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app.py

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+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
+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"])
+    if uploaded_file is not None:
+        g = io.BytesIO(uploaded_file.read()) 
+        temporary_location = "temp_PS1.mp4"
+
+        with open(temporary_location, 'wb') as out:  
+            out.write(g.read())  
+        out.close()
+        model = YOLO('yolov8n.pt')
+        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_PS1.mp4')
+            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 has concluded')
+                    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_PS1.MP4')
+        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()
+        df = pd.DataFrame(st.session_state["data1"])
+        df.to_csv('PS1.csv', sep='\t', encoding='utf-8')
+    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)