Add app

Dockerfile

@@ -0,0 +1,23 @@
+FROM python:3.11
+
+# Set up a new user named "user" with user ID 1000 for permission
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Upgreade pip
+RUN pip install --no-cache-dir --upgrade pip
+
+COPY --chown=user requirements.txt .
+
+# Install requirements
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user app.py app.py
+
+ENTRYPOINT ["solara", "run", "app.py", "--host=0.0.0.0", "--port", "7860"]

README.md

@@ -6,6 +6,7 @@ colorTo: green
 sdk: docker
 pinned: false
 license: mit
+app_port: 7860
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import solara
+
+import numpy as np
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import snapshot_download
+from umap import UMAP
+from annoy import AnnoyIndex
+from cluestar import plot_text
+
+news = pd.read_csv('https://raw.githubusercontent.com/alonsosilvaallende/fake-and-real-news-titles/main/example.csv')
+texts = list(news["title"].values)
+texts = [str(text) for text in texts if str(text) != 'nan']
+
+sentences = ["This is an example sentence", "Each sentence is converted"]
+model_path = snapshot_download(
+    repo_id="TaylorAI/gte-tiny", allow_patterns=["*.json", "pytorch_model.bin"]
+)
+
+embedder2 = SentenceTransformer(model_path)
+embeddings2 = [embedder2.encode(str(texts[i])) for i in range(500)]
+
+reducer = UMAP()
+X2 = reducer.fit_transform(embeddings2)
+
+f = len(embeddings2[0])
+t = AnnoyIndex(f, 'angular')
+for i, embedded_text in enumerate(embeddings2):
+    t.add_item(i, embedded_text)
+t.build(1000)
+
+query = solara.reactive("What did Nancy Pelosi said about Obamacare?")
+@solara.component
+def Page():
+    with solara.Column(margin=10):
+        solara.Markdown("#Embeddings")
+        solara.InputText("Enter some query:", query, continuous_update=True)
+        if query.value != "":
+            embedded_query = embedder2.encode(query.value)
+            idx, distances = t.get_nns_by_vector(embedded_query, 10, include_distances=True)
+            df_neighbors = pd.DataFrame()
+            df_neighbors["neighbors"]=[texts[i] for i in idx]
+            df_neighbors["distances"] = distances
+            x = reducer.transform([embedded_query])
+            color_array = ["texts" if i not in idx else "neighbors" for i in range(len(texts[:500]))]+["query"]
+            solara.AltairChart(plot_text(np.vstack((X2,x)), texts[:500]+[query.value], color_array=color_array).configure_range(
+        category=['#0000ff', '#ff0000', '#a0aab4']
+    ))
+            solara.DataFrame(df_neighbors, items_per_page=10)
+            solara.Markdown("Dataset: 'Fake and real news' from [kaggle](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)")
+        else:
+            color_array = ["texts" for _ in range(500)]
+            solara.AltairChart(plot_text(X2, texts[:500], color_array=color_array))

requirements.txt

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+solara
+numpy
+pandas
+sentence-transformers
+annoy
+cluestar
+umap
+umap-learn