remove Streamlit folder

Streamlit/.streamlit/config.toml

@@ -1,5 +0,0 @@
-[theme]
-primaryColor="#1ea86f"
-backgroundColor="#61eab0"
-secondaryBackgroundColor="#ffffff"
-textColor="#000000"

Streamlit/.streamlit/secrets.toml

@@ -1 +0,0 @@
-OPENAI_API_KEY = "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"

Streamlit/Home.py

@@ -1,53 +0,0 @@
-import streamlit as st
-
-st.set_page_config(
-    page_title="OpenEyes",
-    page_icon=":eyes:",
-)
-
-st.title("👀 OpenEyes")
-
-# Welcome message
-st.write("Welcome to the 👀 OpenEyes Homepage!")
-
-# goto the chat section to talk to the bot
-# st.write("Go to the chat section to talk to the bot!")
-st.info(
-    """👈 Go to the chat section to talk to the bot!
-"""
-)
-
-with st.expander("💡 Idea"):
-    st.markdown(
-        """
-        - The idea behind this project is to create a chatbot that can answer questions related to animals.
-        - The chatbot is trained on the [AnimalQA dataset](
-        https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals
-        ) from Kaggle.
-        We thought that this app will help the tourists who are visiting safari parks and zoos and want to know more about the animals they see.
-        They can scan the animals from their phones and our Object Detection model will detect the animal and then the chatbot will answer the questions related to the animal.
-        """
-    )
-
-with st.expander("🐍 Python Libraries Used"):
-    st.markdown(
-        """
-        - `Streamlit` - For the frontend
-        - `OpenAI` - For the chatbot
-        - `Requests` - For making HTTP requests
-        - `Time` - For adding delays
-        - `Base64` - For encoding and decoding
-        - `JSON` - For parsing JSON
-        - `Pillow` - For image processing
-        - `Numpy` - For numerical processing
-        - `Pandas` - For data processing
-        - `Matplotlib` - For data visualization
-        - `YOLOv8` - For object detection
-        - `PyTorch` - For deep learning
-        - `Transformers` - For NLP
-        - `HuggingFace` - For NLP
-        - `Whisper` - For speech recognition
-        - `PyAudio` - For audio processing
-        
-        """
-    )

Streamlit/pages/.cph/.yolo.py_ab91d06c1049f91607ef5865ba1b5ac0.prob

@@ -1 +0,0 @@
-{"name":"Local: yolo","url":"c:\\Users\\hp\\Documents\\GitHub\\OpenEyes\\Streamlit\\pages\\yolo.py","tests":[{"id":1688839560533,"input":"","output":""}],"interactive":false,"memoryLimit":1024,"timeLimit":3000,"srcPath":"c:\\Users\\hp\\Documents\\GitHub\\OpenEyes\\Streamlit\\pages\\yolo.py","group":"local","local":true}

Streamlit/pages/1_📷_Camera.py

@@ -1,45 +0,0 @@
-import streamlit as st
-from PIL import Image
-from ultralytics import YOLO
-
-# initialize the model
-model = YOLO("yolov8n.pt")
-
-# radio button to upload a picture or take a picture from webcam
-st.sidebar.subheader("Select Input Source")
-input_type = st.sidebar.radio(" ", ("Upload an Image", "Take a Picture"))
-
-global picture
-
-if input_type == "Upload an Image":
-    picture = st.file_uploader("Select an image", type=["jpg", "jpeg", "png"])
-else:
-    # display the picture
-    picture = st.camera_input("Take a picture")
-
-
-if picture:
-    st.image(picture, caption="Your picture", use_column_width=True)
-
-    # save the picture
-    with open("image.jpg", "wb") as f:
-        f.write(picture.getvalue())  # write the picture to the disk
-
-    st.info("Select the confidence threshold for the object detection model and press Detect Objects")
-
-    # confidence slider
-    st.sidebar.subheader("Confidence Threshold")
-    confidence_threshold = st.sidebar.slider("Select a value", 0.0, 1.0, 0.5, 0.01)
-
-    source_img = Image.open("image.jpg")
-
-    if st.sidebar.button("Detect Objects"):
-        # start the detection
-        res = model.predict(source_img, conf=confidence_threshold)
-        boxes = res[0].boxes
-        res_plotted = res[0].plot()[:, :, ::-1]
-        st.image(res_plotted, caption="Detected Image", use_column_width=True)
-
-        st.balloons()
-
-        st.success("Head over to the chat page to ask questions and link to the page")

Streamlit/pages/✉_Chat.py

@@ -1,98 +0,0 @@
-# import openai
-import streamlit as st
-import requests
-import time
-import base64
-import json
-
-st.set_page_config(
-    page_title="OpenEyes",
-    page_icon="👀",
-)
-
-st.title("OpenEyes")
-
-# API_KEY = st.secrets["OPENAI_API_KEY"]
-
-
-def makeRequest(prompt):
-    url = "http://3.88.181.187:8080/v1/"
-    headers = {"Content-Type": "application/json"}
-    data = {
-        "model": "gpt-4",
-        "messages": [{"role": "user", "content": prompt}],
-    }
-    response = requests.post(url, headers=headers, json=data)
-    return response.json()
-
-
-if "openai_model" not in st.session_state:
-    st.session_state["openai_model"] = "gpt-4"  # setting up a session state model
-
-if "messages" not in st.session_state:
-    # st.session_state.messages = [] # setting up a session state messages to store the messages
-    # set a default message hi to the bot before the user types anything
-    st.session_state.messages = [
-        {
-            "role": "assistant",
-            "content": "Hi, I'm OpenEyes. I'm here to help you with your queries related to Animals. What do you want to know?",
-        }
-    ]
-    
-
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):  # creating a chat message with the role
-        st.markdown(message["content"])  # adding the content to the chat message
-
-# Accept user input
-if prompt := st.chat_input("What is up?"):
-    # Add user message to chat history
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    # Display user message in chat message container
-    with st.chat_message("user"):
-        st.markdown(prompt)
-    # Display assistant response in chat message container
-    with st.chat_message("assistant"):
-        message_placeholder = st.empty()
-        full_response = ""
-        response = makeRequest(prompt)
-        for chunk in response["choices"][0]["message"]["content"].split():
-            full_response += chunk + " "
-            time.sleep(0.05)
-            # Add a blinking cursor to simulate typing
-            message_placeholder.markdown(full_response + "▌")
-        message_placeholder.markdown(full_response)
-        st.session_state.messages.append(
-            {"role": "assistant", "content": full_response}
-        )
-
-
-# 3 cols
-col1, col2, col3 = st.columns([1, 1, 1])
-
-with col1:
-    if st.button("Export Chat History"):
-        # Download the chat history as a json file
-        # Convert JSON data to a string and encode as UTF-8
-        json_data = json.dumps(st.session_state.messages).encode("utf-8")
-
-        b64 = base64.b64encode(json_data).decode()
-        href = f'<a href="data:application/json;base64,{b64}" download="example.json">Download JSON</a>'
-        st.markdown(href, unsafe_allow_html=True)
-
-with col3:
-    # Clear chat button
-    if st.button("Clear Chat"):
-        # remove all the messages from the session state
-        st.session_state.messages = []
-        # add a default message to the session state
-        st.session_state.messages.append(
-            {
-                "role": "assistant",
-                "content": "Hi, I'm OpenEyes. I'm here to help you with your queries related to Animals. What do you want to know?",
-            }
-        )
-        # rerun the app
-        st.experimental_rerun()
-
-print(st.session_state.messages)

Streamlit/pages/🧑_About.py

@@ -1,52 +0,0 @@
-import streamlit as st
-
-
-st.set_page_config(
-    page_title="About OpenEyes Devs",
-    page_icon="👀",
-)
-
-TeamMember1 = {
-    "Name": "Adarsh Anand",
-    "image": "https://avatars.githubusercontent.com/u/73928744?v=4",
-    "Role": "Intern @Intel | ICPC'22 Regionalist | Specialist @CodeForces | Ex-Graphy | Knight @LeetCode | Google DSC Lead '22 | IIT Goa CSE '24",
-    "LinkedIn": "https://www.linkedin.com/in/adarsh-anand-iitgoa/",
-    "GitHub": "https://github.com/adarshanand67",
-    "description": "I am a Full-stack developer with a passion for problem-solving. I love working on backend and frontend projects, but my true love is React.js. I enjoy working on projects that are challenging and have the potential to make a positive impact on people's lives.",
-}
-
-TeamMember2 = {
-    "Name": "Aniket Akshay Chaudhri",
-    "image": "https://avatars.githubusercontent.com/u/79798301?v=4",
-    "Role": "Head @ Coding Club IIT Goa | Competitive Programmer | Web Developer | Android Developer | CSE @ IIT Goa",
-    "LinkedIn": "https://www.linkedin.com/in/aniketchaudhri/",
-    "GitHub": "https://github.com/AniketChaudhri/",
-    "description": "I am a CSE pre-final year Undergrad at IIT Goa. I have experience in various Tech Domains such as Web Development, App Development, Deep Learning.",
-}
-
-# adarsh = Image.open("/Streamlit/adarsh.jpg")
-# aniket = Image.open("/Streamlit/aniket.jpg")
-
-# layout 2 cols
-st.title("About OpenEyes")
-col1, col2, col3 = st.columns([3,1,3])
-
-# center text
-# st.markdown("<h1 style='text-align: center; color: black;'>Meet the Team</h1>", unsafe_allow_html=True)
-
-# create 2 cards showing the details
-
-def show_details(TeamMember):
-    st.image(TeamMember["image"], width=200)
-    st.header(TeamMember["Name"])
-    st.markdown(TeamMember["Role"])
-    st.subheader(f"[LinkedIn]({TeamMember['LinkedIn']})")
-    st.subheader(f"[GitHub]({TeamMember['GitHub']})")
-    st.write(TeamMember["description"])
-
-# add border
-with col1:
-    show_details(TeamMember2)
-
-with col3:
-    show_details(TeamMember1)

Streamlit/yolov8n.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:31e20dde3def09e2cf938c7be6fe23d9150bbbe503982af13345706515f2ef95
-size 6534387