multiple apis and ui in a mcp server

app.py

@@ -1,37 +1,83 @@
-import json
+import numpy as np
 import gradio as gr
-from textblob import TextBlob
+from pathlib import Path
+import os
+from PIL import Image
 
-def sentiment_analysis(text: str) -> str:
+def prime_factors(n: str):
     """
-    Analyze the sentiment of the given text.
+    Compute the prime factorization of a positive integer.
 
     Args:
-        text (str): The text to analyze
+        n (str): The integer to factorize. Must be greater than 1.
+    """
+    n_int = int(n)
+    if n_int <= 1:
+        raise ValueError("Input must be an integer greater than 1.")
+
+    factors = []
+    while n_int % 2 == 0:
+        factors.append(2)
+        n_int //= 2
+
+    divisor = 3
+    while divisor * divisor <= n_int:
+        while n_int % divisor == 0:
+            factors.append(divisor)
+            n_int //= divisor
+        divisor += 2
+
+    if n_int > 1:
+        factors.append(n_int)
+
+    return factors
+
+def image_orientation(image: Image.Image) -> str:
+    """
+    Returns whether image is portrait or landscape.
+
+    Args:
+        image (Image.Image): The image to check.
+
+    Returns:
+        str: "Portrait" if image is portrait, "Landscape" if image is landscape.
+    """
+    return "Portrait" if image.height > image.width else "Landscape"
+
+
+def sepia(input_img):
+    """
+    Apply a sepia filter to the input image.
+
+    Args:
+        input_img (np.array): The input image to apply the sepia filter to.
 
     Returns:
-        str: A JSON string containing polarity, subjectivity, and assessment
+        The sepia filtered image.
     """
-    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"
+    sepia_filter = np.array([
+        [0.393, 0.769, 0.189],
+        [0.349, 0.686, 0.168],
+        [0.272, 0.534, 0.131]
+    ])
+    sepia_img = input_img.dot(sepia_filter.T)
+    sepia_img /= sepia_img.max()
+    return sepia_img
+
+
+
+demo = gr.TabbedInterface(
+    [
+        gr.Interface(prime_factors, gr.Textbox("1001"), gr.Textbox()),
+        gr.Interface(image_orientation, gr.Image(type="pil"), gr.Textbox()),
+        gr.Interface(sepia, gr.Image(), gr.Image()),
+    ],
+    [
+        "Prime Factors",
+        "Image Orientation Checker",
+        "Sepia Filter",
+    ]
 )
 
-# Launch the interface and MCP server
 if __name__ == "__main__":
-    demo.launch(mcp_server=True, share=True)
+    demo.launch(mcp_server=True)