diff --git a/main.py b/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c44ed9dbb41a4c6b3d95b41f8013703c4af6e48
--- /dev/null
+++ b/main.py
@@ -0,0 +1,76 @@
+import plantvision
+import requests
+from io import BytesIO
+import pickle as pkl
+from flask import Flask, render_template, request, session, jsonify, url_for
+from PIL import Image
+import os
+import time
+import random
+from pathlib import Path
+THIS_FOLDER = Path(__file__).parent.resolve()
+
+app = Flask(__name__)
+app.secret_key = 'pi-33pp-co-sk-33'
+app.template_folder = os.path.abspath(f'{THIS_FOLDER}/web/templates')
+app.static_folder = os.path.abspath(f'{THIS_FOLDER}/web/static')
+print(app.static_folder)
+
+flowerLayers = None
+leafLayers = None
+fruitLayers = None
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/guess', methods=['POST'])
+def guess():
+    global flowerLayers, leafLayers, fruitLayers
+
+    if request.method == 'POST':
+        print('Thinking...')
+        
+        img = request.files.get('uploaded-image')
+        feature = request.form.get('feature')
+        
+        tensor = plantvision.processImage(img, feature)
+        predictions = plantvision.see(tensor, feature, 6)
+
+        with open(f'{THIS_FOLDER}/resources/speciesNameToKey.pkl','rb') as f:
+            speciesNameToKey = pkl.load(f)
+        with open(f'{THIS_FOLDER}/resources/speciesNameToVernacular.pkl','rb') as f:
+            speciesNameToVernacular = pkl.load(f)
+        with open(f'{THIS_FOLDER}/resources/{feature}speciesIndexDict.pkl','rb') as f:
+            speciesNameToIndex = pkl.load(f)
+
+        urls = []
+        predicted_image_urls = []
+        for p in predictions:
+            key = speciesNameToKey[p]
+            img = speciesNameToIndex[p]
+            query = ''
+            for i in p.split(' '):
+                query += i 
+                query += '+'
+            urls.append(f'https://www.google.com/search?q={query[:-1]}')
+            predicted_image_urls.append(f"https://storage.googleapis.com/bmllc-images-bucket/images/img{img}.jpeg")
+
+        names = []
+        for p in predictions:
+            try:
+                names.append(speciesNameToVernacular[p])
+            except:
+                names.append(p)
+
+        response = {
+            'names': names,
+            'species': predictions,
+            'predictions': urls,
+            'images': predicted_image_urls
+        }
+
+        return jsonify(response)
+
+if __name__ == '__main__':
+    app.run(port=int(os.environ.get("PORT", 8080)),host='0.0.0.0',debug=True)
diff --git a/plantvision.py b/plantvision.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fdb5cf27a6793a9d4177f6c8c20bce1bec2b8a2
--- /dev/null
+++ b/plantvision.py
@@ -0,0 +1,115 @@
+import requests
+from io import BytesIO
+from PIL import Image, ImageOps
+import torchvision.transforms as T
+import torch
+import gc
+import pickle as pkl
+from pathlib import Path
+THIS_FOLDER = Path(__file__).parent.resolve()
+import datetime as dt
+from transformers import AutoModel
+import torch.nn as nn
+import torch.nn.functional as F
+import logging
+logging.disable(logging.INFO)
+logging.disable(logging.WARNING)
+
+visionTransformer = AutoModel.from_pretrained(r"google/vit-base-patch16-224-in21k")
+
+class PlantVision(nn.Module):
+    def __init__(self, num_classes):
+        super(PlantVision, self).__init__()
+        self.vit = visionTransformer
+        count = 0
+        for child in self.vit.children():
+            count += 1
+            if count < 4:
+                for param in child.parameters():
+                    param.requires_grad = False
+        self.vitLayers = list(self.vit.children()) 
+        self.vitTop = nn.Sequential(*self.vitLayers[:-2])
+        self.vitNorm = list(self.vit.children())[2]
+        self.vit = None
+        gc.collect()
+        self.vitFlatten = nn.Flatten()
+        self.vitLinear = nn.Linear(151296,num_classes)
+        self.fc = nn.Linear(num_classes, num_classes)
+
+    def forward(self, input):
+        output = self.vitTop(input).last_hidden_state
+        output = self.vitNorm(output)
+        output = self.vitFlatten(output)
+        output = F.relu(self.vitLinear(output))
+        output = self.fc(output)
+        return output
+
+device = 'cpu' # ('cuda' if torch.cuda.is_available else 'cpu')
+
+with open(fr'{THIS_FOLDER}/resources/flowerLabelSet.pkl', 'rb') as f:
+        flowerLabelSet = pkl.load(f)
+
+with open(fr'{THIS_FOLDER}/resources/leafLabelSet.pkl', 'rb') as f:
+        leafLabelSet = pkl.load(f)
+
+with open(fr'{THIS_FOLDER}/resources/fruitLabelSet.pkl', 'rb') as f:
+        fruitLabelSet = pkl.load(f)
+
+def loadModel(feature, labelSet):
+    model = PlantVision(num_classes=len(labelSet))
+    model.vitFlatten.load_state_dict(torch.load(BytesIO(requests.get(f"https://storage.googleapis.com/bmllc-plant-model-bucket/{feature}-vitFlatten-weights.pt").content), map_location=torch.device(device)), strict=False)
+    model.vitLinear.load_state_dict(torch.load(BytesIO(requests.get(f"https://storage.googleapis.com/bmllc-plant-model-bucket/{feature}-vitLinear-weights.pt").content), map_location=torch.device(device)), strict=False)
+    model.fc.load_state_dict(torch.load(BytesIO(requests.get(f"https://storage.googleapis.com/bmllc-plant-model-bucket/{feature}-fc-weights.pt").content), map_location=torch.device(device)), strict=False)
+    model = model.half()
+    return model
+
+start = dt.datetime.now()
+flower = loadModel('flower',flowerLabelSet)
+leaf = loadModel('leaf',leafLabelSet)
+fruit = loadModel('fruit',fruitLabelSet)
+print(dt.datetime.now() - start)
+
+def processImage(imagePath, feature):
+    with open(fr'{THIS_FOLDER}/resources/{feature}MeansAndStds.pkl', 'rb') as f:
+        meansAndStds = pkl.load(f)
+
+    img = Image.open(imagePath).convert('RGB')
+    cropped = ImageOps.fit(img, (224,224), Image.Resampling.LANCZOS)
+
+    process = T.Compose([
+        T.CenterCrop(224),
+        T.ToTensor(),
+        T.ConvertImageDtype(torch.float32),
+        T.Normalize(
+       mean=meansAndStds['mean'],
+       std=meansAndStds['std'])
+    ])
+
+    return process(cropped)
+
+def see(tensor,feature,k):
+
+        if feature=='flower':
+                model = flower.float()
+                labelSet = flowerLabelSet
+        
+        elif feature=='leaf':
+                model = leaf.float()
+                labelSet = leafLabelSet
+        
+        elif feature=='fruit':
+                model = fruit.float()
+                labelSet = fruitLabelSet
+        
+        with torch.no_grad():        
+                output = model(tensor.unsqueeze(0))
+                top = torch.topk(output,k,dim=1)
+                predictions = top.indices[0]
+        
+        predictedSpecies = []
+        for i in predictions:
+                predictedSpecies.append(labelSet[i])
+        
+        model = None
+        gc.collect()
+        return predictedSpecies
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a76ca1fdc08b828d13bce174e53add6fb312e6d8
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,11 @@
+Flask==2.1.0
+gunicorn==20.1.0
+torch
+torchvision
+pillow
+tqdm
+argparse
+pathlib
+transformers
+fastapi
+uvicorn
\ No newline at end of file
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diff --git a/web/static/predicted-images/img18.jpeg b/web/static/predicted-images/img18.jpeg
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diff --git a/web/static/predicted-images/img19.jpeg b/web/static/predicted-images/img19.jpeg
new file mode 100644
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diff --git a/web/static/predicted-images/img2.jpeg b/web/static/predicted-images/img2.jpeg
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diff --git a/web/static/predicted-images/img3.jpeg b/web/static/predicted-images/img3.jpeg
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diff --git a/web/static/predicted-images/img4.jpeg b/web/static/predicted-images/img4.jpeg
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diff --git a/web/static/predicted-images/img5.jpeg b/web/static/predicted-images/img5.jpeg
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diff --git a/web/static/predicted-images/img6.jpeg b/web/static/predicted-images/img6.jpeg
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diff --git a/web/static/predicted-images/img7.jpeg b/web/static/predicted-images/img7.jpeg
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diff --git a/web/static/predicted-images/img8.jpeg b/web/static/predicted-images/img8.jpeg
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diff --git a/web/static/predicted-images/img9.jpeg b/web/static/predicted-images/img9.jpeg
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diff --git a/web/static/script.js b/web/static/script.js
new file mode 100644
index 0000000000000000000000000000000000000000..114d5c29c694cafb4eb4ad07e7be89e64e553d94
--- /dev/null
+++ b/web/static/script.js
@@ -0,0 +1,174 @@
+document.addEventListener("DOMContentLoaded", () => {
+  let lastUploadedImage = null;
+  const dropzone = document.getElementById("image-dropzone");
+  const featureSelect = document.getElementById("feature-select");
+  const thinkingText = document.getElementById("thinking-text");
+
+  function handleImageUpload(file) {
+    const predictedImagesContainer = document.getElementById("predicted-images");
+    predictedImagesContainer.innerHTML = "";
+
+    const imageElement = document.createElement("img");
+    imageElement.src = "static/loading.gif";
+    predictedImagesContainer.appendChild(imageElement);
+
+    const selectedFeature = featureSelect.value;
+    const formData = new FormData();
+    thinkingText.innerText = "Loading the model, it might take a second...";
+    formData.append("uploaded-image", file);
+    formData.append("feature", selectedFeature);
+    lastUploadedImage = file;
+    fetch("/guess", {
+      method: "POST",
+      body: formData,
+    })
+      .then((response) => response.json())
+      .then((data) => {
+        console.log(data);
+        displayResults(data);
+      })
+      .catch((error) => {
+        console.error("Error uploading image:", error);
+      });
+
+    const reader = new FileReader();
+    reader.onloadend = () => {
+      const uploadedImage = new Image();
+
+      uploadedImage.src = reader.result;
+      uploadedImage.classList.add("uploaded-image");
+      uploadedImage.onload = async () => {
+
+        const dropzoneWidth = dropzone.offsetWidth;
+        const dropzoneHeight = dropzone.offsetHeight;
+        const imageWidth = uploadedImage.width;
+        const imageHeight = uploadedImage.height;
+        const widthScale = dropzoneWidth / imageWidth;
+        const heightScale = dropzoneHeight / imageHeight;
+        const scaleFactor = Math.min(widthScale, heightScale);
+        uploadedImage.width = imageWidth * (scaleFactor-0.1);
+        uploadedImage.height = imageHeight * (scaleFactor-0.1);
+        dropzone.innerHTML = "";
+        dropzone.appendChild(uploadedImage);
+      };
+    };
+
+    if (file) {
+      reader.readAsDataURL(file);
+    }
+  }
+
+
+dropzone.addEventListener("click", () => {
+    const fileInput = document.createElement("input");
+    fileInput.type = "file";
+    fileInput.accept = "image/*";
+    fileInput.onchange = (e) => {
+      const file = e.target.files[0];
+      handleImageUpload(file);
+    };
+    fileInput.click();
+});
+
+
+dropzone.addEventListener("dragover", (e) => {
+    e.preventDefault();
+    dropzone.classList.add("highlight");
+});
+
+
+dropzone.addEventListener("dragleave", () => {
+    dropzone.classList.remove("highlight");
+});
+
+
+dropzone.addEventListener("drop", (e) => {
+    e.preventDefault();
+    dropzone.classList.remove("highlight");
+
+    const file = e.dataTransfer.files[0];
+    handleImageUpload(file);
+});
+
+featureSelect.addEventListener("change", () => {
+  if (lastUploadedImage) {
+    handleImageUpload(lastUploadedImage);
+  }
+});
+
+function displayResults(data) {
+  thinkingText.innerText = "Looks like...";
+  const predictedImagesContainer = document.getElementById("predicted-images");
+  predictedImagesContainer.innerHTML = ""; // Clear previous images
+
+  for (let i = 0; i < data.images.length; i++) {
+    const imageUrl = data.images[i];
+    const predictionUrl = data.predictions[i];
+    const name = data.names[i];
+    const species = data.species[i];
+
+    // Create a container div for each image and its anchor
+    const imageContainer = document.createElement("div");
+
+    // Create the anchor element
+    const anchorElement = document.createElement("a");
+    anchorElement.href = predictionUrl;
+    anchorElement.target = "_blank"; // Open the link in a new tab
+
+    // Create the caption element
+    const captionElement = document.createElement("div");
+    captionElement.classList.add("image-caption");
+    captionElement.textContent = name; // Use the name from the 'names' list as the caption text
+
+    // Create the caption element
+    const speciesElement = document.createElement("div");
+    speciesElement.classList.add("image-species");
+    speciesElement.textContent = species; // Use the name from the 'names' list as the caption text
+
+    // Create the image element
+    const imageElement = document.createElement("img");
+    // Add cache-busting parameter to the image URL
+    const cacheBustUrl = imageUrl + `?cache=${Date.now()}`;
+    // Set the lazy loading attribute
+    imageElement.loading = "lazy";
+    // Set the data-src attribute with the cache-busted URL
+    imageElement.src = cacheBustUrl;
+
+    const tooltip = document.createElement("div");
+    tooltip.classList.add("image-tooltip");
+    tooltip.textContent = species; // Use the name from the 'names' list as the tooltip text
+
+    // Append the image to the anchor and the anchor to the container
+    anchorElement.appendChild(imageElement);
+    anchorElement.appendChild(captionElement);
+    imageContainer.appendChild(tooltip);
+    imageContainer.appendChild(anchorElement);
+    captionElement.style.opacity = "0";
+    captionElement.style.opacity = "1";
+    
+
+    // Append the container to the predictedImagesContainer
+    predictedImagesContainer.appendChild(imageContainer);
+    
+    // Add the tooltip element to the document body
+    document.body.appendChild(tooltip);
+
+    // Add event listeners to handle tooltip visibility
+    imageContainer.addEventListener("mouseover", () => {
+      tooltip.style.opacity = "1";
+    });
+
+    imageContainer.addEventListener("mouseout", () => {
+      tooltip.style.opacity = "0";
+    });
+
+    // Position the tooltip dynamically based on cursor movement
+    imageContainer.addEventListener("mousemove", (event) => {
+      tooltip.style.left = event.pageX + "px";
+      tooltip.style.top = event.pageY + "px";
+    });
+  }
+}
+
+
+});
diff --git a/web/static/styles.css b/web/static/styles.css
new file mode 100644
index 0000000000000000000000000000000000000000..8f0dd4a270c404b1a8f07b5f62a6a8076283a13c
--- /dev/null
+++ b/web/static/styles.css
@@ -0,0 +1,167 @@
+body {
+  font-family: 'Helvetica';
+  margin-top: 100px;
+  margin-left: 100px;
+  margin-right: 100px;
+  padding: 0;
+}
+
+.header {
+  margin-top: 40px;
+  margin-bottom: 20px;
+  background-color: #ffffff;
+  color: #000000;
+  position: absolute;
+  text-align: center;
+  top: 40px;
+  left: 50%;
+  transform: translateX(-50%);
+}
+
+.container {
+  margin-top: 120px;
+  height: auto;
+  top: 100px;
+  display: flex;
+  justify-content: space-around;
+  padding: 20px;
+  position: relative;
+  align-items: stretch;
+}
+
+.left-container {
+  width: 52%;
+  padding: 20px;
+  text-align: center;
+  align-items: center;
+  align-content: center;
+}
+
+.right-container {
+  margin-left:-2px;
+  width: 48%;
+  padding: 20px;
+  border: 2px solid #000000;
+  text-align: left;
+}
+
+.dropzone {
+  width: 52%;
+  min-height: 500px;
+  border: 2px solid #000000;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  cursor: pointer;
+}
+
+#uploaded-image {
+  max-width: 100%;
+  max-height: 200px;
+  display: none;
+  align-items: center;
+}
+
+.github-container {
+  display: flex;
+  justify-content: center;
+  margin-top: 100px;
+}
+
+.github-container a {
+  display: flex;
+  color:#000000;
+  align-items: center;
+}
+
+.feature-select {
+  display: flex;
+  justify-content: center;
+  margin-top: 10px;
+}
+
+/* The container for the predicted images */
+#predicted-images {
+  display: grid;
+  row-gap: 1%;
+  column-gap: 1%;
+  width: 100%;
+  grid-template-columns: repeat(3, 1fr);
+  grid-template-rows: repeat(3, fr);
+  position: relative; /* Set the container as a relative positioning context */
+}
+
+/* The predicted image */
+#predicted-images img {
+  width: 100%; 
+  height: auto;
+  display: block; 
+  align-items: center;
+}
+
+/* The tooltip */
+.image-tooltip {
+  position: absolute;
+  bottom: 100%; /* Position the tooltip above the image */
+  left: 50%; /* Center the tooltip horizontally */
+  transform: translate(0%, -60%); /* Center both horizontally and vertically */
+  background-color: rgba(0, 0, 0, 0.6);
+  padding-left: 10px;
+  padding-right: 10px;
+  padding-top: 10px;
+  padding-bottom: 20px;
+  color: white;
+  font-size: 12px;
+  pointer-events: none;
+  opacity: 0; /* Initially set the tooltip to be transparent */
+}
+
+/* Apply the hover effect */
+#predicted-images img {
+  /* Set initial transition properties */
+  transition: all 0.3s ease-in-out;
+  transform: scale(1); /* Set the initial scale to 1 (normal size) */
+}
+
+#predicted-images img:hover {
+  filter: brightness(70%);
+  transform: scale(1.07); /* Scale the image up to 1.1 times its original size on hover */
+}
+
+/* Show the tooltip on image hover */
+#predicted-images .image-container:hover .image-tooltip {
+opacity: 1; /* Make the tooltip visible on image hover */
+visibility: visible; /* Show the tooltip on image hover */
+}
+
+/* The caption */
+.image-caption {
+  bottom: 0;
+  left: 0;
+  width: auto;
+  gap: 0%;
+  padding: 10px;
+  align-items: center;
+  text-decoration: none; 
+  font-size: 14px;
+  text-align: center;
+  font-weight: normal;
+  pointer-events: none; /* Prevent caption from blocking clicks */
+}
+
+.image-species {
+  opacity: 0;
+}
+
+#predicted-images a {
+  color: #4e4e4e;
+  text-decoration: none; 
+  font-weight: normal;
+}
+
+#predicted-images > div > a:hover .image-caption{
+  color: #000000;
+  font-size:  14px;
+  transition: 0.5s;
+}
+
diff --git a/web/templates/index.html b/web/templates/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..9edcbb7ac740e5d0d142dddf4e2a495c0429436a
--- /dev/null
+++ b/web/templates/index.html
@@ -0,0 +1,52 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <title>What Plant Is This?</title>
+  <link rel="stylesheet" href="static/styles.css">
+  <link rel="icon" type="image/png" href="https://images.squarespace-cdn.com/content/v1/64790f5777b5d772678cce83/6d71eaee-f825-4324-be9b-2def32469eac/Untitled+drawing+%2811%29.png?format=100w">
+</head>
+<body>
+  <div class="header">
+    <h1>What Plant is This?</h1>
+    <p> Have a neural net try to identify the plant you found.</p>
+
+    <div class="feature-select">
+    <label for="feature">This is a picture of a...</label>
+    <select name="feature" id="feature-select">
+      <option value="flower">flower</option>
+      <option value="leaf">leaf</option>
+      <option value="fruit">fruit</option>
+    </select>
+  </div>
+
+  </div>
+
+  <div class="container">
+    <div id="image-dropzone" class="dropzone">
+      <p>Drop an image</p>
+    </div>
+    <img id="uploaded-image" src="#" alt="Uploaded Image" />
+    <div class="right-container">
+      <h2 id="thinking-text">Suggestions will appear here...</h2>
+      <div class="image-grid">
+        <div id="predicted-images">
+        </div>
+      </div>
+    </div>
+  </div>
+
+  <!-- Add the GitHub link and icon below the container -->
+  <div class="github-container">
+    <a href="https://github.com/brayden1moore/What-Plant-Is-This" target="_blank">
+      <img src="https://upload.wikimedia.org/wikipedia/commons/9/91/Octicons-mark-github.svg" height=20px />
+      <p> See the code</p>
+    </a>
+  </div>
+
+  <div class="spacing">
+    <p><br> <br> <br> </p>
+  </div>
+  <script src="https://cdn.jsdelivr.net/gh/alexcorvi/heic2any/dist/heic2any.js"></script>
+  <script src="static/script.js"></script>
+</body>
+</html>