Create space_app.py

space_app.py

@@ -0,0 +1,49 @@
+from fastapi import FastAPI
+import gradio as gr
+
+# Reuse the existing FastAPI app and inference from api_server
+from api_server import app as fastapi_app, run_models
+
+app: FastAPI = fastapi_app
+
+
+def ui_fn(text: str):
+    r = run_models(text)
+    fabsa_output = r.get("FABSA", {"label": "N/A", "scores": {}})
+    moodmeter_output = r.get("MoodMeter", {"label": "N/A", "scores": {}})
+    twitter_output = r.get("Twitter", {"label": "N/A", "scores": {}})
+    ensemble_output = r.get("Ensemble", {"label": "N/A"})
+
+    # Ensure outputs are dictionaries for gr.JSON
+    if not isinstance(fabsa_output, dict):
+        fabsa_output = {"label": str(fabsa_output), "scores": {}}
+    if not isinstance(moodmeter_output, dict):
+        moodmeter_output = {"label": str(moodmeter_output), "scores": {}}
+    if not isinstance(twitter_output, dict):
+        twitter_output = {"label": str(twitter_output), "scores": {}}
+    if not isinstance(ensemble_output, dict):
+        ensemble_output = {"label": str(ensemble_output)}
+
+    return fabsa_output, moodmeter_output, twitter_output, ensemble_output
+
+
+demo = gr.Interface(
+    fn=ui_fn,
+    inputs=gr.Textbox(label="Enter text", placeholder="Type something emotional…"),
+    outputs=[
+        gr.JSON(label="FABSA"),
+        gr.JSON(label="MoodMeter"),
+        gr.JSON(label="Twitter"),
+        gr.JSON(label="Ensemble"),
+    ],
+    title="Mental Health Sentiment Analyzer",
+    description="Gradio UI + FastAPI backend deployed on Hugging Face Spaces.",
+)
+
+# Mount Gradio UI on the root path
+app = gr.mount_gradio_app(app, demo, path="/")
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run("space_app:app", host="127.0.0.1", port=8000, reload=False)