Update app.py

app.py

@@ -2,14 +2,12 @@ import os, json
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# Load prompts
+# Load your prompts
 with open("system_prompts.json", encoding="utf-8") as f:
     SYSTEM_PROMPTS = json.load(f)
 
-# Initialize HF InferenceClient
 client = InferenceClient(model=os.getenv("HF_MODEL"), token=os.getenv("HF_TOKEN"))
 
-# Common runner
 def run_model(prompt: str, text: str) -> str:
     resp = client.chat.completions.create(
         messages=[{"role":"system","content":prompt},{"role":"user","content":text}],
@@ -17,27 +15,15 @@ def run_model(prompt: str, text: str) -> str:
     )
     return resp.choices[0].message.content
 
-# Dynamically create tool functions
-def make_tool_fn(prompt):
-    return lambda text: run_model(prompt, text)
-
-tool_fns = {
-    name.lower().replace(" ", "_"): make_tool_fn(prompt)
-    for name, prompt in SYSTEM_PROMPTS.items()
-}
-
-# Build Gradio interface
 demo = gr.Blocks()
 with demo:
     gr.Markdown("# 🌱 Ecolinguistic MCP Server")
-    tabs = gr.Tabs()
-    for badge, fn in tool_fns.items():
-        with tabs:
-            with gr.Tab(badge.replace("_", " ").title()):
-                inp = gr.Textbox(lines=5, label="Text")
-                out = gr.Textbox(label="Response")
-                gr.Button("Run").click(fn, inp, out)
+    for name, prompt in SYSTEM_PROMPTS.items():
+        func_name = name.lower().replace(" ", "_")
+        textbox = gr.Textbox(lines=5, label="Text")
+        output = gr.Textbox(label="Response")
+        gr.Button("Run", api_name=func_name)\
+          .click(lambda txt, p=prompt: run_model(p, txt), inputs=textbox, outputs=output)
 
-# Expose as MCP server
 if __name__ == "__main__":
     demo.launch(mcp_server=True, share=True)