Upload 3 files

Dockerfile

@@ -0,0 +1,24 @@
+FROM python:3.10-slim
+
+# Create a new user - myuser
+RUN useradd -ms /bin/bash myuser
+
+# Switch to that user
+USER myuser
+
+# Create a .cache directory for Hugging Face transformers
+# This is necessary to avoid permission issues when running the container as a non-root user,
+# especially when using Hugging Face libraries.
+# The directory will be used to store cached files, models, etc.
+# The permissions are set to allow read/write access for the user.
+RUN mkdir -p /home/myuser/.cache && chmod -R 755 /home/myuser/.cache
+
+ADD requirements.txt .
+
+RUN pip install -r requirements.txt
+
+ADD my_server.py .
+
+EXPOSE 8000
+
+CMD ["python", "my_server.py"]

my_server.py

@@ -0,0 +1,48 @@
+from transformers import pipeline
+from typing import List
+from mcp.server.fastmcp import FastMCP
+
+# Load Sentiment Pipeline from HuggingFace
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+
+# Create an MCP server
+mcp = FastMCP("Second-MCP-Server")
+
+
+#### Tool ####
+# Tool to do sentiment analysis for a list of sentences
+@mcp.tool()
+def sentiment_analyzer(sentences: List[str]) -> List[dict]:
+    """
+    Analyzes the sentiment of a list of input sentences using a preloaded sentiment analysis pipeline.
+    Args:
+        sentences (List[str]): A list of input strings to be analyzed.
+    Returns:
+        List[dict]: A list of dictionaries, each containing:
+            - 'text' (str): The original input sentence.
+            - 'sentiment' (str): The predicted sentiment label (e.g., 'POSITIVE', 'NEGATIVE', etc.).
+    
+    Example:
+        sentiment_analyzer(["I love this!", "This is terrible."])
+        [{'text': 'I love this!', 'sentiment': 'POSITIVE'}, 
+         {'text': 'This is terrible.', 'sentiment': 'NEGATIVE'}]
+    """
+    result = sentiment_pipeline(sentences)
+    sentiments = []
+    for i in range(len(sentences)):
+        sentiments.append({'text': sentences[i], 
+                           'sentiment': result[i]['label']})
+    
+    return sentiments
+
+
+#### Prompt ####
+@mcp.prompt()
+def review_code(sentences: List[str]) -> str:
+    return f"Analyze the sentiment of the following sentences:\n\n{sentences}"
+
+
+if __name__ == "__main__":
+    # Initialize and run the server
+    mcp.run(transport='sse')

requirements.txt

@@ -0,0 +1,3 @@
+torch
+transformers
+gradio[mcp]