Update app.py

app.py

@@ -1,416 +1,413 @@
-from ultralytics import YOLO
-import base64
-import cv2
-import io
-import numpy as np
-from ultralytics.utils.plotting import Annotator
-import streamlit as st
-from streamlit_image_coordinates import streamlit_image_coordinates
-import pandas as pd
-import ollama
-import bs4
-import tempfile
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.document_loaders import WebBaseLoader
-from langchain_community.document_loaders import CSVLoader
-from langchain_community.vectorstores import Chroma
-from langchain_community.embeddings import OllamaEmbeddings
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-
-def set_background(image_file1,image_file2):
-    
-    with open(image_file1, "rb") as f:
-        img_data1 = f.read()
-    b64_encoded1 = base64.b64encode(img_data1).decode()
-    with open(image_file2, "rb") as f:
-        img_data2 = f.read()
-    b64_encoded2 = base64.b64encode(img_data2).decode()
-    style = f"""
-        <style>
-        .stApp{{
-            background-image: url(data:image/png;base64,{b64_encoded1});
-            background-size: cover;
-            
-        }}
-        .st-emotion-cache-6qob1r{{
-            background-image: url(data:image/png;base64,{b64_encoded2});
-            background-size: cover;
-            border: 5px solid rgb(14, 17, 23);
-            
-        }}
-        </style>
-    """
-    st.markdown(style, unsafe_allow_html=True)
-
-set_background('pngtree-city-map-navigation-interface-picture-image_1833642.png','2024-05-18_14-57-09_5235.png')
-
-st.title("Traffic Flow and Optimization Toolkit")
-
-sb = st.sidebar # defining the sidebar
-
-sb.markdown("🛰️ **Navigation**")
-page_names = ["PS1", "PS2", "PS3","Chat with Results"]
-page = sb.radio("", page_names, index=0)
-st.session_state['n'] = sb.slider("Number of ROIs",1,5)
-
-if page == 'PS1':
-    uploaded_file = st.file_uploader("Choose a video...", type=["mp4", "mpeg"])
-    model = YOLO('yolov8n.pt')
-    if uploaded_file is not None:
-        with tempfile.NamedTemporaryFile(delete=False) as temp:
-            temp.write(uploaded_file.read())
-            if 'roi_list1' not in st.session_state:
-                st.session_state['roi_list1'] = []
-            if "all_rois1" not in st.session_state:    
-                st.session_state['all_rois1'] = []
-            classes = model.names
-
-            done_1 = st.button('Selection Done')
-
-            while len(st.session_state["all_rois1"]) < st.session_state['n']:
-                cap = cv2.VideoCapture(temp.name)
-                while not done_1:
-                    ret,frame=cap.read()
-                    cv2.putText(frame,'SELECT ROI',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
-                    if not ret:
-                        st.write('ROI selection unsuccessfull')
-                        break
-                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                    value = streamlit_image_coordinates(frame,key='numpy',width=750)
-                    st.session_state["roi_list1"].append([int(value['x']*2.55),int(value['y']*2.55)])
-                    st.write(st.session_state["roi_list1"])
-                    if cv2.waitKey(0)&0xFF==27:
-                        break
-                cap.release()
-                st.session_state["all_rois1"].append(st.session_state["roi_list1"])
-                st.session_state["roi_list1"] = []
-                done_1 = False
-
-            st.write('ROI indices: ',st.session_state["all_rois1"][0])
-
-
-
-            cap = cv2.VideoCapture(temp.name)
-            st.write("Detection started")
-            st.session_state['fps'] = cap.get(cv2.CAP_PROP_FPS)
-            st.write(f"FPS OF VIDEO: {st.session_state['fps']}")
-            avg_list = []
-            count = 0
-            frame_placeholder = st.empty()
-            st.session_state["data1"] = {}
-            for i in range(len(st.session_state["all_rois1"])):
-                st.session_state["data1"][f"ROI{i}"] = []
-            while cap.isOpened():
-                ret,frame=cap.read()
-                if not ret:
-                    break
-                count += 1
-                if count % 3 != 0:
-                    continue
-                k = 0
-                for roi_list_here1 in st.session_state["all_rois1"]:
-                    max = [0,0]
-                    min = [10000,10000]
-                    roi_list_here = roi_list_here1[1:]
-                    for i in range(len(roi_list_here)):
-                        if roi_list_here[i][0] > max[0]:
-                            max[0] = roi_list_here[i][0]
-                        if roi_list_here[i][1] > max[1]:
-                            max[1] = roi_list_here[i][1]
-                        if roi_list_here[i][0] < min[0]:
-                            min[0] = roi_list_here[i][0]
-                        if roi_list_here[i][1] < min[1]:
-                            min[1] = roi_list_here[i][1]
-                    frame_cropped = frame[min[1]:max[1],min[0]:max[0]]
-                    roi_corners = np.array([roi_list_here],dtype=np.int32)
-                    mask = np.zeros(frame.shape,dtype=np.uint8)
-                    mask.fill(255)
-                    channel_count = frame.shape[2]
-                    ignore_mask_color = (255,)*channel_count
-                    cv2.fillPoly(mask,roi_corners,0)
-                    mask_cropped = mask[min[1]:max[1],min[0]:max[0]]
-                    roi = cv2.bitwise_or(frame_cropped,mask_cropped)
-
-                    #roi = frame[roi_list_here[0][1]:roi_list_here[1][1],roi_list_here[0][0]:roi_list_here[1][0]]
-                    number = []
-                    results = model.predict(roi)
-                    for r in results:
-                        boxes = r.boxes
-                        counter = 0
-                        for box in boxes:
-                            counter += 1
-                            name = classes[box.cls.numpy()[0]]
-                            conf = str(round(box.conf.numpy()[0],2))
-                            text = name+""+conf
-                            bbox = box.xyxy[0].numpy()
-                            cv2.rectangle(frame,(int(bbox[0])+min[0],int(bbox[1])+min[1]),(int(bbox[2])+min[0],int(bbox[3])+min[1]),(0,255,0),2)
-                            cv2.putText(frame,text,(int(bbox[0])+min[0],int(bbox[1])+min[1]-5),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),2)
-                        number.append(counter)
-                    avg = sum(number)/len(number)
-                    stats = str(round(avg,2))
-                    if count%10 == 0:
-                        st.session_state["data1"][f"ROI{k}"].append(avg)
-                        k+=1
-                    cv2.putText(frame,stats,(min[0],min[1]),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,0),4)
-                    cv2.polylines(frame,roi_corners,True,(255,0,0),2)
-                cv2.putText(frame,'The average number of vehicles in the Regions of Interest',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
-                frame_placeholder.image(frame,channels='BGR')
-                st.write(st.session_state.data1)
-            cap.release()
-    else:
-        st.error('PLEASE UPLOAD AN IMAGE OF THE FORMAT JPG,JPEG OR PNG', icon="🚨")
-
-elif page == "PS3":
-    uploaded_file1 = st.file_uploader("Choose a video...", type=["mp4", "mpeg"])
-    if uploaded_file1 is not None:
-        g = io.BytesIO(uploaded_file1.read()) 
-        temporary_location = "temp_PS2.mp4"
-
-        with open(temporary_location, 'wb') as out:  
-            out.write(g.read())  
-        out.close()
-        model1 = YOLO("yolov8n.pt")
-        model2 = YOLO("best.pt")
-        if 'roi_list2' not in st.session_state:
-            st.session_state['roi_list2'] = []
-        if "all_rois2" not in st.session_state:    
-            st.session_state['all_rois2'] = []
-        classes = model1.names
-
-        done_2 = st.button('Selection Done')
-
-        while len(st.session_state["all_rois2"]) < st.session_state['n']:
-            cap = cv2.VideoCapture('temp_PS2.mp4')
-            while not done_2:
-                ret,frame=cap.read()
-                cv2.putText(frame,'SELECT ROI',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
-                if not ret:
-                    st.write('ROI selection has concluded')
-                    break
-                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                value = streamlit_image_coordinates(frame,key='numpy',width=750)
-                st.session_state["roi_list2"].append([int(value['x']*2.5),int(value['y']*2.5)])
-                st.write(st.session_state["roi_list2"])
-                if cv2.waitKey(0)&0xFF==27:
-                    break
-            cap.release()
-            st.session_state["all_rois2"].append(st.session_state["roi_list2"])
-            st.session_state["roi_list2"] = []
-            done_2 = False
-
-        st.write('ROI indices: ',st.session_state["all_rois2"][0])
-
-
-
-        cap = cv2.VideoCapture('temp_PS2.MP4')
-        st.write("Detection started")
-        avg_list = []
-        count = 0
-        frame_placeholder = st.empty()
-        st.session_state.data = {}
-        for i in range(len(st.session_state["all_rois2"])):
-            st.session_state["data"][f"ROI{i}"] = []
-        for i in range(len(st.session_state['all_rois2'])):
-            st.session_state.data[f"ROI{i}"] = []
-        while cap.isOpened():
-            ret,frame=cap.read()
-            if not ret:
-                break
-            count += 1
-            if count % 3 != 0:
-                continue
-            # rois = []
-            k = 0
-            for roi_list_here1 in st.session_state["all_rois2"]:
-                max = [0,0]
-                min = [10000,10000]
-                roi_list_here = roi_list_here1[1:]
-                for i in range(len(roi_list_here)-1):
-                    if roi_list_here[i][0] > max[0]:
-                        max[0] = roi_list_here[i][0]
-                    if roi_list_here[i][1] > max[1]:
-                        max[1] = roi_list_here[i][1]
-                    if roi_list_here[i][0] < min[0]:
-                        min[0] = roi_list_here[i][0]
-                    if roi_list_here[i][1] < min[1]:
-                        min[1] = roi_list_here[i][1]
-                frame_cropped = frame[min[1]:max[1],min[0]:max[0]]
-                roi_corners = np.array([roi_list_here],dtype=np.int32)
-                mask = np.zeros(frame.shape,dtype=np.uint8)
-                mask.fill(255)
-                channel_count = frame.shape[2]
-                ignore_mask_color = (255,)*channel_count
-                cv2.fillPoly(mask,roi_corners,0)
-                mask_cropped = mask[min[1]:max[1],min[0]:max[0]]
-                roi = cv2.bitwise_or(frame_cropped,mask_cropped)
-
-                #roi = frame[roi_list_here[0][1]:roi_list_here[1][1],roi_list_here[0][0]:roi_list_here[1][0]]
-                number = []
-                results = model1.predict(roi)
-                results_pothole = model2.predict(source=frame)
-                for r in results:
-                    boxes = r.boxes
-                    counter = 0
-                    for box in boxes:
-                        counter += 1
-                        name = classes[box.cls.numpy()[0]]
-                        conf = str(round(box.conf.numpy()[0],2))
-                        text = name+conf
-                        bbox = box.xyxy[0].numpy()
-                        cv2.rectangle(frame,(int(bbox[0])+min[0],int(bbox[1])+min[1]),(int(bbox[2])+min[0],int(bbox[3])+min[1]),(0,255,0),2)
-                        cv2.putText(frame,text,(int(bbox[0])+min[0],int(bbox[1])+min[1]-5),cv2.FONT_HERSHEY_SIMPLEX, 0.4,(0,0,255),2)
-                    number.append(counter)
-                for r in results_pothole:
-                    masks = r.masks
-                    boxes = r.boxes.cpu().numpy()
-                    xyxys = boxes.xyxy
-                    confs = boxes.conf
-                    if masks is not None:
-                        shapes = np.ones_like(frame)
-                        for mask,conf,xyxy in zip(masks,confs,xyxys):
-                            polygon = mask.xy[0]
-                            if conf >= 0.49 and len(polygon)>=3:
-                                cv2.fillPoly(shapes,pts=np.int32([polygon]),color=(0,0,255,0.5))
-                                frame = cv2.addWeighted(frame,0.7,shapes,0.3,gamma=0)
-                                cv2.rectangle(frame,(int(xyxy[0]),int(xyxy[1])),(int(xyxy[2]),int(xyxy[3])),(0,0,255),2)
-                                cv2.putText(frame,'Pothole '+str(conf),(int(xyxy[0]),int(xyxy[1])-5),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),2)
-
-                avg = sum(number)/len(number)
-                stats = str(round(avg,2))
-                if count % 10 == 0:
-                    st.session_state.data[f"ROI{k}"].append(avg)
-                    k+=1
-                cv2.putText(frame,stats,(min[0],min[1]),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,0),4)
-                cv2.polylines(frame,roi_corners,True,(255,0,0),2)
-                if counter >= 5:
-                    cv2.putText(frame,'!!CONGESTION MORE THAN '+str(counter)+' Objects',(min[0]+20,min[1]+20),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,0),4)
-                cv2.polylines(frame,roi_corners,True,(255,0,0),2)
-                cv2.putText(frame,'Objects in the Regions of Interest',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
-            frame_placeholder.image(frame,channels='BGR')
-        cap.release()
-
-        df = pd.DataFrame(st.session_state.data)
-        df.to_csv('PS2.csv', sep='\t', encoding='utf-8')
-
-    else:
-        st.error('PLEASE UPLOAD AN IMAGE OF THE FORMAT JPG,JPEG OR PNG', icon="🚨")
-
-elif page == "PS2":
-    st.header("CLICK ON RUN SCRIPT TO START A TRAFFIC SIMULATION")
-    script = st.button("RUN SCRIPT")
-    st.session_state.con = -1
-    if script:
-        st.session_state.con += 1
-        import gymnasium as gym
-        import sumo_rl
-        import os
-        from stable_baselines3 import DQN
-        from stable_baselines3.common.vec_env import DummyVecEnv
-        from stable_baselines3.common.evaluation import evaluate_policy
-        from sumo_rl import SumoEnvironment
-        env = gym.make('sumo-rl-v0',
-                net_file='single-intersection.net.xml',
-                route_file='single-intersection-gen.rou.xml',
-                out_csv_name='output',
-                use_gui=True,
-                single_agent=True,    
-                num_seconds=10000)
-        model1 = DQN.load('DQN_MODEL3.zip',env=env)
-        one,two = evaluate_policy(model1,env = env,n_eval_episodes=5,render=True)
-        st.write("Evaluation Results: ",one,two)
-        import matplotlib.pyplot as plt
-        def eval_plot(path,metric,path_compare = None):
-            data = pd.read_csv(path)
-            if path_compare is not None:
-                data1 = pd.read_csv(path_compare)
-            x = []
-            for i in range(0,len(data)):
-                x.append(i)
-
-            y = data[metric]
-            y_1 = pd.to_numeric(y)
-            y_arr = np.array(y_1)
-            if path_compare is not None:
-                y2 = data1[metric]
-                y_2 = pd.to_numeric(y2)
-                y_arr2 = np.array(y_2)
-
-            x_arr = np.array(x)
-
-            fig = plt.figure()
-            ax1 = fig.add_subplot(2, 1, 1)
-            ax1.set_title(metric)
-            if path_compare is not None:
-                ax2 = fig.add_subplot(2, 1, 2,sharey=ax1)
-                ax2.set_title('compare '+metric)
-
-            ax1.plot(x_arr,y_arr)
-            
-            if path_compare  is not None:
-                ax2.plot(x_arr,y_arr2)
-            
-            return fig
-        for i in range(1,2):
-            st.pyplot(eval_plot(f'output_conn{st.session_state.con}_ep{i}.csv','system_mean_waiting_time'))
-            st.pyplot(eval_plot(f'output_conn{st.session_state.con}_ep{i}.csv','agents_total_accumulated_waiting_time'))  
-
-elif page == "Chat with Results":
-    st.title('Chat with the Results')
-    st.write("Please upload the relevant CSV data to get started")
-    reload = st.button('Reload')
-    if 'isran' not in st.session_state or reload == True:
-        st.session_state['isran'] = False
-
-
-    uploaded_file = st.file_uploader('Choose your .csv file', type=["csv"])
-    if uploaded_file is not None and st.session_state['isran'] == False:
-        with open("temp.csv", "wb") as f:
-            f.write(uploaded_file.getvalue())
-        loader = CSVLoader('temp.csv')
-        docs = loader.load()
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)
-        splits = text_splitter.split_documents(docs)
-        
-        embeddings = OllamaEmbeddings(model='mistral')
-        st.session_state.vectorstore = Chroma.from_documents(documents=splits,embedding=embeddings)
-        st.session_state['isran'] = True
-
-    if st.session_state['isran'] == True:
-        st.write("Embedding created")
-
-    def fdocs(docs):
-        return "\n\n".join(doc.page_content for doc in docs)
-
-    def llm(question,context):
-        formatted_prompt = f"Question: {question}\n\nContext:{context}"
-        response = ollama.chat(model='mistral', messages=[
-            {
-                'role': 'user',
-                'content': formatted_prompt
-            },
-            ])
-        return response['message']['content']
-
-
-
-    def rag_chain(question): 
-        retriever = st.session_state.vectorstore.as_retriever()
-        retrieved_docs = retriever.invoke(question)
-        formatted_context = fdocs(retrieved_docs)
-        return llm(question,formatted_context)
-
-    if 'messages' not in st.session_state:
-        st.session_state.messages = []
-
-    for message in st.session_state.messages:
-        st.chat_message(message['role']).markdown(message['content'])
-
-    prompt = st.chat_input("Say something")
-    response = rag_chain(prompt)
-    if prompt:
-        st.chat_message('user').markdown(prompt)
-        st.session_state.messages.append({'role':'user','content':prompt})
-        st.session_state.messages.append({'role':'AI','content':response})
-        st.chat_message('AI').markdown(response)
+from ultralytics import YOLO
+import base64
+import cv2
+import io
+import numpy as np
+from ultralytics.utils.plotting import Annotator
+import streamlit as st
+from streamlit_image_coordinates import streamlit_image_coordinates
+import pandas as pd
+import ollama
+import bs4
+import tempfile
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.document_loaders import CSVLoader
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+def set_background(image_file1,image_file2):
+    
+    with open(image_file1, "rb") as f:
+        img_data1 = f.read()
+    b64_encoded1 = base64.b64encode(img_data1).decode()
+    with open(image_file2, "rb") as f:
+        img_data2 = f.read()
+    b64_encoded2 = base64.b64encode(img_data2).decode()
+    style = f"""
+        <style>
+        .stApp{{
+            background-image: url(data:image/png;base64,{b64_encoded1});
+            background-size: cover;
+            
+        }}
+        .st-emotion-cache-6qob1r{{
+            background-image: url(data:image/png;base64,{b64_encoded2});
+            background-size: cover;
+            border: 5px solid rgb(14, 17, 23);
+            
+        }}
+        </style>
+    """
+    st.markdown(style, unsafe_allow_html=True)
+
+set_background('pngtree-city-map-navigation-interface-picture-image_1833642.png','2024-05-18_14-57-09_5235.png')
+
+st.title("Traffic Flow and Optimization Toolkit")
+
+sb = st.sidebar # defining the sidebar
+
+sb.markdown("🛰️ **Navigation**")
+page_names = ["PS1", "PS2", "PS3","Chat with Results"]
+page = sb.radio("", page_names, index=0)
+st.session_state['n'] = sb.slider("Number of ROIs",1,5)
+
+if page == 'PS1':
+    uploaded_file = st.file_uploader("Choose a video...", type=["mp4", "mpeg"])
+    model = YOLO('yolov8n.pt')
+    if uploaded_file is not None:
+        with tempfile.NamedTemporaryFile(delete=False) as temp:
+            temp.write(uploaded_file.read())
+            if 'roi_list1' not in st.session_state:
+                st.session_state['roi_list1'] = []
+            if "all_rois1" not in st.session_state:    
+                st.session_state['all_rois1'] = []
+            classes = model.names
+
+            done_1 = st.button('Selection Done')
+
+            while len(st.session_state["all_rois1"]) < st.session_state['n']:
+                cap = cv2.VideoCapture(temp.name)
+                while not done_1:
+                    ret,frame=cap.read()
+                    cv2.putText(frame,'SELECT ROI',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
+                    if not ret:
+                        st.write('ROI selection unsuccessfull')
+                        break
+                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                    value = streamlit_image_coordinates(frame,key='numpy',width=750)
+                    st.session_state["roi_list1"].append([int(value['x']*2.55),int(value['y']*2.55)])
+                    st.write(st.session_state["roi_list1"])
+                    if cv2.waitKey(0)&0xFF==27:
+                        break
+                cap.release()
+                st.session_state["all_rois1"].append(st.session_state["roi_list1"])
+                st.session_state["roi_list1"] = []
+                done_1 = False
+
+            st.write('ROI indices: ',st.session_state["all_rois1"][0])
+
+
+
+            cap = cv2.VideoCapture(temp.name)
+            st.write("Detection started")
+            st.session_state['fps'] = cap.get(cv2.CAP_PROP_FPS)
+            st.write(f"FPS OF VIDEO: {st.session_state['fps']}")
+            avg_list = []
+            count = 0
+            frame_placeholder = st.empty()
+            st.session_state["data1"] = {}
+            for i in range(len(st.session_state["all_rois1"])):
+                st.session_state["data1"][f"ROI{i}"] = []
+            while cap.isOpened():
+                ret,frame=cap.read()
+                if not ret:
+                    break
+                count += 1
+                if count % 3 != 0:
+                    continue
+                k = 0
+                for roi_list_here1 in st.session_state["all_rois1"]:
+                    max = [0,0]
+                    min = [10000,10000]
+                    roi_list_here = roi_list_here1[1:]
+                    for i in range(len(roi_list_here)):
+                        if roi_list_here[i][0] > max[0]:
+                            max[0] = roi_list_here[i][0]
+                        if roi_list_here[i][1] > max[1]:
+                            max[1] = roi_list_here[i][1]
+                        if roi_list_here[i][0] < min[0]:
+                            min[0] = roi_list_here[i][0]
+                        if roi_list_here[i][1] < min[1]:
+                            min[1] = roi_list_here[i][1]
+                    frame_cropped = frame[min[1]:max[1],min[0]:max[0]]
+                    roi_corners = np.array([roi_list_here],dtype=np.int32)
+                    mask = np.zeros(frame.shape,dtype=np.uint8)
+                    mask.fill(255)
+                    channel_count = frame.shape[2]
+                    ignore_mask_color = (255,)*channel_count
+                    cv2.fillPoly(mask,roi_corners,0)
+                    mask_cropped = mask[min[1]:max[1],min[0]:max[0]]
+                    roi = cv2.bitwise_or(frame_cropped,mask_cropped)
+
+                    #roi = frame[roi_list_here[0][1]:roi_list_here[1][1],roi_list_here[0][0]:roi_list_here[1][0]]
+                    number = []
+                    results = model.predict(roi)
+                    for r in results:
+                        boxes = r.boxes
+                        counter = 0
+                        for box in boxes:
+                            counter += 1
+                            name = classes[box.cls.numpy()[0]]
+                            conf = str(round(box.conf.numpy()[0],2))
+                            text = name+""+conf
+                            bbox = box.xyxy[0].numpy()
+                            cv2.rectangle(frame,(int(bbox[0])+min[0],int(bbox[1])+min[1]),(int(bbox[2])+min[0],int(bbox[3])+min[1]),(0,255,0),2)
+                            cv2.putText(frame,text,(int(bbox[0])+min[0],int(bbox[1])+min[1]-5),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),2)
+                        number.append(counter)
+                    avg = sum(number)/len(number)
+                    stats = str(round(avg,2))
+                    if count%10 == 0:
+                        st.session_state["data1"][f"ROI{k}"].append(avg)
+                        k+=1
+                    cv2.putText(frame,stats,(min[0],min[1]),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,0),4)
+                    cv2.polylines(frame,roi_corners,True,(255,0,0),2)
+                cv2.putText(frame,'The average number of vehicles in the Regions of Interest',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
+                frame_placeholder.image(frame,channels='BGR')
+            cap.release()
+        st.write("The resultant data is:")
+        st.write(st.session_state.data1)
+    else:
+        st.error('PLEASE UPLOAD AN IMAGE OF THE FORMAT JPG,JPEG OR PNG', icon="🚨")
+
+elif page == "PS3":
+    uploaded_file1 = st.file_uploader("Choose a video...", type=["mp4", "mpeg"])
+    model1 = YOLO("yolov8n.pt")
+    model2 = YOLO("best.pt")
+    if uploaded_file1 is not None:
+        with tempfile.NamedTemporaryFile(delete=False) as temp:
+            temp.write(uploaded_file.read())
+            if 'roi_list2' not in st.session_state:
+                st.session_state['roi_list2'] = []
+            if "all_rois2" not in st.session_state:    
+                st.session_state['all_rois2'] = []
+            classes = model1.names
+    
+            done_2 = st.button('Selection Done')
+    
+            while len(st.session_state["all_rois2"]) < st.session_state['n']:
+                cap = cv2.VideoCapture(temp.name)
+                while not done_2:
+                    ret,frame=cap.read()
+                    cv2.putText(frame,'SELECT ROI',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
+                    if not ret:
+                        st.write('ROI selection has concluded')
+                        break
+                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                    value = streamlit_image_coordinates(frame,key='numpy',width=750)
+                    st.session_state["roi_list2"].append([int(value['x']*2.5),int(value['y']*2.5)])
+                    st.write(st.session_state["roi_list2"])
+                    if cv2.waitKey(0)&0xFF==27:
+                        break
+                cap.release()
+                st.session_state["all_rois2"].append(st.session_state["roi_list2"])
+                st.session_state["roi_list2"] = []
+                done_2 = False
+    
+            st.write('ROI indices: ',st.session_state["all_rois2"][0])
+    
+    
+    
+            cap = cv2.VideoCapture(temp.name)
+            st.write("Detection started")
+            avg_list = []
+            count = 0
+            frame_placeholder = st.empty()
+            st.session_state.data = {}
+            for i in range(len(st.session_state["all_rois2"])):
+                st.session_state["data"][f"ROI{i}"] = []
+            for i in range(len(st.session_state['all_rois2'])):
+                st.session_state.data[f"ROI{i}"] = []
+            while cap.isOpened():
+                ret,frame=cap.read()
+                if not ret:
+                    break
+                count += 1
+                if count % 3 != 0:
+                    continue
+                # rois = []
+                k = 0
+                for roi_list_here1 in st.session_state["all_rois2"]:
+                    max = [0,0]
+                    min = [10000,10000]
+                    roi_list_here = roi_list_here1[1:]
+                    for i in range(len(roi_list_here)-1):
+                        if roi_list_here[i][0] > max[0]:
+                            max[0] = roi_list_here[i][0]
+                        if roi_list_here[i][1] > max[1]:
+                            max[1] = roi_list_here[i][1]
+                        if roi_list_here[i][0] < min[0]:
+                            min[0] = roi_list_here[i][0]
+                        if roi_list_here[i][1] < min[1]:
+                            min[1] = roi_list_here[i][1]
+                    frame_cropped = frame[min[1]:max[1],min[0]:max[0]]
+                    roi_corners = np.array([roi_list_here],dtype=np.int32)
+                    mask = np.zeros(frame.shape,dtype=np.uint8)
+                    mask.fill(255)
+                    channel_count = frame.shape[2]
+                    ignore_mask_color = (255,)*channel_count
+                    cv2.fillPoly(mask,roi_corners,0)
+                    mask_cropped = mask[min[1]:max[1],min[0]:max[0]]
+                    roi = cv2.bitwise_or(frame_cropped,mask_cropped)
+    
+                    #roi = frame[roi_list_here[0][1]:roi_list_here[1][1],roi_list_here[0][0]:roi_list_here[1][0]]
+                    number = []
+                    results = model1.predict(roi)
+                    results_pothole = model2.predict(source=frame)
+                    for r in results:
+                        boxes = r.boxes
+                        counter = 0
+                        for box in boxes:
+                            counter += 1
+                            name = classes[box.cls.numpy()[0]]
+                            conf = str(round(box.conf.numpy()[0],2))
+                            text = name+conf
+                            bbox = box.xyxy[0].numpy()
+                            cv2.rectangle(frame,(int(bbox[0])+min[0],int(bbox[1])+min[1]),(int(bbox[2])+min[0],int(bbox[3])+min[1]),(0,255,0),2)
+                            cv2.putText(frame,text,(int(bbox[0])+min[0],int(bbox[1])+min[1]-5),cv2.FONT_HERSHEY_SIMPLEX, 0.4,(0,0,255),2)
+                        number.append(counter)
+                    for r in results_pothole:
+                        masks = r.masks
+                        boxes = r.boxes.cpu().numpy()
+                        xyxys = boxes.xyxy
+                        confs = boxes.conf
+                        if masks is not None:
+                            shapes = np.ones_like(frame)
+                            for mask,conf,xyxy in zip(masks,confs,xyxys):
+                                polygon = mask.xy[0]
+                                if conf >= 0.49 and len(polygon)>=3:
+                                    cv2.fillPoly(shapes,pts=np.int32([polygon]),color=(0,0,255,0.5))
+                                    frame = cv2.addWeighted(frame,0.7,shapes,0.3,gamma=0)
+                                    cv2.rectangle(frame,(int(xyxy[0]),int(xyxy[1])),(int(xyxy[2]),int(xyxy[3])),(0,0,255),2)
+                                    cv2.putText(frame,'Pothole '+str(conf),(int(xyxy[0]),int(xyxy[1])-5),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),2)
+    
+                    avg = sum(number)/len(number)
+                    stats = str(round(avg,2))
+                    if count % 10 == 0:
+                        st.session_state.data[f"ROI{k}"].append(avg)
+                        k+=1
+                    cv2.putText(frame,stats,(min[0],min[1]),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,0),4)
+                    cv2.polylines(frame,roi_corners,True,(255,0,0),2)
+                    if counter >= 5:
+                        cv2.putText(frame,'!!CONGESTION MORE THAN '+str(counter)+' Objects',(min[0]+20,min[1]+20),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,0),4)
+                    cv2.polylines(frame,roi_corners,True,(255,0,0),2)
+                    cv2.putText(frame,'Objects in the Regions of Interest',(100,100),cv2.FONT_HERSHEY_SIMPLEX, 1,(0,0,255),4)
+                frame_placeholder.image(frame,channels='BGR')
+            cap.release()
+        st.write("The result is:")
+        st.write(st.session.data)
+
+    else:
+        st.error('PLEASE UPLOAD AN IMAGE OF THE FORMAT JPG,JPEG OR PNG', icon="🚨")
+
+elif page == "PS2":
+    st.header("CLICK ON RUN SCRIPT TO START A TRAFFIC SIMULATION")
+    script = st.button("RUN SCRIPT")
+    st.session_state.con = -1
+    if script:
+        st.session_state.con += 1
+        import gymnasium as gym
+        import sumo_rl
+        import os
+        from stable_baselines3 import DQN
+        from stable_baselines3.common.vec_env import DummyVecEnv
+        from stable_baselines3.common.evaluation import evaluate_policy
+        from sumo_rl import SumoEnvironment
+        env = gym.make('sumo-rl-v0',
+                net_file='single-intersection.net.xml',
+                route_file='single-intersection-gen.rou.xml',
+                out_csv_name='output',
+                use_gui=True,
+                single_agent=True,    
+                num_seconds=5000)
+        model1 = DQN.load('DQN_MODEL3.zip',env=env)
+        st.write("The Simulation is currently running for 5000 steps, Results will be shown shortly.....")
+        one,two = evaluate_policy(model1,env = env,n_eval_episodes=5,render=True)
+        st.write("Evaluation Results: \nPer Episode Rewards(Higher the better):",one,"\nPer-episode lengths (in number of steps):",two)
+        import matplotlib.pyplot as plt
+        def eval_plot(path,metric,path_compare = None):
+            data = pd.read_csv(path)
+            if path_compare is not None:
+                data1 = pd.read_csv(path_compare)
+            x = []
+            for i in range(0,len(data)):
+                x.append(i)
+
+            y = data[metric]
+            y_1 = pd.to_numeric(y)
+            y_arr = np.array(y_1)
+            if path_compare is not None:
+                y2 = data1[metric]
+                y_2 = pd.to_numeric(y2)
+                y_arr2 = np.array(y_2)
+
+            x_arr = np.array(x)
+
+            fig = plt.figure()
+            ax1 = fig.add_subplot(2, 1, 1)
+            ax1.set_title(metric)
+            if path_compare is not None:
+                ax2 = fig.add_subplot(2, 1, 2,sharey=ax1)
+                ax2.set_title('compare '+metric)
+
+            ax1.plot(x_arr,y_arr)
+            
+            if path_compare  is not None:
+                ax2.plot(x_arr,y_arr2)
+            
+            return fig
+        for i in range(1,2):
+            st.pyplot(eval_plot(f'output_conn{st.session_state.con}_ep{i}.csv','system_mean_waiting_time'))
+            st.pyplot(eval_plot(f'output_conn{st.session_state.con}_ep{i}.csv','agents_total_accumulated_waiting_time'))  
+
+elif page == "Chat with Results":
+    st.title('Chat with the Results')
+    st.write("Please upload the relevant CSV data to get started")
+    reload = st.button('Reload')
+    if 'isran' not in st.session_state or reload == True:
+        st.session_state['isran'] = False
+
+
+    uploaded_file = st.file_uploader('Choose your .csv file', type=["csv"])
+    if uploaded_file is not None and st.session_state['isran'] == False:
+        with open("temp.csv", "wb") as f:
+            f.write(uploaded_file.getvalue())
+        loader = CSVLoader('temp.csv')
+        docs = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)
+        splits = text_splitter.split_documents(docs)
+        
+        embeddings = OllamaEmbeddings(model='mistral')
+        st.session_state.vectorstore = Chroma.from_documents(documents=splits,embedding=embeddings)
+        st.session_state['isran'] = True
+
+    if st.session_state['isran'] == True:
+        st.write("Embedding created")
+
+    def fdocs(docs):
+        return "\n\n".join(doc.page_content for doc in docs)
+
+    def llm(question,context):
+        formatted_prompt = f"Question: {question}\n\nContext:{context}"
+        response = ollama.chat(model='mistral', messages=[
+            {
+                'role': 'user',
+                'content': formatted_prompt
+            },
+            ])
+        return response['message']['content']
+
+
+
+    def rag_chain(question): 
+        retriever = st.session_state.vectorstore.as_retriever()
+        retrieved_docs = retriever.invoke(question)
+        formatted_context = fdocs(retrieved_docs)
+        return llm(question,formatted_context)
+
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+
+    for message in st.session_state.messages:
+        st.chat_message(message['role']).markdown(message['content'])
+
+    prompt = st.chat_input("Say something")
+    response = rag_chain(prompt)
+    if prompt:
+        st.chat_message('user').markdown(prompt)
+        st.session_state.messages.append({'role':'user','content':prompt})
+        st.session_state.messages.append({'role':'AI','content':response})
+        st.chat_message('AI').markdown(response)