uploading everything

Pipfile

@@ -0,0 +1,12 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+scikit-learn = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.10"

Pipfile.lock

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+{
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+        },
+        "sources": [
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+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
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+    "develop": {}
+}

face_feature_vecs.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ae9b94b41a9e81924a41eb58a349c19d6f30359b95a07d8c9a177ee323f51bc
+size 303673119

face_labels.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:07ea5e42025c67c4f2ef00ad7c46d5b0f0e4485a8d739d7a8f5da87b74d340c8
+size 379928

featurizer.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import os
+from os import path
+import matplotlib.pyplot as plt
+from itertools import cycle
+from sklearn.neighbors import NearestNeighbors
+
+feature_vecs = pd.read_csv("face_feature_vecs.csv")
+feature_vecs = feature_vecs.iloc[:, 1:]
+feature_vecs_array = feature_vecs.to_numpy()
+
+neighborhood = NearestNeighbors(n_neighbors=11)
+neighborhood.fit(feature_vecs_array)
+
+face_labels_df = pd.read_csv("face_labels.csv")
+face_labels_df = pd.DataFrame({"Name" : face_labels_df["0"]})
+face_labels = face_labels_df["Name"].values
+current_dir = os.getcwd()
+faces_dir = os.path.join(current_dir, "faces/")
+
+# test_indices = np.random.randint(0, len(face_labels), 10)
+# neighborhood = test_indices
+
+print(faces_dir)
+with st.form(key="init_form"):
+   
+    choice = st.selectbox("Choose Picture", face_labels)
+    face_index = np.where(face_labels == choice)[0]
+    img_path = os.path.join(faces_dir, choice)
+    # st.image(img_path)
+    st.image(img_path) 
+
+    neighbors = neighborhood.kneighbors(feature_vecs_array[face_index].reshape(1,-1))
+    neighbors = neighbors[-1][0][1:]
+    
+    face_paths = [os.path.join(faces_dir, face_labels[index]) for index in neighbors]
+    # The index of choice in model_pointers will access the models list
+    # and select the Hugging Face model path at index.  
+    analyze = st.form_submit_button("Analyze")
+
+if analyze:
+    with st.spinner("Analyzing..."):
+        
+        st.write("Nothing Yet")
+        
+
+        cols = cycle(st.columns(5)) # st.columns here since it is out of beta at the time I'm writing this
+        for idx, face in enumerate(face_paths):
+            next(cols).image(face, width=150, caption=face_labels[neighbors[idx]])
+            
+        # sentiment_pipeline = pipeline(model=user_picked_model)
+        # sentiment_results=sentiment_pipeline(input_text)
+        # st.write(f"Sentiment: {sentiment_results[0]['label']}")
+        # st.write(f"Score: {sentiment_results[0]['score']}")
+else:
+    st.write("no input detected")
+
+    

requirements.txt

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+streamlit
+sklearn
+matplotlib