Initial commit

.continue/mcpServers/playwright-mcp.yaml

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+name: Playwright mcpServer
+version: 0.0.1
+schema: v1
+mcpServers:
+  - name: Browser search
+    command: npx
+    args:
+      - "@playwright/mcp@latest"

.continue/models/llama-max.yaml

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+name: Ollama Llama model
+version: 0.0.1
+schema: v1
+models:
+  - provider: ollama
+    model: llama3.1:8b
+    defaultCompletionOptions:
+      contextLength: 128000
+    name: a llama3.1:8b max
+    roles:
+      - chat
+      - edit

.gitignore

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+node_modules/
+__pycache__/
+.tm

README.md

@@ -1,13 +1,20 @@
 ---
-title: Gradio
-emoji: 💻
-colorFrom: indigo
-colorTo: yellow
+title: Mcp Sentiment
+emoji: 🐢
+colorFrom: green
+colorTo: blue
 sdk: gradio
 sdk_version: 5.34.0
 app_file: app.py
 pinned: false
-short_description: Unit 2 client
+license: mit
+short_description: Unit 2 MCP course
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Deps
+
+```bash
+pip install "smolagents[mcp]" "gradio[mcp]" mcp fastmcp
+```

app.py

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+import json
+import gradio as gr
+from textblob import TextBlob
+
+def sentiment_analysis(text: str) -> str:
+    """
+    Analyze the sentiment of the given text.
+
+    Args:
+        text (str): The text to analyze
+
+    Returns:
+        str: A JSON string containing polarity, subjectivity, and assessment
+    """
+    blob = TextBlob(text)
+    sentiment = blob.sentiment
+
+    result = {
+        "polarity": round(sentiment.polarity, 2),  # -1 (negative) to 1 (positive)
+        "subjectivity": round(sentiment.subjectivity, 2),  # 0 (objective) to 1 (subjective)
+        "assessment": "positive" if sentiment.polarity > 0 else "negative" if sentiment.polarity < 0 else "neutral"
+    }
+
+    return json.dumps(result)
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=gr.Textbox(placeholder="Enter text to analyze..."),
+    outputs=gr.Textbox(),  # Changed from gr.JSON() to gr.Textbox()
+    title="Text Sentiment Analysis",
+    description="Analyze the sentiment of text using TextBlob"
+)
+
+# Launch the interface and MCP server
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

gradio-client.py

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+import gradio as gr
+import os
+
+from smolagents import InferenceClientModel, CodeAgent, MCPClient
+
+
+try:
+    mcp_client = MCPClient(
+        {"url": "https://abidlabs-mcp-tool-http.hf.space/gradio_api/mcp/sse"}
+    )
+    tools = mcp_client.get_tools()
+
+    model = InferenceClientModel(token=os.getenv("HF_HUB_TOKEN"))
+    agent = CodeAgent(tools=[*tools], model=model, additional_authorized_imports=["json", "ast", "urllib", "base64"])
+
+    demo = gr.ChatInterface(
+        fn=lambda message, history: str(agent.run(message)),
+        type="messages",
+        examples=["Analyze the sentiment of the following text 'This is awesome'"],
+        title="Agent with MCP Tools",
+        description="This is a simple agent that uses MCP tools to answer questions.",
+    )
+
+    demo.launch()
+finally:
+    mcp_client.disconnect()

requirements.txt

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+gradio[mcp]
+textblob