Rename app_old.py to app.py

app.py

@@ -0,0 +1,69 @@
+from mcp.server.fastmcp import FastMCP
+from gradio_client import Client
+import sys
+import io
+import json 
+
+mcp = FastMCP("gradio-spaces")
+
+clients = {}
+
+def get_client(space_id: str) -> Client:
+    """Get or create a Gradio client for the specified space."""
+    if space_id not in clients:
+        clients[space_id] = Client(space_id)
+    return clients[space_id]
+
+
+@mcp.tool()
+async def generate_image(prompt: str, space_id: str = "ysharma/SanaSprint") -> str:
+    """Generate an image using Flux.
+    
+    Args:
+        prompt: Text prompt describing the image to generate
+        space_id: HuggingFace Space ID to use 
+    """
+    client = get_client(space_id)
+    result = client.predict(
+            prompt=prompt,
+            model_size="1.6B",
+            seed=0,
+            randomize_seed=True,
+            width=1024,
+            height=1024,
+            guidance_scale=4.5,
+            num_inference_steps=2,
+            api_name="/infer"
+    )
+    return result
+
+
+@mcp.tool()
+async def run_dia_tts(prompt: str, space_id: str = "ysharma/Dia-1.6B") -> str:
+    """Text-to-Speech Synthesis.
+    
+    Args:
+        prompt: Text prompt describing the conversation between speakers S1, S2
+        space_id: HuggingFace Space ID to use 
+    """
+    client = get_client(space_id)
+    result = client.predict(
+            text_input=f"""{prompt}""",
+            audio_prompt_input=None, 
+            max_new_tokens=3072,
+            cfg_scale=3,
+            temperature=1.3,
+            top_p=0.95,
+            cfg_filter_top_k=30,
+            speed_factor=0.94,
+            api_name="/generate_audio"
+    )
+    return result
+
+
+if __name__ == "__main__":
+    import sys
+    import io
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+    
+    mcp.run(transport='stdio')

app_old.py

@@ -1,64 +0,0 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()