Sync from GitHub 2025-09-07T21:40:05Z

hf-space/hf-space/.github/workflows/sync-to-hf.yml

@@ -0,0 +1,34 @@
+name: Sync to Hugging Face Space
+
+on:
+  push:
+    branches: [ main ]
+
+jobs:
+  sync:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout GitHub repo
+        uses: actions/checkout@v4
+
+      - name: Set up Git
+        run: |
+          git config --global user.email "actions@github.com"
+          git config --global user.name "github-actions"
+
+      - name: Mirror to Hugging Face Space
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          SPACE="mandar1007/cs1-team20"
+          rm -rf hf-space
+          git clone https://user:$HF_TOKEN@huggingface.co/spaces/$SPACE hf-space
+          rsync -av --exclude ".git" ./ hf-space/
+          cd hf-space
+          git add -A
+          if git diff --cached --quiet; then
+            echo "No changes to commit."
+          else
+            git commit -m "Sync from GitHub $(date -u +%Y-%m-%dT%H:%M:%SZ)"
+            git push origin main
+          fi

hf-space/hf-space/app.py

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+import gradio as gr
+import time
+from inference_api import run_inference_api
+from inference_local import run_inference_local
+
+def predict(text, backend):
+    text = (text or "").strip()
+    if not text:
+        return {"error": "Please enter some text."}, ""
+
+    start = time.perf_counter()
+    if backend == "API (InferenceClient)":
+        result = run_inference_api(text)
+    else:
+        result = run_inference_local(text)
+    latency_ms = (time.perf_counter() - start) * 1000
+    return result, f"{latency_ms:.2f} ms"
+
+with gr.Blocks(theme=gr.themes.Base()) as demo:
+    gr.Markdown(
+        """
+        # Case Study 1 — Sentiment Analysis
+        This application demonstrates two approaches to running a machine learning model:
+        1) Using a remote API (Hugging Face Inference Client), and
+        2) Running the model locally within this Space using a Transformers pipeline.
+
+        Enter text, choose the backend, and compare performance and results.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            text_input = gr.Textbox(
+                label="Input Text",
+                placeholder="Type a sentence or review...",
+                lines=3
+            )
+            backend = gr.Radio(
+                ["API (InferenceClient)", "Local (Transformers pipeline)"],
+                value="API (InferenceClient)",
+                label="Select Backend"
+            )
+            examples = gr.Examples(
+                examples=[
+                    ["I really enjoyed this product.", "API (InferenceClient)"],
+                    ["The service was disappointing.", "Local (Transformers pipeline)"],
+                ],
+                inputs=[text_input, backend],
+                label="Example Inputs"
+            )
+            submit_btn = gr.Button("Analyze Sentiment")
+            clear_btn = gr.Button("Clear")
+
+        with gr.Column(scale=1):
+            output = gr.JSON(label="Prediction Result")
+            latency = gr.Textbox(label="Latency", interactive=False)
+
+    submit_btn.click(fn=predict, inputs=[text_input, backend], outputs=[output, latency])
+    clear_btn.click(fn=lambda: ("", "API (InferenceClient)", "",), outputs=[text_input, backend, latency])
+
+if __name__ == "__main__":
+    demo.launch()

hf-space/hf-space/hf-space/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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

hf-space/hf-space/hf-space/README.md

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+---
+title: Cs1 Team20
+emoji: 🌖
+colorFrom: gray
+colorTo: blue
+sdk: gradio
+sdk_version: 5.44.1
+app_file: app.py
+pinned: false
+short_description: Gradio app demonstrating local vs API-based ML inference
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

hf-space/hf-space/requirements.txt

@@ -0,0 +1 @@
+gradio>=4.0.0

hf-space/inference_api.py

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+import os
+from huggingface_hub import InferenceClient
+
+# Public sentiment model
+MODEL_ID = os.getenv("HF_API_MODEL_ID", "distilbert-base-uncased-finetuned-sst-2-english")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+_client = InferenceClient(model=MODEL_ID, token=HF_TOKEN)
+
+def run_inference_api(text: str):
+    try:
+        # Returns a list of {label, score}
+        return _client.text_classification(text)
+    except Exception as e:
+        return {"error": str(e)}

hf-space/inference_local.py

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+from transformers import pipeline
+
+_classifier = pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english"
+)
+
+def run_inference_local(text: str):
+    try:
+        return _classifier(text)  # list of {label, score}
+    except Exception as e:
+        return {"error": str(e)}

hf-space/requirements.txt

@@ -1 +1,4 @@
-gradio>=4.0.0
+gradio>=4.0.0
+transformers>=4.42.0
+huggingface_hub>=0.23.0
+torch --extra-index-url https://download.pytorch.org/whl/cpu

inference_local.py

@@ -1,10 +1,12 @@
 from transformers import pipeline
 
-# Downloads on first run; then cached in the Space
-_classifier = pipeline("sentiment-analysis")
+_classifier = pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english"
+)
 
 def run_inference_local(text: str):
     try:
         return _classifier(text)  # list of {label, score}
     except Exception as e:
-        return {"error": str(e)}
+        return {"error": str(e)}