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shubham5027 commited on Aug 23, 2024

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.gitattributes +1 -0
.streamlit/config.toml +2 -0
exifa.py +1176 -0
img/1.png +0 -0
img/3.png +0 -0
img/Exifa-1.png +0 -0
img/Exifa-2.png +0 -0
img/Exifa-3.png +0 -0
img/Exifa-4.png +0 -0
img/Exifa-5.png +0 -0
img/Exifa.gif +0 -0
img/Headshot.png +0 -0
img/assistant-done.svg +1 -0
img/assistant.gif +3 -0
img/email.gif +0 -0
img/file1 +1 -0
img/kaggle.gif +0 -0
img/linkedin.gif +0 -0
img/newsletter.gif +0 -0
img/share.gif +0 -0
img/topmate.gif +0 -0
img/user-done.svg +1 -0
img/user.gif +0 -0
requirements.txt +12 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+img/assistant.gif filter=lfs diff=lfs merge=lfs -text

.streamlit/config.toml ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ [theme]
2	+ base = "dark"

exifa.py ADDED Viewed

	@@ -0,0 +1,1176 @@

+"""
+All code contributed to Exifa.net is © 2024 by Sahir Maharaj.
+The content is licensed under the Creative Commons Attribution 4.0 International License.
+This allows for sharing and adaptation, provided appropriate credit is given, and any changes made are indicated.
+When using the code from Exifa.net, please credit as follows: "Code sourced from Exifa.net, authored by Sahir Maharaj, 2024."
+For reporting bugs, requesting features, or further inquiries, please reach out to Sahir Maharaj at sahir@sahirmaharaj.com.
+Connect with Sahir Maharaj on LinkedIn for updates and potential collaborations: https://www.linkedin.com/in/sahir-maharaj/
+Hire Sahir Maharaj: https://topmate.io/sahirmaharaj/362667
+"""
+import streamlit as st
+import replicate
+import os
+import pdfplumber
+from docx import Document
+import pandas as pd
+from io import BytesIO
+from transformers import AutoTokenizer
+import exifread
+import requests
+from PIL import Image
+import numpy as np
+import plotly.express as px
+import matplotlib.colors as mcolors
+import plotly.graph_objs as go
+import streamlit.components.v1 as components
+import random
+config = {
+    "toImageButtonOptions": {
+        "format": "png",
+        "filename": "custom_image",
+        "height": 720,
+        "width": 480,
+        "scale": 6,
+    }
+}
+icons = {
+    "assistant": "https://raw.githubusercontent.com/sahirmaharaj/exifa/2f685de7dffb583f2b2a89cb8ee8bc27bf5b1a40/img/assistant-done.svg",
+    "user": "https://raw.githubusercontent.com/sahirmaharaj/exifa/2f685de7dffb583f2b2a89cb8ee8bc27bf5b1a40/img/user-done.svg",
+}
+particles_js = """<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Particles.js</title>
+  <style>
+  #particles-js {
+    position: fixed;
+    width: 100vw;
+    height: 100vh;
+    top: 0;
+    left: 0;
+    z-index: -1; /* Send the animation to the back */
+  }
+  .content {
+    position: relative;
+    z-index: 1;
+    color: white;
+  }
+</style>
+</head>
+<body>
+  <div id="particles-js"></div>
+  <div class="content">
+    <!-- Placeholder for Streamlit content -->
+  </div>
+  <script src="https://cdn.jsdelivr.net/particles.js/2.0.0/particles.min.js"></script>
+  <script>
+    particlesJS("particles-js", {
+      "particles": {
+        "number": {
+          "value": 300,
+          "density": {
+            "enable": true,
+            "value_area": 800
+          }
+        },
+        "color": {
+          "value": "#ffffff"
+        },
+        "shape": {
+          "type": "circle",
+          "stroke": {
+            "width": 0,
+            "color": "#000000"
+          },
+          "polygon": {
+            "nb_sides": 5
+          },
+          "image": {
+            "src": "img/github.svg",
+            "width": 100,
+            "height": 100
+          }
+        },
+        "opacity": {
+          "value": 0.5,
+          "random": false,
+          "anim": {
+            "enable": false,
+            "speed": 1,
+            "opacity_min": 0.2,
+            "sync": false
+          }
+        },
+        "size": {
+          "value": 2,
+          "random": true,
+          "anim": {
+            "enable": false,
+            "speed": 40,
+            "size_min": 0.1,
+            "sync": false
+          }
+        },
+        "line_linked": {
+          "enable": true,
+          "distance": 100,
+          "color": "#ffffff",
+          "opacity": 0.22,
+          "width": 1
+        },
+        "move": {
+          "enable": true,
+          "speed": 0.2,
+          "direction": "none",
+          "random": false,
+          "straight": false,
+          "out_mode": "out",
+          "bounce": true,
+          "attract": {
+            "enable": false,
+            "rotateX": 600,
+            "rotateY": 1200
+          }
+        }
+      },
+      "interactivity": {
+        "detect_on": "canvas",
+        "events": {
+          "onhover": {
+            "enable": true,
+            "mode": "grab"
+          },
+          "onclick": {
+            "enable": true,
+            "mode": "repulse"
+          },
+          "resize": true
+        },
+        "modes": {
+          "grab": {
+            "distance": 100,
+            "line_linked": {
+              "opacity": 1
+            }
+          },
+          "bubble": {
+            "distance": 400,
+            "size": 2,
+            "duration": 2,
+            "opacity": 0.5,
+            "speed": 1
+          },
+          "repulse": {
+            "distance": 200,
+            "duration": 0.4
+          },
+          "push": {
+            "particles_nb": 2
+          },
+          "remove": {
+            "particles_nb": 3
+          }
+        }
+      },
+      "retina_detect": true
+    });
+  </script>
+</body>
+</html>
+"""
+st.set_page_config(page_title="Exifa.net", page_icon="✨", layout="wide")
+welcome_messages = [
+    "Hello! I'm Exifa, an AI assistant designed to make image metadata meaningful. Ask me anything!",
+    "Hi! I'm Exifa, an AI-powered assistant for extracting and explaining EXIF data. How can I help you today?",
+    "Hey! I'm Exifa, your AI-powered guide to understanding the metadata in your images. What would you like to explore?",
+    "Hi there! I'm Exifa, an AI-powered tool built to help you make sense of your image metadata. How can I help you today?",
+    "Hello! I'm Exifa, an AI-driven tool designed to help you understand your images' metadata. What can I do for you?",
+    "Hi! I'm Exifa, an AI-driven assistant designed to make EXIF data easy to understand. How can I help you today?",
+    "Welcome! I'm Exifa, an intelligent AI-powered tool for extracting and explaining EXIF data. How can I assist you today?",
+    "Hello! I'm Exifa, your AI-powered guide for understanding image metadata. Ask me anything!",
+    "Hi! I'm Exifa, an intelligent AI assistant ready to help you understand your images' metadata. What would you like to explore?",
+    "Hey! I'm Exifa, an AI assistant for extracting and explaining EXIF data. How can I help you today?",
+]
+message = random.choice(welcome_messages)
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [{"role": "assistant", "content": message}]
+if "exif_df" not in st.session_state:
+    st.session_state["exif_df"] = pd.DataFrame()
+if "url_exif_df" not in st.session_state:
+    st.session_state["url_exif_df"] = pd.DataFrame()
+if "show_expanders" not in st.session_state:
+    st.session_state.show_expanders = True
+if "reset_trigger" not in st.session_state:
+    st.session_state.reset_trigger = False
+if "uploaded_files" not in st.session_state:
+    st.session_state["uploaded_files"] = None
+if "image_url" not in st.session_state:
+    st.session_state["image_url"] = ""
+if "follow_up" not in st.session_state:
+    st.session_state.follow_up = False
+if "show_animation" not in st.session_state:
+    st.session_state.show_animation = True
+def clear_url():
+    st.session_state["image_url"] = ""
+def clear_files():
+    st.session_state["uploaded_files"] = None
+    st.session_state["file_uploader_key"] = not st.session_state.get(
+        "file_uploader_key", False
+    )
+def download_image(data):
+    st.download_button(
+        label="⇩ Download Image",
+        data=data,
+        file_name="image_no_exif.jpg",
+        mime="image/jpeg",
+    )
+def clear_chat_history():
+    st.session_state.reset_trigger = not st.session_state.reset_trigger
+    st.session_state.show_expanders = True
+    st.session_state.show_animation = True
+    st.session_state.messages = [{"role": "assistant", "content": message}]
+    st.session_state["exif_df"] = pd.DataFrame()
+    st.session_state["url_exif_df"] = pd.DataFrame()
+    uploaded_files = ""
+    if "uploaded_files" in st.session_state:
+        del st.session_state["uploaded_files"]
+    if "image_url" in st.session_state:
+        st.session_state["image_url"] = ""
+    st.cache_data.clear()
+    st.success("Chat History Cleared!")
+def clear_exif_data(image_input):
+    if isinstance(image_input, BytesIO):
+        image_input.seek(0)
+        image = Image.open(image_input)
+    elif isinstance(image_input, Image.Image):
+        image = image_input
+    else:
+        raise ValueError("Unsupported image input type")
+    data = list(image.getdata())
+    image_without_exif = Image.new(image.mode, image.size)
+    image_without_exif.putdata(data)
+    buffered = BytesIO()
+    image_without_exif.save(buffered, format="JPEG", quality=100, optimize=True)
+    buffered.seek(0)
+    return buffered.getvalue()
+with st.sidebar:
+    image_url = (
+        "https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/Exifa.gif"
+    )
+    st.markdown(
+        f"""
+        <div style='display: flex; align-items: center;'>
+            <img src='{image_url}' style='width: 50px; height: 50px; margin-right: 30px;'>
+            <h1 style='margin: 0;'>Exifa.net</h1>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+    expander = st.expander("🗀 File Input")
+    with expander:
+        image_url = st.text_input(
+            "Enter image URL for EXIF analysis:",
+            key="image_url",
+            on_change=clear_files,
+            value=st.session_state.image_url,
+        )
+        file_uploader_key = "file_uploader_{}".format(
+            st.session_state.get("file_uploader_key", False)
+        )
+        uploaded_files = st.file_uploader(
+            "Upload local files:",
+            type=["txt", "pdf", "docx", "csv", "jpg", "png", "jpeg"],
+            key=file_uploader_key,
+            on_change=clear_url,
+            accept_multiple_files=True,
+        )
+        if uploaded_files is not None:
+            st.session_state["uploaded_files"] = uploaded_files
+    expander = st.expander("⚒ Model Configuration")
+    with expander:
+        if "REPLICATE_API_TOKEN" in st.secrets:
+            replicate_api = st.secrets["REPLICATE_API_TOKEN"]
+        else:
+            replicate_api = st.text_input("Enter Replicate API token:", type="password")
+            if not (replicate_api.startswith("r8_") and len(replicate_api) == 40):
+                st.warning("Please enter your Replicate API token.", icon="⚠️")
+                st.markdown(
+                    "**Don't have an API token?** Head over to [Replicate](https://replicate.com/account/api-tokens) to sign up for one."
+                )
+        os.environ["REPLICATE_API_TOKEN"] = replicate_api
+        st.subheader("Adjust model parameters")
+        temperature = st.slider(
+            "Temperature", min_value=0.01, max_value=5.0, value=0.3, step=0.01
+        )
+        top_p = st.slider("Top P", min_value=0.01, max_value=1.0, value=0.2, step=0.01)
+        max_new_tokens = st.number_input(
+            "Max New Tokens", min_value=1, max_value=1024, value=512
+        )
+        min_new_tokens = st.number_input(
+            "Min New Tokens", min_value=0, max_value=512, value=0
+        )
+        presence_penalty = st.slider(
+            "Presence Penalty", min_value=0.0, max_value=2.0, value=1.15, step=0.05
+        )
+        frequency_penalty = st.slider(
+            "Frequency Penalty", min_value=0.0, max_value=2.0, value=0.2, step=0.05
+        )
+        stop_sequences = st.text_area("Stop Sequences", value="<|im_end|>", height=100)
+    if uploaded_files and not st.session_state["exif_df"].empty:
+        with st.expander("🗏 EXIF Details"):
+            st.dataframe(st.session_state["exif_df"])
+    if image_url and not st.session_state["url_exif_df"].empty:
+        with st.expander("🗏 EXIF Details"):
+            st.dataframe(st.session_state["url_exif_df"])
+    base_prompt = """
+    You are an expert EXIF Analyser. The user will provide an image file and you will explain the file EXIF in verbose detail.
+    Pay careful attention to the data of the EXIF image and create a profile for the user who took this image.
+    1. Make inferences on things like location, budget, experience, etc. (2 paragraphs)
+    2. Make as many inferences as possible about the exif data in the next 3 paragraphs.
+    3. Please follow this format, style, pacing and structure.
+    4. In addition to the content above, provide 1 more paragraph about the users financial standing based on the equipment they are using and estimate their experience.
+    DO NOT skip any steps.
+    FORMAT THE RESULT IN MULTIPLE PARAGRAPHS
+    Do not keep talking and rambling on - Get to the point.
+    """
+    if uploaded_files:
+        for uploaded_file in uploaded_files:
+            if uploaded_file.type == "application/pdf":
+                with pdfplumber.open(uploaded_file) as pdf:
+                    pages = [page.extract_text() for page in pdf.pages]
+                file_text = "\n".join(pages) if pages else ""
+            elif uploaded_file.type == "text/plain":
+                file_text = str(uploaded_file.read(), "utf-8")
+            elif (
+                uploaded_file.type
+                == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+            ):
+                doc = Document(uploaded_file)
+                file_text = "\n".join([para.text for para in doc.paragraphs])
+            elif uploaded_file.type == "text/csv":
+                df = pd.read_csv(uploaded_file)
+                file_text = df.to_string(index=False)
+            elif uploaded_file.type in ["image/jpeg", "image/png", "image/jpg"]:
+                import tempfile
+                with tempfile.NamedTemporaryFile(delete=False) as temp:
+                    temp.write(uploaded_file.read())
+                    temp.flush()
+                    temp.close()
+                    with open(temp.name, "rb") as file:
+                        tags = exifread.process_file(file)
+                    exif_data = {}
+                    for tag in tags.keys():
+                        if tag not in [
+                            "JPEGThumbnail",
+                            "TIFFThumbnail",
+                            "Filename",
+                            "EXIF MakerNote",
+                        ]:
+                            exif_data[tag] = str(tags[tag])
+                    df = pd.DataFrame(exif_data, index=[0])
+                    df.insert(loc=0, column="Image Feature", value=["Value"] * len(df))
+                    df = df.transpose()
+                    df.columns = df.iloc[0]
+                    df = df.iloc[1:]
+                    st.session_state["exif_df"] = df
+                    file_text = "\n".join(
+                        [
+                            f"{tag}: {tags[tag]}"
+                            for tag in tags.keys()
+                            if tag
+                            not in (
+                                "JPEGThumbnail",
+                                "TIFFThumbnail",
+                                "Filename",
+                                "EXIF MakerNote",
+                            )
+                        ]
+                    )
+                    os.unlink(temp.name)
+            base_prompt += "\n" + file_text
+    if image_url:
+        try:
+            response = requests.head(image_url)
+            if response.headers["Content-Type"] in [
+                "image/jpeg",
+                "image/png",
+                "image/jpg",
+            ]:
+                response = requests.get(image_url)
+                response.raise_for_status()
+                image_data = BytesIO(response.content)
+                image = Image.open(image_data)
+                image.load()
+                tags = exifread.process_file(image_data)
+                exif_data = {}
+                for tag in tags.keys():
+                    if tag not in [
+                        "JPEGThumbnail",
+                        "TIFFThumbnail",
+                        "Filename",
+                        "EXIF MakerNote",
+                    ]:
+                        exif_data[tag] = str(tags[tag])
+                df = pd.DataFrame(exif_data, index=[0])
+                df.insert(loc=0, column="Image Feature", value=["Value"] * len(df))
+                df = df.transpose()
+                df.columns = df.iloc[0]
+                df = df.iloc[1:]
+                st.session_state["url_exif_df"] = df
+                file_text = "\n".join(
+                    [
+                        f"{tag}: {tags[tag]}"
+                        for tag in tags.keys()
+                        if tag
+                        not in (
+                            "JPEGThumbnail",
+                            "TIFFThumbnail",
+                            "Filename",
+                            "EXIF MakerNote",
+                        )
+                    ]
+                )
+                base_prompt += "\n" + file_text
+            else:
+                pass
+        except requests.RequestException:
+            pass
+        def load_image(file):
+            if isinstance(file, str):
+                response = requests.get(file)
+                response.raise_for_status()
+                return Image.open(BytesIO(response.content))
+            elif isinstance(file, bytes):
+                return Image.open(BytesIO(file))
+            else:
+                return Image.open(file)
+        uploaded_file = image
+        with st.expander("⛆ RGB Channel"):
+            def get_channel_image(image, channels):
+                data = np.array(image)
+                channel_data = np.zeros_like(data)
+                for channel in channels:
+                    channel_data[:, :, channel] = data[:, :, channel]
+                return Image.fromarray(channel_data)
+            channels = st.multiselect(
+                "Select channels:",
+                ["Red", "Green", "Blue"],
+                default=["Red", "Green", "Blue"],
+            )
+            if channels:
+                channel_indices = [
+                    0 if channel == "Red" else 1 if channel == "Green" else 2
+                    for channel in channels
+                ]
+                combined_image = get_channel_image(image, channel_indices)
+                st.image(combined_image, use_column_width=True)
+            else:
+                st.image(image, use_column_width=True)
+        with st.expander("〽 HSV Distribution"):
+            def get_hsv_histogram(image):
+                hsv_image = image.convert("HSV")
+                data = np.array(hsv_image)
+                hue_hist, _ = np.histogram(data[:, :, 0], bins=256, range=(0, 256))
+                saturation_hist, _ = np.histogram(
+                    data[:, :, 1], bins=256, range=(0, 256)
+                )
+                value_hist, _ = np.histogram(data[:, :, 2], bins=256, range=(0, 256))
+                histogram_df = pd.DataFrame(
+                    {
+                        "Hue": hue_hist,
+                        "Saturation": saturation_hist,
+                        "Value": value_hist,
+                    }
+                )
+                return histogram_df
+            hsv_histogram_df = get_hsv_histogram(image)
+            st.line_chart(hsv_histogram_df)
+        with st.expander("☄ Color Distribution"):
+            if image_url:
+                image = load_image(image_url)
+                if image:
+                    def color_distribution_sunburst(data):
+                        data = np.array(data)
+                        red, green, blue = data[:, :, 0], data[:, :, 1], data[:, :, 2]
+                        color_intensity = {"color": [], "intensity": [], "count": []}
+                        for name, channel in zip(
+                            ["Red", "Green", "Blue"], [red, green, blue]
+                        ):
+                            unique, counts = np.unique(channel, return_counts=True)
+                            color_intensity["color"].extend([name] * len(unique))
+                            color_intensity["intensity"].extend(unique)
+                            color_intensity["count"].extend(counts)
+                        df = pd.DataFrame(color_intensity)
+                        fig = px.sunburst(
+                            df,
+                            path=["color", "intensity"],
+                            values="count",
+                            color="color",
+                            color_discrete_map={
+                                "Red": "#ff6666",
+                                "Green": "#85e085",
+                                "Blue": "#6666ff",
+                            },
+                        )
+                        return fig
+                    fig = color_distribution_sunburst(image)
+                    st.plotly_chart(fig, use_container_width=True)
+        with st.expander("🕸 3D Color Space"):
+            def plot_3d_color_space(data, skip_factor):
+                sample = data[::skip_factor, ::skip_factor].reshape(-1, 3)
+                normalized_colors = sample / 255.0
+                trace = go.Scatter3d(
+                    x=sample[:, 0],
+                    y=sample[:, 1],
+                    z=sample[:, 2],
+                    mode="markers",
+                    marker=dict(
+                        size=5,
+                        color=["rgb({},{},{})".format(r, g, b) for r, g, b in sample],
+                        opacity=0.8,
+                    ),
+                )
+                layout = go.Layout(
+                    scene=dict(
+                        xaxis=dict(title="Red"),
+                        yaxis=dict(title="Green"),
+                        zaxis=dict(title="Blue"),
+                        camera=dict(eye=dict(x=1.25, y=1.25, z=1.25)),
+                    ),
+                    margin=dict(l=0, r=0, b=0, t=30),
+                )
+                fig = go.Figure(data=[trace], layout=layout)
+                return fig
+            skip_factor = 8
+            if isinstance(uploaded_file, Image.Image):
+                data = np.array(uploaded_file)
+            else:
+                data = np.array(Image.open(uploaded_file))
+            fig = plot_3d_color_space(data, skip_factor)
+            st.plotly_chart(fig, use_container_width=True, config=config)
+        with st.expander("‏𖦹 Pixel Density Polar"):
+            def pixel_density_polar_plot(image):
+                image_data = np.array(image)
+                hsv_data = mcolors.rgb_to_hsv(image_data / 255.0)
+                hue = hsv_data[:, :, 0].flatten()
+                hist, bins = np.histogram(hue, bins=360, range=(0, 1))
+                theta = np.linspace(0, 360, len(hist), endpoint=False)
+                fig = px.bar_polar(
+                    r=hist,
+                    theta=theta,
+                    template="seaborn",
+                    color_discrete_sequence=["red"],
+                )
+                fig.update_traces(marker=dict(line=dict(color="red", width=1)))
+                fig.update_layout()
+                return fig
+            if uploaded_file is not None:
+                if isinstance(uploaded_file, Image.Image):
+                    image = uploaded_file
+                else:
+                    image = Image.open(uploaded_file)
+                fig = pixel_density_polar_plot(image)
+                st.plotly_chart(fig, use_container_width=True, config=config)
+        with st.expander("ᨒ 3D Surface (Color Intensities)"):
+            def surface_plot_image_intensity(data):
+                intensity = np.mean(data, axis=2)
+                sample_size = int(intensity.shape[0] * 0.35)
+                intensity_sample = intensity[:sample_size, :sample_size]
+                fig = go.Figure(
+                    data=[go.Surface(z=intensity_sample, colorscale="Viridis")]
+                )
+                fig.update_layout(autosize=True)
+                return fig
+            if isinstance(uploaded_file, Image.Image):
+                data = np.array(uploaded_file)
+            else:
+                data = np.array(Image.open(uploaded_file))
+            fig = surface_plot_image_intensity(data)
+            st.plotly_chart(fig, use_container_width=True, config=config)
+        with st.expander("🖌 Color Palette"):
+            def extract_color_palette(image, num_colors=6):
+                image = image.resize((100, 100))
+                result = image.quantize(colors=num_colors)
+                palette = result.getpalette()
+                color_counts = result.getcolors()
+                colors = [palette[i * 3 : (i + 1) * 3] for i in range(num_colors)]
+                counts = [
+                    count
+                    for count, _ in sorted(
+                        color_counts, reverse=True, key=lambda x: x[0]
+                    )
+                ]
+                return colors, counts
+            def plot_color_palette(colors, counts):
+                fig = go.Figure()
+                for i, (color, count) in enumerate(zip(colors, counts)):
+                    hex_color = "#%02x%02x%02x" % tuple(color)
+                    fig.add_trace(
+                        go.Bar(
+                            x=[1],
+                            y=[hex_color],
+                            orientation="h",
+                            marker=dict(color=hex_color),
+                            hoverinfo="text",
+                            hovertext=f"<b>HEX:</b> {hex_color}<br><b>Count:</b> {count}",
+                            name="",
+                        )
+                    )
+                fig.update_layout(
+                    xaxis=dict(showticklabels=False),
+                    yaxis=dict(showticklabels=True),
+                    showlegend=False,
+                    template="plotly_dark",
+                    height=400,
+                )
+                return fig
+            num_colors = st.slider("Number of Colors", 2, 10, 6)
+            if isinstance(uploaded_file, Image.Image):
+                image = uploaded_file.convert("RGB")
+            else:
+                image = Image.open(uploaded_file).convert("RGB")
+            colors, counts = extract_color_palette(image, num_colors)
+            fig = plot_color_palette(colors, counts)
+            st.plotly_chart(fig, use_container_width=True, config=config)
+        if uploaded_file is not None:
+            col1, col2 = st.columns(2)
+            clean_img = clear_exif_data(image)
+            with col1:
+                st.button("🗑 Clear Chat History", on_click=clear_chat_history)
+            with col2:
+                download_image(clean_img)
+        st.session_state.reset_trigger = True
+    if st.session_state.show_expanders:
+        if uploaded_files and not st.session_state["exif_df"].empty:
+            with st.expander("⛆ RGB Channel"):
+                for uploaded_file in uploaded_files:
+                    if uploaded_file.type in ["image/jpeg", "image/png", "image/jpg"]:
+                        def load_image(image_file):
+                            return Image.open(image_file)
+                        image = load_image(uploaded_file)
+                def get_channel_image(image, channels):
+                    data = np.array(image)
+                    channel_data = np.zeros_like(data)
+                    for channel in channels:
+                        channel_data[:, :, channel] = data[:, :, channel]
+                    return Image.fromarray(channel_data)
+                channels = st.multiselect(
+                    "Select channels:",
+                    ["Red", "Green", "Blue"],
+                    default=["Red", "Green", "Blue"],
+                )
+                if channels:
+                    channel_indices = [
+                        0 if channel == "Red" else 1 if channel == "Green" else 2
+                        for channel in channels
+                    ]
+                    combined_image = get_channel_image(image, channel_indices)
+                    st.image(combined_image, use_column_width=True)
+                else:
+                    st.image(image, use_column_width=True)
+            with st.expander("〽 HSV Distribution"):
+                def get_hsv_histogram(image):
+                    hsv_image = image.convert("HSV")
+                    data = np.array(hsv_image)
+                    hue_hist, _ = np.histogram(data[:, :, 0], bins=256, range=(0, 256))
+                    saturation_hist, _ = np.histogram(
+                        data[:, :, 1], bins=256, range=(0, 256)
+                    )
+                    value_hist, _ = np.histogram(
+                        data[:, :, 2], bins=256, range=(0, 256)
+                    )
+                    histogram_df = pd.DataFrame(
+                        {
+                            "Hue": hue_hist,
+                            "Saturation": saturation_hist,
+                            "Value": value_hist,
+                        }
+                    )
+                    return histogram_df
+                hsv_histogram_df = get_hsv_histogram(image)
+                st.line_chart(hsv_histogram_df)
+            with st.expander("☄ Color Distribution"):
+                def color_distribution_sunburst(data):
+                    data = np.array(data)
+                    red, green, blue = data[:, :, 0], data[:, :, 1], data[:, :, 2]
+                    color_intensity = {"color": [], "intensity": [], "count": []}
+                    for name, channel in zip(
+                        ["Red", "Green", "Blue"], [red, green, blue]
+                    ):
+                        unique, counts = np.unique(channel, return_counts=True)
+                        color_intensity["color"].extend([name] * len(unique))
+                        color_intensity["intensity"].extend(unique)
+                        color_intensity["count"].extend(counts)
+                    df = pd.DataFrame(color_intensity)
+                    fig = px.sunburst(
+                        df,
+                        path=["color", "intensity"],
+                        values="count",
+                        color="color",
+                        color_discrete_map={
+                            "Red": "#ff6666",
+                            "Green": "#85e085",
+                            "Blue": "#6666ff",
+                        },
+                    )
+                    return fig
+                image = load_image(uploaded_file)
+                fig = color_distribution_sunburst(image)
+                st.plotly_chart(fig, use_container_width=True, config=config)
+            with st.expander("🕸 3D Color Space"):
+                def plot_3d_color_space(data, skip_factor):
+                    sample = data[::skip_factor, ::skip_factor].reshape(-1, 3)
+                    normalized_colors = sample / 255.0
+                    trace = go.Scatter3d(
+                        x=sample[:, 0],
+                        y=sample[:, 1],
+                        z=sample[:, 2],
+                        mode="markers",
+                        marker=dict(
+                            size=5,
+                            color=[
+                                "rgb({},{},{})".format(r, g, b) for r, g, b in sample
+                            ],
+                            opacity=0.8,
+                        ),
+                    )
+                    layout = go.Layout(
+                        scene=dict(
+                            xaxis=dict(title="Red"),
+                            yaxis=dict(title="Green"),
+                            zaxis=dict(title="Blue"),
+                            camera=dict(eye=dict(x=1.25, y=1.25, z=1.25)),
+                        ),
+                        margin=dict(l=0, r=0, b=0, t=30),
+                    )
+                    fig = go.Figure(data=[trace], layout=layout)
+                    return fig
+                skip_factor = 8
+                data = np.array(Image.open(uploaded_file))
+                fig = plot_3d_color_space(data, skip_factor)
+                st.plotly_chart(fig, use_container_width=True, config=config)
+            with st.expander("𖦹 Pixel Density Polar"):
+                def pixel_density_polar_plot(data):
+                    image_data = np.array(Image.open(data))
+                    hsv_data = mcolors.rgb_to_hsv(image_data / 255.0)
+                    hue = hsv_data[:, :, 0].flatten()
+                    hist, bins = np.histogram(hue, bins=360, range=(0, 1))
+                    theta = np.linspace(0, 360, len(hist), endpoint=False)
+                    fig = px.bar_polar(
+                        r=hist,
+                        theta=theta,
+                        template="seaborn",
+                        color_discrete_sequence=["red"],
+                    )
+                    fig.update_traces(marker=dict(line=dict(color="red", width=1)))
+                    fig.update_layout()
+                    return fig
+                if uploaded_file is not None:
+                    fig = pixel_density_polar_plot(uploaded_file)
+                    st.plotly_chart(fig, use_container_width=True, config=config)
+            with st.expander("ᨒ 3D Surface (Color Intensities)"):
+                def surface_plot_image_intensity(data):
+                    intensity = np.mean(data, axis=2)
+                    sample_size = int(intensity.shape[0] * 0.35)
+                    intensity_sample = intensity[:sample_size, :sample_size]
+                    fig = go.Figure(
+                        data=[go.Surface(z=intensity_sample, colorscale="Viridis")]
+                    )
+                    fig.update_layout(autosize=True)
+                    return fig
+                data = np.array(Image.open(uploaded_file))
+                fig = surface_plot_image_intensity(data)
+                st.plotly_chart(fig, use_container_width=True, config=config)
+            with st.expander("🖌 Color Palette"):
+                def extract_color_palette(image, num_colors=6):
+                    image = image.resize((100, 100))
+                    result = image.quantize(colors=num_colors)
+                    palette = result.getpalette()
+                    color_counts = result.getcolors()
+                    colors = [palette[i * 3 : (i + 1) * 3] for i in range(num_colors)]
+                    counts = [
+                        count
+                        for count, _ in sorted(
+                            color_counts, reverse=True, key=lambda x: x[0]
+                        )
+                    ]
+                    return colors, counts
+                def plot_color_palette(colors, counts):
+                    fig = go.Figure()
+                    for i, (color, count) in enumerate(zip(colors, counts)):
+                        hex_color = "#%02x%02x%02x" % tuple(color)
+                        fig.add_trace(
+                            go.Bar(
+                                x=[1],
+                                y=[hex_color],
+                                orientation="h",
+                                marker=dict(color=hex_color),
+                                hoverinfo="text",
+                                hovertext=f"<b>HEX:</b> {hex_color}<br><b>Count:</b> {count}",
+                                name="",
+                            )
+                        )
+                    fig.update_layout(
+                        xaxis=dict(showticklabels=False),
+                        yaxis=dict(showticklabels=True),
+                        showlegend=False,
+                        template="plotly_dark",
+                        height=400,
+                    )
+                    return fig
+                num_colors = st.slider("Number of Colors", 2, 10, 6)
+                image = Image.open(uploaded_file).convert("RGB")
+                colors, counts = extract_color_palette(image, num_colors)
+                fig = plot_color_palette(colors, counts)
+                st.plotly_chart(fig, use_container_width=True, config=config)
+            st.session_state.reset_trigger = True
+            col1, col2 = st.columns(2)
+            with col1:
+                st.button("🗑 Clear Chat History", on_click=clear_chat_history)
+            with col2:
+                clear = clear_exif_data(image)
+                download_image(clear)
+@st.experimental_dialog("How to use Exifa.net", width=1920)
+def show_video(item):
+    video_url = "https://www.youtube.com/watch?v=CS7rkWu7LNY"
+    st.video(video_url, loop=False, autoplay=True, muted=False)
+for message in st.session_state.messages:
+    with st.chat_message(message["role"], avatar=icons[message["role"]]):
+        st.write(message["content"])
+        if message == st.session_state["messages"][0]:
+            if st.button("How can I use Exifa?"):
+                show_video("")
+st.sidebar.caption(
+    "Built by [Sahir Maharaj](https://www.linkedin.com/in/sahir-maharaj/). Like this? [Hire me!](https://topmate.io/sahirmaharaj/362667)"
+)
+linkedin = "https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/linkedin.gif"
+topmate = "https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/topmate.gif"
+email = "https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/email.gif"
+newsletter = (
+    "https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/newsletter.gif"
+)
+share = "https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/share.gif"
+uptime = "https://uptime.betterstack.com/status-badges/v1/monitor/196o6.svg"
+st.sidebar.caption(
+    f"""
+        <div style='display: flex; align-items: center;'>
+            <a href = 'https://www.linkedin.com/in/sahir-maharaj/'><img src='{linkedin}' style='width: 35px; height: 35px; margin-right: 25px;'></a>
+            <a href = 'https://topmate.io/sahirmaharaj/362667'><img src='{topmate}' style='width: 32px; height: 32px; margin-right: 25px;'></a>
+            <a href = 'mailto:sahir@sahirmaharaj.com'><img src='{email}' style='width: 28px; height: 28px; margin-right: 25px;'></a>
+            <a href = 'https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7163516439096733696'><img src='{newsletter}' style='width: 28px; height: 28px; margin-right: 25px;'></a>
+            <a href = 'https://www.kaggle.com/sahirmaharajj'><img src='{share}' style='width: 28px; height: 28px; margin-right: 25px;'></a>
+        </div>
+        <br>
+        <a href = 'https://exifa.betteruptime.com/'><img src='{uptime}'></a>
+        &nbsp; <a href="https://www.producthunt.com/posts/exifa-net?embed=true&utm_source=badge-featured&utm_medium=badge&utm_souce=badge-exifa&#0045;net" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=474560&theme=dark" alt="Exifa&#0046;net - Your&#0032;AI&#0032;assistant&#0032;for&#0032;understanding&#0032;EXIF&#0032;data | Product Hunt" style="width: 125px; height: 27px;" width="125" height="27" /></a>
+        """,
+    unsafe_allow_html=True,
+)
+@st.cache_resource(show_spinner=False)
+def get_tokenizer():
+    return AutoTokenizer.from_pretrained("huggyllama/llama-7b")
+def get_num_tokens(prompt):
+    tokenizer = get_tokenizer()
+    tokens = tokenizer.tokenize(prompt)
+    return len(tokens)
+def generate_arctic_response_follow_up():
+    follow_up_response = ""
+    last_three_messages = st.session_state.messages[-3:]
+    for message in last_three_messages:
+        follow_up_response += "\n\n {}".format(message)
+    prompt = [
+        "Please generate one question based on the conversation thus far that the user might ask next. Ensure the question is short, less than 8 words, stays on the topic of EXIF and its importance and dangers, and is formatted with underscores instead of spaces, e.g., What_does_EXIF_mean? Conversation Info = {}. Please generate one question based on the conversation thus far that the user might ask next. Ensure the question is short, less than 8 words, stays on the topic of EXIF and its importance and dangers, and is formatted with underscores instead of spaces".format(
+            follow_up_response
+        )
+    ]
+    prompt.append("assistant\n")
+    prompt_str = "\n".join(prompt)
+    full_response = []
+    for event in replicate.stream(
+        "snowflake/snowflake-arctic-instruct",
+        input={
+            "prompt": prompt_str,
+            "prompt_template": r"{prompt}",
+            "temperature": temperature,
+            "top_p": top_p,
+            "max_new_tokens": max_new_tokens,
+            "min_new_tokens": min_new_tokens,
+            "presence_penalty": presence_penalty,
+            "frequency_penalty": frequency_penalty,
+            "stop_sequences": stop_sequences,
+        },
+    ):
+        full_response.append(str(event).strip())
+    complete_response = "".join(full_response)
+    return complete_response
+def generate_arctic_response():
+    prompt = [base_prompt] if base_prompt else []
+    for dict_message in st.session_state.messages:
+        if dict_message["role"] == "user":
+            prompt.append("user\n" + dict_message["content"])
+        else:
+            prompt.append("assistant\n" + dict_message["content"])
+    prompt.append("assistant\n")
+    prompt_str = "\n".join(prompt)
+    if get_num_tokens(prompt_str) >= 1000000:
+        st.error("Conversation length too long. Please keep it under 1000000 tokens.")
+        st.button(
+            "🗑 Clear Chat History",
+            on_click=clear_chat_history,
+            key="clear_chat_history",
+        )
+        st.stop()
+    for event in replicate.stream(
+        "snowflake/snowflake-arctic-instruct",
+        input={
+            "prompt": prompt_str,
+            "prompt_template": r"{prompt}",
+            "temperature": temperature,
+            "top_p": top_p,
+            "max_new_tokens": max_new_tokens,
+            "min_new_tokens": min_new_tokens,
+            "presence_penalty": presence_penalty,
+            "frequency_penalty": frequency_penalty,
+            "stop_sequences": stop_sequences,
+        },
+    ):
+        yield str(event)
+def display_question():
+    st.session_state.follow_up = True
+if prompt := st.chat_input(disabled=not replicate_api):
+    st.session_state.show_animation = False
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message(
+        "user",
+        avatar="https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/user.gif",
+    ):
+        st.write(prompt)
+if st.session_state.follow_up:
+    st.session_state.show_animation = False
+    unique_key = "chat_input_" + str(hash("Snowflake Arctic is cool"))
+    complete_question = generate_arctic_response_follow_up()
+    formatted_question = complete_question.replace("_", " ").strip()
+    st.session_state.messages.append({"role": "user", "content": formatted_question})
+    with st.chat_message(
+        "user",
+        avatar="https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/user.gif",
+    ):
+        st.write(formatted_question)
+    st.session_state.follow_up = False
+    with st.chat_message(
+        "assistant",
+        avatar="https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/assistant.gif",
+    ):
+        response = generate_arctic_response()
+        full_response = st.write_stream(response)
+        message = {"role": "assistant", "content": full_response}
+        st.session_state.messages.append(message)
+        full_response_prompt = generate_arctic_response_follow_up()
+        message_prompt = {"content": full_response_prompt}
+        st.button(
+            str(message_prompt["content"]).replace("_", " ").strip(),
+            on_click=display_question,
+        )
+if st.session_state.messages[-1]["role"] != "assistant":
+    st.session_state.show_animation = False
+    with st.chat_message(
+        "assistant",
+        avatar="https://raw.githubusercontent.com/sahirmaharaj/exifa/main/img/assistant.gif",
+    ):
+        response = generate_arctic_response()
+        full_response = st.write_stream(response)
+        message = {"role": "assistant", "content": full_response}
+        full_response_prompt = generate_arctic_response_follow_up()
+        message_prompt = {"content": full_response_prompt}
+        st.button(
+            str(message_prompt["content"]).replace("_", " ").strip(),
+            on_click=display_question,
+        )
+        st.session_state.messages.append(message)
+if st.session_state.reset_trigger:
+    unique_key = "chat_input_" + str(hash("Snowflake Arctic is cool"))
+    complete_question = generate_arctic_response_follow_up()
+    st.session_state.show_animation = False
+if "has_snowed" not in st.session_state:
+    st.snow()
+    st.session_state["has_snowed"] = True
+if st.session_state.show_animation:
+    components.html(particles_js, height=370, scrolling=False)