Update Dockerfile to streamline application setup by copying all contents and modifying the command to run multiple Python scripts concurrently.

Dockerfile

@@ -1,17 +1,14 @@
+# Use an official Python runtime as a parent image
 FROM python:3.12-slim
 
-# Set the working directory
+# Set the working directory in the container
 WORKDIR /app
 
-# Copy the requirements file and install dependencies
-COPY ./requirements.txt /app/requirements.txt
-RUN pip install --no-cache-dir -r /app/requirements.txt
+# Copy the current directory contents into the container at /app
+COPY . /app
 
-# Copy the application code
-COPY ./app.py /app/app.py
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
 
-# Expose the port the app runs on
-EXPOSE 7860
-
-# Command to run the application
-CMD ["python", "/app/app.py"]
+# Run numpy_server.py and scipy_server.py
+CMD ["sh", "-c", "python numpy_server.py & python scipy_server.py"]

numpy_server.py

@@ -0,0 +1,17 @@
+from fastmcp import FastMCP
+import numpy as np
+
+mcp = FastMCP()
+
+@mcp.tool()
+def numpy_add(a: list[float], b: list[float]) -> list[float]:
+    """Adds two vectors element-wise."""
+    return np.add(a, b).tolist()
+
+@mcp.tool()
+def numpy_multiply(a: list[float], b: list[float]) -> list[float]:
+    """Multiplies two vectors element-wise."""
+    return np.multiply(a, b).tolist()
+
+if __name__ == "__main__":
+    mcp.run() 

scipy_server.py

@@ -0,0 +1,19 @@
+from fastmcp import FastMCP
+from scipy import stats
+
+mcp = FastMCP()
+
+@mcp.tool()
+def scipy_ttest(a: list[float], b: list[float]) -> dict:
+    """Performs an independent two-sample t-test."""
+    t_stat, p_value = stats.ttest_ind(a, b)
+    return {"t_statistic": t_stat, "p_value": p_value}
+
+@mcp.tool()
+def scipy_pearsonr(x: list[float], y: list[float]) -> dict:
+    """Calculates the Pearson correlation coefficient."""
+    corr_coefficient, p_value = stats.pearsonr(x, y)
+    return {"correlation_coefficient": corr_coefficient, "p_value": p_value}
+
+if __name__ == "__main__":
+    mcp.run()