1st version of the app

app.py

@@ -1,4 +1,188 @@
+#importing the libraries
 import streamlit as st
+from PIL import Image
+import torch
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+import numpy as np
+import pandas as pd
+import time
+import os
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+model_repository_id = "Dusduo/Pokemon-classification-1stGen"
+# Loading the pokemon classifier model and its processor
+image_processor = AutoImageProcessor.from_pretrained(model_repository_id)
+model = AutoModelForImageClassification.from_pretrained(model_repository_id)
+# Loading the pokemon information table
+pokemon_info_df = pd.read_csv('pokemon_info.csv')
+
+pokeball_image = Image.open('pokeball.png').resize((20,20))
+
+#functions to predict image
+def preprocess(processor: AutoImageProcessor, image):
+    return processor(image.convert("RGB").resize((200,200)), return_tensors="pt")
+
+def predict(model: AutoModelForImageClassification, inputs, k=5):
+    
+    # Forward the image to the model and retrieve the logits 
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    
+    # Convert the retrieved logits into a vector of probabilities for each class
+    probabilities = torch.softmax(logits[0], dim=0).tolist()
+    
+    # Discriminate wether or not the inputted image was an image of a Pokemon
+    # Compute the variance of the vector of probabilities 
+        # The spread of the probability values is a good represent of the confusion of the model
+        # Or in other words, its confidence => the greater the spread, the lower its confidence 
+    variance = np.var(probabilities)
+    
+    # Too great of a spread: it is likely the image provided did not correspond to any known classes
+    if variance < 0.001: #not a pokemon
+       predicted_label = 'not a pokemon' 
+       probability = -1
+       (top_k_labels, top_k_probability) = '_', '_'
+    else: # it is a pokemon
+        # Retrieve the predicted class (pokemon)
+        predicted_id = logits.argmax(-1).item()
+        predicted_label = model.config.id2label[predicted_id]
+        # Retrieve the probability for the predicted class, and format it to 2 decimals
+        probability = round(probabilities[predicted_id]*100,2)
+        # Retrieve the top 5 classes and their probabilities
+        #top_k_labels = [model.config.id2label[key] for key in np.argpartition(logits.numpy(), -k)[-k:]]
+        #top_k_probability = [round(prob*100,2) for prob in np.sort(probabilities.numpy())[-k:]]
+        
+    return predicted_label, probability #, (top_k_labels, top_k_probability)
+
+
+
+# Designing the interface ------------------------------------------
+
+# Use the full page instead of a narrow central column
+st.set_page_config(layout="wide")
+
+# Define the title
+st.title("Gotta Classify 'Em All - 1st Generation Pokedex -")
+# For newline
+st.write('\n')
+
+image = Image.open('anime1.jpeg')
+
+col1, col2 = st.columns([3,1]) # [3,1]
+
+with col1:
+    image = Image.open('anime1.jpeg')
+    show = st.image(image, use_column_width=True)
+
+
+
+# Display Sample images  ----
+st.subheader('Sample images')
+
+sample_imgs_dir = "sample_imgs/"
+sample_imgs = os.listdir(sample_imgs_dir) # get the list of all sample images
+img_idx = 0
+
+n_cols = 4
+groups = []
+for i in range(0, len(sample_imgs), n_cols):
+    groups.append(sample_imgs[i:i+n_cols])
+
+for group in groups: 
+    cols = st.columns(n_cols)
+    for i,image_file in enumerate(group):
+        cols[i].image(sample_imgs_dir+image_file)
+    
+
+
+# Sidebar work and model outputs ---------------
+
+st.sidebar.title("Upload Image")
+
+#Disabling warning
+#st.set_option('deprecation.showfileUploaderEncoding', False)
+#Choose your own image
+uploaded_file = st.sidebar.file_uploader("",type=['png', 'jpg', 'jpeg'], accept_multiple_files=False )
+
+if uploaded_file is not None:
+    
+    u_img = Image.open(uploaded_file)
+    show.image(u_img, 'Uploaded Image', width=400 )#use_column_width=True)
+    
+    # Preprocess the image for the model
+    model_inputs = preprocess(image_processor, u_img)
+    
+# For newline
+st.sidebar.write('\n')
+    
+if st.sidebar.button("Click Here to Classify"):
+    
+    if uploaded_file is None:
+        
+        st.sidebar.write("Please upload an Image to Classify")
+    
+    else:
+        
+        with st.spinner('Classifying ...'):
+            # Get prediction
+            prediction, probability = predict(model, model_inputs,5) #, (top_k_labels, top_k_probability)
+            time.sleep(2)
+            st.sidebar.success('Done!')
+            
+        st.sidebar.header("Model predicts: ")
+        
+        # Display prediction
+        
+        if probability==-1:
+            
+            st.sidebar.write("It seems like it is not a picture of a 1st Generation Pokemon alone.", '\n', 
+                             "There might be too many entities on the image." )
+                             
+        else:
+            st.sidebar.write(f" It's a(n) {prediction} picture.",'\n')
+            
+            st.sidebar.write('Probability:',probability,'%')
+            
+            # Retrieve predicted pokemon information
+            _, pokedex_number, english_name, romaji_name, katakana_name, weight_kg, height_m, type1, type2, color1, color2, classification, evolve_from, evolve_into, is_legendary = pokemon_info_df[pokemon_info_df['name']==prediction].values[0]
+            with col2:
+                # pokedex box 
+                with st.container(border=True ):
+                    # first row
+                    with st.container():
+                        pokeball_image_col,pokedex_number_col, pokemon_name_col = st.columns([1,1,8])
+                        pokeball_image_col.image(pokeball_image)
+                        pokedex_number_col.markdown(f'<div style="text-align: left; font-size: 1.4rem;"><b>{pokedex_number}</b></div>', unsafe_allow_html=True)
+                        pokemon_name_col.markdown(f'<div style="text-align: right; font-size: 1.4rem;"><b>{english_name}</b></div>', unsafe_allow_html=True)
+                        
+                    # second row
+                    with st.container():
+                        st.markdown(f'<div style="text-align: center; color: {color1}; font-size: 1.2rem;"><b>{classification}</b></div>', unsafe_allow_html=True)
+                        
+                    # 3rd row
+                    with st.container():
+                        if pd.isna(type2):
+                            st.write('\n')
+                            st.markdown(f'<div style="display: flex; justify-content: center; align-items: center; "><div style="display: inline-block; padding: 5px; margin: 0 5px; border-radius: 5px; background-color: {color1}; color: white;">{type1}</div>', unsafe_allow_html=True)
+                        else: 
+                            type1_col, type2_col = st.columns(2)
+                            type1_col.markdown(f'<div style="display: flex; justify-content: center; align-items: center;"><div style="display: inline-block; padding: 5px; margin: 0 5px; border-radius: 5px; background-color: {color1}; color: white;">{type1}</div>', unsafe_allow_html=True)
+                            type2_col.markdown(f'<div style="display: flex; justify-content: center; align-items: center;"><div style="display: inline-block; padding: 5px; margin: 0 5px; border-radius: 5px; background-color: {color2}; color: white;">{type2}</div>', unsafe_allow_html=True) 
+                        st.write('\n')
+                    # 4th row
+                    with st.container():
+                        st.write(f'<div style=font-size: 1.4rem;><b>Height:</b> {height_m}m', unsafe_allow_html=True)
+                        st.write('\n')
+                        st.write(f'<div style=font-size: 1.4rem;><b>Weight:</b> {weight_kg}kg', unsafe_allow_html=True)
+                        st.write('\n')
+                        if not pd.isna(evolve_from):
+                            st.markdown(f'<div style=font-size: 1.4rem;><b>Evolves from:</b> {evolve_from}', unsafe_allow_html=True)
+                            #st.write(f'Evolves from: {evolve_from}')
+                            st.write('\n')
+                        if not pd.isna(evolve_into):
+                            st.markdown(f'<div style=font-size: 1.4rem;><b>Evolves into:</b> {evolve_into}', unsafe_allow_html=True)
+                            #st.write(f'Evolves into: {evolve_into}')
+                            st.write('\n')
+                    
+    
+    
+