_csv("data/traffic_images.csv")
-
-    # Update cameraimg_df
-    merged_df = pd.merge(cameraimg_df, camerainfo_df[["CameraID", "Description", "Section"]], on='CameraID', how='inner')
-    cameraimg_df = merged_df
-
-    # Calculate distances
-    cameraimg_df['Distance'] = haversine(lat,lng, cameraimg_df['Latitude'], cameraimg_df['Longitude'])
-    closest_cam = cameraimg_df.sort_values(by='Distance').head(num)
-
-    # Append encoded image and time retrieved into dataframe
-    img_list, camera_coords, encoded_img, datetime_list = [], [], [], []
-
-    for idx in closest_cam.index:
-        response = requests.get(closest_cam["ImageLink"][idx])
-        # print('time after request image:', datetime.now().strftime('%I:%M:%S:%f %p'))
-
-        # # Plot images of cameras
-        # print(f"{nearest_cams['Description'][idx]} ({nearest_cams['Latitude'][idx]}, {nearest_cams['Longitude'][idx]})")
-        # img = Image.open(BytesIO(response.content))
-        # plt.imshow(img)
-        # plt.show(img)
-        # print("\n")
-
-        img_list.append(closest_cam["ImageLink"][idx])
-        encoded_img.append(encode_image_to_base64(response))
-        # print('time after embed image:', datetime.now().strftime('%I:%M:%S:%f %p'))
-        datetime_list.append(datetime.now().strftime('%d/%m/%Y %I:%M:%S:%f %p'))
-
-    closest_cam["encoded_img"] = encoded_img
-    closest_cam["time_retrieved"] = datetime_list
-    return closest_cam
-
-def get_firestation_from_latlong(latlong, num):
-    """
-    Retrieves the "num" nearest firestation based on specified lat, lng
-    """
-
-    if not num:
-        return
-
-    lat,lng = latlong
-
-    civil_df = pd.read_excel("data/fire_hosp.xlsx")
-    civil_df = civil_df[civil_df["category"].isin(["Firestation"])]
-
-    # Calculate distances
-    civil_df['Distance'] = haversine(lat,lng, civil_df['lat'], civil_df['long'])
-    closest_fire = civil_df.sort_values(by='Distance').head(num)
-
-    return closest_fire
-
-def get_hospital_from_latlong(latlong, num):
-    """
-    Retrieves the "num" nearest firestation based on specified lat, lng
-    """
-    if not num:
-        return
-
-    lat,lng = latlong
-
-    civil_df = pd.read_excel("data/fire_hosp.xlsx")
-    civil_df = civil_df[civil_df["category"].isin(["Hospital", "Firestation", "Firepost"])]
-
-    # Calculate distances
-    civil_df['Distance'] = haversine(lat,lng, civil_df['lat'], civil_df['long'])
-    closest_hosp = civil_df.sort_values(by='Distance').head(num)
-
-    return closest_hosp
-
-########################## Location to Map generator functions ##############################
-def get_map_from_summary(summary_txt):
-    """
-    Provide a Folium Map showing the location of the incident and the "num" nearest traffic
-    cameras, fire stations and ambulance sites.
-    """
-    lat, lng = get_latlong_from_summary(summary_txt)
-
-    if pd.isna(lat) and pd.isna(lng):
-        print("Lat, Lng cannot be determined. Please try again")
-        return None
-    else:
-        cameraimg_df = call_api('http://datamall2.mytransport.sg/ltaodataservice/Traffic-Imagesv2?long=')
-
-        avg_lat = np.mean(cameraimg_df["Latitude"])
-        avg_lng = np.mean(cameraimg_df["Longitude"])
-
-        map = folium.Map(location=[avg_lat, avg_lng], zoom_start=12)
-
-        folium.Marker(location=[float(lat), float(lng)],
-                      icon=folium.Icon(color='red'),
-                      popup="Incident"
-                     ).add_to(map)
-
-        nearest_cam_df = get_nearest_camera((lat,lng), 3, cameraimg_df)
-        nearest_fire_df = get_firestation_from_latlong((lat,lng), 3)
-        nearest_hosp_df = get_hospital_from_latlong((lat,lng), 3)
-
-        for idx in tqdm(cameraimg_df.index, desc="Processing Traffic Cameras"):
-            if cameraimg_df["CameraID"][idx] in list(nearest_cam_df["CameraID"].astype(str)):
-
-                html = '<h1>{}</h1><div>{}</div>&nbsp;<img style="width:100%; height:100%;" src="data:image/jpeg;base64,{}">'.format(
-                                                                                                                nearest_cam_df["Description"][idx],
-                                                                                                                nearest_cam_df["time_retrieved"][idx],
-                                                                                                                nearest_cam_df["encoded_img"][idx]
-                                                                                                                )
-                iframe = folium.IFrame(html, width=632+20, height=500+20)
-
-                popup = folium.Popup(iframe, max_width=2650)
-
-                folium.Marker(location=[nearest_cam_df["Latitude"][idx], nearest_cam_df["Longitude"][idx]],
-                                       icon=folium.Icon(color='blue'),
-                                       popup=popup).add_to(map)
-            else:
-                # Add marker for the camera with the specified color
-                folium.Marker(location=[cameraimg_df["Latitude"][idx], cameraimg_df["Longitude"][idx]], icon=folium.Icon(color='gray')).add_to(map)
-
-        for idx in tqdm(nearest_fire_df.index, desc="Processing Fire Stations"):
-            folium.Marker(location=[nearest_fire_df["lat"][idx], nearest_fire_df["long"][idx]],
-                              icon=folium.Icon(color='orange'),
-                             popup=nearest_fire_df["name"][idx]).add_to(map)
-
-        for idx in tqdm(nearest_hosp_df.index, desc="Processing Hospitals"):
-            folium.Marker(location=[nearest_hosp_df["lat"][idx], nearest_hosp_df["long"][idx]],
-                              icon=folium.Icon(color='green'),
-                             popup=nearest_hosp_df["name"][idx]).add_to(map)
-
-        return map
-
-########################## RAG to Recommendations functions ##############################
-
-action_prompt = """\
-**Traffic Incident Response Assistant**
-
-You are a dispatching agent for traffic conditions. You will be provided with the Standard Operating Procedures (SOPs) of various departments, with a description of what each department's roles and responsibilities are. From these information, you are a well-versed dispatcher that can recommend the corresponding actions accurately for various scenarios.
-
-**Your Task**
-Your task is to analyze the provided information and generate a short and sweet summary for the stakeholder: {stakeholder}. This summary should extract key steps from the relevant SOPs, tailored to the specific incident scenario.
-
-**Information Provided:**
-You will be provided the following information:
-1. **Roles and Responsibilities**: The stakeholder and its roles and responsibilities.
-2. **Incident Report Summary**: A concise description of the incident location and nature.
-3. **Standard Operating Procedures**: A list of relevant sections from various SOPs for {stakeholder}.
-
-----------------------------------------------------------------
-**Roles and Responsibilities**
-Here is the description of the stakeholder, and a description of its roles and responsibilities.
-
-Stakeholder: {stakeholder}
-
-**Roles and Responsibilities**:
-
-{stakeholder_role}
-
-----------------------------------------------------------------
-Below is the incident summary, and location of the incident.
-
-Location: {location}
-
-**Incident Summary**:
-
-{summary}
-
-----------------------------------------------------------------
-Below is some relevant standard operating procedures.
-You will be provided with a list of SOPs, that are possibly relevant to the incident. They will be split with ==============.
-The filename of the document and the contents will be provided below.
-
-{ref_content}
-
-----------------------------------------------------------------
-
-Given the situation above and the relevant SOPs, provide in detail the relevant procedure recommendations for the stakeholder {stakeholder}.
-
-**Important**
-* Remember to keep the action plan concise short and sweet. Incorporate only the necessary action plans from the relevant SOPs.
-
-**Your Response**:
-
-"""
-
-stakeholder_roles_gpt35 = {
-    "SCDF": "The Singapore Civil Defence Force (SCDF) plays a crucial role in managing traffic incidents, including accidents, vehicle breakdowns, and road blockages. Their responsibilities include providing emergency medical services, extrication of trapped individuals, and ensuring public safety during such incidents. \n\nThe SCDF is mandated to respond to emergencies and protect lives and property. Traffic incidents often involve casualties and pose risks to public safety. SCDF's expertise in emergency medical services and rescue operations enables them to provide timely assistance, including medical care, extrication of trapped individuals, and clearing obstructions to restore traffic flow swiftly. Their swift response helps minimize casualties, alleviate traffic congestion, and ensure smooth coordination with other agencies for effective incident management.",
-
-    "LTA": "The Land Transport Authority (LTA) in Singapore is responsible for managing and regulating various aspects of the transportation system, including responding to traffic incidents. Their roles involve coordinating with other agencies, managing traffic flow, implementing road safety measures, and providing real-time information to the public during incidents. \n\nLTA is tasked with ensuring smooth and safe transportation operations. During traffic incidents, LTA's role becomes crucial in managing traffic flow, implementing diversions, and coordinating with relevant agencies to clear obstructions promptly. They leverage technology and infrastructure such as traffic lights, CCTV cameras, and electronic signages to monitor and manage traffic effectively. Additionally, LTA disseminates real-time updates to the public to facilitate informed decision-making and minimize disruptions caused by incidents.",
-
-    "Traffic Police": "The Traffic Police in Singapore are tasked with managing traffic incidents, including accidents, road obstructions, and heavy traffic. Their responsibilities involve ensuring road safety, managing traffic flow, conducting investigations, and enforcing traffic laws to prevent further incidents and maintain order on the roads. \n\nTraffic Police are essential for maintaining order and safety on Singapore's roads. When incidents occur, they must promptly respond to manage traffic, ensure the safety of motorists and pedestrians, and investigate the causes to prevent recurrence. Their enforcement of traffic laws deters reckless behavior and promotes compliance, contributing to overall road safety. Through effective coordination with other agencies, Traffic Police play a vital role in minimizing disruptions and ensuring smooth traffic flow during incidents."
-}
-
-stakeholder_roles = stakeholder_roles_gpt35
-
-def retrieve_sop_from_summary(summary_txt,
-                              stakeholders = ["SCDF", "LTA", "Traffic Police"],
-                              top_k = 3
-                              ):
-    sops_retrieved = {}
-    for stakeholder in stakeholders:
-        retriever = get_store(f"index/{stakeholder}").as_retriever(search_type="similarity", search_kwargs={"k":top_k})
-        selected_sops = retriever.invoke(summary_txt)
-
-        ref_content = [sop.page_content for sop in selected_sops]
-        ref_filename = [str(sop.metadata) for sop in selected_sops]
-
-        sops_retrieved[stakeholder] = (ref_content, ref_filename)
-
-    return sops_retrieved
-
-def get_actions_from_summary(summary_txt, location = None,
-                             stakeholders = ["SCDF", "LTA", "Traffic Police"],
-                             top_k = 3):
-    """
-    Provides a json output of the SOPs for the relevant stakeholders based on the Summary + 5W1H
-    processed from the transcript.
-    """
-
-    sops_retrieved = retrieve_sop_from_summary(summary_txt, top_k = top_k)
-
-    results = {}
-
-    for stakeholder in stakeholders:
-        ref_content, ref_filename = sops_retrieved[stakeholder]
-        stakeholder_action_prompt = action_prompt.format(
-            summary=summary_txt,
-            location=location,
-            # ref_content=ref_content,
-            # ref_filename=ref_filename,
-            ref_content = ("\n"+'='*20+"\n").join(f"{i}" for i, j in zip(ref_content,ref_filename)),
-            stakeholder=stakeholder,
-            stakeholder_role = stakeholder_roles[stakeholder])
-
-        completion = client.chat.completions.create(
-          model = "gpt-3.5-turbo-1106",
-          max_tokens=1000,
-          messages=[{
-            "role": "system",
-            "content": stakeholder_action_prompt.format()}]
-            )
-
-        results[stakeholder] = ({
-            "stakeholder": stakeholder,
-            # "result_sop": completion.choices[0].text,
-            "actionables": completion.choices[0].message.content,
-            "ref_content": ref_content,
-            "ref_filename" : ref_filename,
-            # "images": images,
-            # "ambulance_needed": "1",
-            # "fire_truck_needed": "1",
-        })
-    return results
-
-########################## Final Output function ##############################
-
-def disseminate_actions(smry):
-    """
-    Provides relevant information and recommended actions based on the Summary + 5W1H processed
-    from the transcript.
-    """
-    location = extract_location_for_prompt(smry)
-
-    actionables = get_actions_from_summary(smry, location)
-    folium_map = get_map_from_summary(smry)
-
-    return actionables, folium_map
-
-########################## gradio code ##############################
-
-def change_accordion(x):
-    # print("debug: accordion")
-    if len(x) >0 and x!= 'Summary':
-        isOpen = True
-        actionables, folium_map = disseminate_actions(x)
-        if folium_map == None:
-            return (gr.Accordion("Recommendations output (NOTE: unable to determine incident location)", visible=isOpen), gr.Textbox(visible=False) , \
-                    folium.Map(location=[1.2879, 103.8517], zoom_start=12), \
-                actionables['SCDF']["actionables"], actionables['LTA']["actionables"], actionables['Traffic Police']["actionables"])
-        # return gr.Accordion("Image Location", open=isOpen), map_out
-        return (gr.Accordion("Recommendations output and Locations on Map", visible=isOpen), gr.Textbox(visible=False) ,folium_map, \
-                actionables['SCDF']["actionables"], actionables['LTA']["actionables"], actionables['Traffic Police']["actionables"])
-    else:
-        isOpen = False
-        # return gr.Accordion("Image Location", open=isOpen), x[1]
-        return (gr.Accordion("Recommendations output and Locations on Map", visible=isOpen), gr.Textbox(visible=False), \
-                folium.Map(location=[1.2879, 103.8517], zoom_start=12), recc_textbox1,recc_textbox2, recc_textbox3)
-
-js = '''
-function refresh() {
-    const url = new URL(window.location);
-
-    if (url.searchParams.get('__theme') !== 'dark') {
-        url.searchParams.set('__theme', 'dark');
-        window.location.href = url.href;
-    }
-}
-'''
-
-trans_textbox = gr.Textbox('Transcript',lines=10,max_lines=19, autoscroll = False)
-summ_textbox = gr.Textbox('Summary',lines=10,max_lines=19, autoscroll = False)
-# recc_textbox1 = gr.Textbox(label="SCDF", max_lines=8, show_copy_button=True)
-# recc_textbox2 = gr.Textbox(label="LTA", max_lines=8, show_copy_button=True)
-# recc_textbox3 = gr.Textbox(label="Traffic Police", max_lines=8, show_copy_button=True)
-# map_gr = Folium(value=map, height=400)
-
-with gr.Blocks(js=js) as demo:
-    # summ_textbox = gr.Textbox('Summary')
-    gr.Markdown("Emergency Responder Copilot Demonstrator")
-    isOpen = False
-    outs_textbox = gr.Textbox("Recommendations outputs will appear here once summary is generated from audio transcript.",lines=1,max_lines=1, autoscroll = False, show_label= False)
-    with gr.Accordion("Recommendations output and Locations on Map", open=True, visible = isOpen) as img:
-        with gr.Row():
-            recc_textbox1 = gr.Textbox(label="SCDF", max_lines=8, show_copy_button=True, autoscroll = False)
-            recc_textbox2 = gr.Textbox(label="LTA", max_lines=8, show_copy_button=True, autoscroll = False)
-            recc_textbox3 = gr.Textbox(label="Traffic Police", max_lines=8, show_copy_button=True, autoscroll = False)
-        # gr.Markdown(loc)
-        map_gr = Folium(value=folium.Map(location=[1.2879, 103.8517], zoom_start=12), height=400)
-
-    with gr.Tab("Transcript Generator from Audio"):
-        input_audio = gr.Audio(
-            sources=["microphone"],
-            type = 'filepath',
-            waveform_options=gr.WaveformOptions(
-                waveform_color="#01C6FF",
-                waveform_progress_color="#0066B4",
-                skip_length=2,
-                show_controls=False,
-            ),
-        )
-        audio2trans = gr.Interface(
-            fn=get_transcript_from_audio,
-            inputs=input_audio,
-            outputs=trans_textbox,
-            examples=[\
-                      # "audio/call_711.mp3", \
-                      "audio/ex1.m4a", "audio/ex2.m4a", "audio/ex3.m4a"],
-            cache_examples=True,
-            allow_flagging = 'never'
-        )
-    with gr.Tab("Summarisation from Audio") as tab2:
-        trans2summary = gr.Interface(
-            fn=get_summary_from_transcript,
-            inputs=trans_textbox,
-            outputs=summ_textbox,
-            allow_flagging = 'never'
-        )
-    # trans_textbox.change(lambda x: gr.Tab("Summarisation from Audio"), inputs=[trans_textbox], outputs=[tab2], scroll_to_output = True)
-    summ_textbox.change(change_accordion, inputs=[summ_textbox], outputs=[img, outs_textbox, map_gr, recc_textbox1,recc_textbox2, recc_textbox3], scroll_to_output = True)
-
-if __name__ == "__main__":
-    demo.launch()

requirements.txt

@@ -1,3 +1,5 @@
+asyncio
+aiohttp
 gradio==4.17
 gradio-folium
 python-docx==1.1.0