update

Dockerfile

@@ -1,16 +1,16 @@
 FROM python:3.10
 
-# Set working directory
+# Set working directory and ensure it's clean
 
-WORKDIR /app
+WORKDIR /app RUN rm -rf /app/* # Clear any existing files to avoid conflicts
 
 # Copy requirements and install dependencies
 
-COPY requirements.txt . RUN pip install --no-cache-dir --upgrade pip && pip install --no-cache-dir -r requirements.txt
+COPY requirements.txt /app/requirements.txt RUN pip install --no-cache-dir --upgrade pip && pip install --no-cache-dir -r requirements.txt
 
 # Copy the application code
 
-COPY app.py .
+COPY app.py /app/app.py
 
 # Expose the port
 

app.py

@@ -10,7 +10,6 @@ logger = logging.getLogger(__name__)
 # Initialize FastMCP server
 try:
     mcp = FastMCP("gradio-spaces")
-    # Log attributes of mcp to debug
     logger.info("Attributes of mcp: %s", dir(mcp))
 except Exception as e:
     logger.error("Failed to initialize FastMCP: %s", str(e))
@@ -23,7 +22,11 @@ def get_client(space_id: str) -> Client:
     """Get or create a Gradio client for the specified space."""
     if space_id not in clients:
         logger.info("Creating Gradio client for space: %s", space_id)
-        clients[space_id] = Client(space_id)
+        try:
+            clients[space_id] = Client(space_id)  # Add hf_token if private Spaces are used
+        except Exception as e:
+            logger.error("Failed to create Gradio client for %s: %s", space_id, str(e))
+            raise
     return clients[space_id]
 
 @mcp.tool()
@@ -37,19 +40,23 @@ async def generate_image(prompt: str, space_id: str = "ysharma/SanaSprint") -> s
         str: URL or path to the generated image
     """
     logger.info("Generating image with prompt: %s, space: %s", prompt, space_id)
-    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
+    try:
+        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
+    except Exception as e:
+        logger.error("Failed to generate image: %s", str(e))
+        raise
 
 @mcp.tool()
 async def run_dia_tts(prompt: str, space_id: str = "ysharma/Dia-1.6B") -> str:
@@ -62,27 +69,36 @@ async def run_dia_tts(prompt: str, space_id: str = "ysharma/Dia-1.6B") -> str:
         str: URL or path to the generated audio
     """
     logger.info("Generating TTS with prompt: %s, space: %s", prompt, space_id)
-    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
+    try:
+        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
+    except Exception as e:
+        logger.error("Failed to generate TTS: %s", str(e))
+        raise
 
 # Use mcp.sse_app as the FastAPI application
 try:
     fastapi_app = mcp.sse_app  # Use SSE app for MCP server
     logger.info("Using mcp.sse_app as the FastAPI application")
 except AttributeError:
-    logger.error("mcp.sse_app not found; FastMCP configuration may be incorrect")
-    raise
+    logger.error("mcp.sse_app not found; trying streamable_http_app")
+    try:
+        fastapi_app = mcp.streamable_http_app  # Fallback to HTTP app
+        logger.info("Using mcp.streamable_http_app as the FastAPI application")
+    except AttributeError:
+        logger.error("mcp.streamable_http_app not found; FastMCP configuration may be incorrect")
+        raise
 
 if __name__ == "__main__":
     uvicorn.run(fastapi_app, host="0.0.0.0", port=7860)

requirements.txt

@@ -1,4 +1,4 @@
-mcp>=1.0.0 
-gradio-client>=1.0.0 
-uvicorn>=0.30.0 
+mcp>=1.0.0
+gradio-client>=1.0.0
+uvicorn>=0.30.0
 fastapi>=0.115.0