Stabilize example input handling

app.py

@@ -17,9 +17,9 @@ def _hash_config(cfg: dict) -> str:
 @lru_cache(maxsize=3)
 def _load_pipeline(hash_key: str, cfg_json: str):
     cfg = json.loads(cfg_json)
-    logger.info(f"🔄 Loading pipeline: {cfg}")
+    logger.info(f"Loading pipeline: {cfg}")
     pipe = pipeline(**cfg)
-    logger.info("✅ Loaded.")
+    logger.info("Loaded.")
     return pipe
 
 def get_pipe(cfg: dict):
@@ -63,39 +63,35 @@ def inference(pipeline_config, inputs, inference_kwargs=None):
     except Exception as e:
         return json.dumps({"error": f"Inference failed: {e}"})
 
-APP_CSS = """
-    /* Force body/Blocks to allow scrolling */
-    .gradio-container {
-        max-height: none !important;
-        overflow-y: auto !important;
-    }
-    /* Ensure the app wrapper scrolls */
-    #component-0 {
-        max-height: none !important;
-    }
-    /* JSON editors need fixed height but scroll internally */
-    .json-editor {
-        max-height: 300px;
-    }
-    """
+# Example configs
+EX1 = {
+    "pipeline_config": {"task": "text-generation", "model": "HuggingFaceTB/SmolLM2-135M-Instruct"},
+    "inputs": "The future of AI is",
+    "inference_kwargs": {"max_new_tokens": 50},
+}
+EX2 = {
+    "pipeline_config": {"task": "zero-shot-classification", "model": "facebook/bart-large-mnli"},
+    "inputs": "This is a contract about data privacy and user rights.",
+    "inference_kwargs": {"candidate_labels": ["legal", "finance", "technology", "sports"]},
+}
+EX3 = {
+    "pipeline_config": {"task": "token-classification", "model": "openai/privacy-filter"},
+    "inputs": "My name is Alice Smith",
+    "inference_kwargs": {},
+}
+
+def run_example(ex):
+    result = inference(ex["pipeline_config"], ex["inputs"], ex["inference_kwargs"])
+    return ex["pipeline_config"], ex["inputs"], ex["inference_kwargs"], result
 
 with gr.Blocks(title="Dynamic Transformers Pipeline API") as demo:
     gr.Markdown("# 🚀 Dynamic Transformers Pipeline API\nZero-GPU. Pass any `transformers.pipeline` config via JSON.")
 
     with gr.Row():
         with gr.Column(scale=1):
-            pcfg = gr.JSON(
-                label="pipeline_config",
-                value={"task": "text-generation", "model": "HuggingFaceTB/SmolLM2-135M-Instruct"},
-            )
-            inp = gr.JSON(
-                label="inputs",
-                value='"The future of AI is"',
-            )
-            ikw = gr.JSON(
-                label="inference_kwargs",
-                value={"max_new_tokens": 50},
-            )
+            pcfg = gr.JSON(label="pipeline_config", value=EX1["pipeline_config"])
+            inp = gr.Textbox(label="inputs", value=EX1["inputs"], lines=3)
+            ikw = gr.JSON(label="inference_kwargs", value=EX1["inference_kwargs"])
             btn = gr.Button("▶️ Run Inference", variant="primary")
 
         with gr.Column(scale=1):
@@ -103,8 +99,7 @@ with gr.Blocks(title="Dynamic Transformers Pipeline API") as demo:
 
     btn.click(inference, [pcfg, inp, ikw], out)
 
-    # Computed examples (not cached, run live on click)
-    gr.Markdown("## 📝 Examples (click to compute live)")
+    gr.Markdown("## 📝 Examples (click to populate & run)")
     with gr.Row():
         with gr.Column():
             gr.Markdown("**Text Generation**")
@@ -121,39 +116,18 @@ with gr.Blocks(title="Dynamic Transformers Pipeline API") as demo:
             ex3_btn = gr.Button("Run: privacy-filter", size="sm")
             ex3_out = gr.JSON(label="result")
 
-    EX1 = {
-        "pipeline_config": {"task": "text-generation", "model": "HuggingFaceTB/SmolLM2-135M-Instruct"},
-        "inputs": "The future of AI is",
-        "inference_kwargs": {"max_new_tokens": 50},
-    }
-    EX2 = {
-        "pipeline_config": {"task": "zero-shot-classification", "model": "facebook/bart-large-mnli"},
-        "inputs": "This is a contract about data privacy and user rights.",
-        "inference_kwargs": {"candidate_labels": ["legal", "finance", "technology", "sports"]},
-    }
-    EX3 = {
-        "pipeline_config": {"task": "token-classification", "model": "openai/privacy-filter"},
-        "inputs": "My name is Alice Smith",
-        "inference_kwargs": {},
-    }
-
-    def load_example(ex):
-        """Populate the main inputs and run the selected example."""
-        result = inference(ex["pipeline_config"], ex["inputs"], ex["inference_kwargs"])
-        return ex["pipeline_config"], ex["inputs"], ex["inference_kwargs"], result
-
     ex1_btn.click(
-        fn=lambda: load_example(EX1),
+        fn=lambda: run_example(EX1),
         inputs=None,
         outputs=[pcfg, inp, ikw, ex1_out],
     )
     ex2_btn.click(
-        fn=lambda: load_example(EX2),
+        fn=lambda: run_example(EX2),
         inputs=None,
         outputs=[pcfg, inp, ikw, ex2_out],
     )
     ex3_btn.click(
-        fn=lambda: load_example(EX3),
+        fn=lambda: run_example(EX3),
         inputs=None,
         outputs=[pcfg, inp, ikw, ex3_out],
     )
@@ -169,4 +143,4 @@ print(client.predict(
     api_name="/inference"
 ))""", language="python")
 
-demo.launch(css=APP_CSS)
+demo.launch()