Configure app for Hugging Face Gradio SDK deployment

.github/workflows/deploy.yml

@@ -1,4 +1,4 @@
-name: Deploy to Modal.com
+name: Deploy to Hugging Face Spaces
 
 on:
   push:
@@ -7,23 +7,19 @@ on:
 
 jobs:
   deploy:
-    name: Deploy FastAPI to Modal.com
+    name: Sync to Hugging Face Space
     runs-on: ubuntu-latest
 
     steps:
       - name: Checkout Repository
         uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
-
-      - name: Install Modal CLI
-        run: pip install modal
+          fetch-depth: 0
 
-      - name: Deploy to Modal
+      - name: Push to Hugging Face Space
         env:
-          MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }}
-          MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }}
-        run: modal deploy modal_app.py
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          HF_USERNAME: ${{ secrets.HF_USERNAME }}
+        run: |
+          git push https://$HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/$HF_USERNAME/urdu-sentiment-engine main
+

.gitignore

@@ -12,4 +12,6 @@ venv/
 remove_comments_script.py
 *.tsv
 *.zip
+*.mp4
 .claude/
+

app.py

@@ -168,4 +168,27 @@ def health_check():
 if __name__ == "__main__":
     import uvicorn
     print("Starting FastAPI Uvicorn Server...")
-    uvicorn.run("app:app", host="0.0.0.0", port=5000, reload=True)
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)
+else:
+    import gradio as gr
+
+    def gradio_predict(text):
+        if not text or not text.strip():
+            return {"error": "Please enter valid text."}
+        lang = detect_language(text)
+        res = engine.predict(text)
+        res["language"] = lang
+        update_analytics(res, lang)
+        return res
+
+    with gr.Blocks(title="Urdu Sentiment & Emotion Engine") as demo:
+        gr.Markdown("# 🇵🇰 Urdu Sentiment & Emotion Analysis Engine")
+        gr.Markdown("Multilingual Sentiment and Emotion Analysis for Urdu Script, Roman Urdu, and English/Mixed text.")
+        with gr.Row():
+            input_text = gr.Textbox(lines=3, placeholder="یہ پروڈکٹ بہت عمدہ ہے / Main aj bohat khush hoon", label="Input Text")
+        btn = gr.Button("Analyze Sentiment & Emotion", variant="primary")
+        output_json = gr.JSON(label="Analysis Output")
+        btn.click(fn=gradio_predict, inputs=input_text, outputs=output_json)
+
+    app = gr.mount_gradio_app(app, demo, path="/gradio")
+

requirements.txt

@@ -8,4 +8,6 @@ numpy==1.26.0
 pandas==2.2.0
 scikit-learn==1.4.0
 accelerate==0.29.0
+gradio
+