Implement Gradio interface for emoji mashup based on sentence input

app.py

@@ -1,7 +1,77 @@
 import gradio as gr
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import requests
+from PIL import Image
+from io import BytesIO
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load model
+model = SentenceTransformer('all-mpnet-base-v2')
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch(share=True)
+# Load emoji dictionaries
+def kitchen_txt_to_dict(filepath):
+    emoji_dict = {}
+    with open(filepath, 'r', encoding='utf-8') as f:
+        for line in f:
+            parts = line.strip().split(' ', 1)
+            if len(parts) == 2:
+                emoji, desc = parts
+                emoji_dict[emoji] = desc
+    return emoji_dict
+
+emotion_dict = kitchen_txt_to_dict('/content/drive/MyDrive/google emoji/google-emoji-kitchen-emotion.txt')
+event_dict = kitchen_txt_to_dict('/content/drive/MyDrive/google emoji/google-emoji-kitchen-item.txt')
+
+# Precompute embeddings
+emotion_embeddings = {emoji: model.encode(desc) for emoji, desc in emotion_dict.items()}
+event_embeddings = {emoji: model.encode(desc) for emoji, desc in event_dict.items()}
+
+# Helper functions
+def find_top_emojis(embedding, emoji_embeddings, top_n=1):
+    similarities = [
+        (emoji, cosine_similarity([embedding], [e_embed])[0][0])
+        for emoji, e_embed in emoji_embeddings.items()
+    ]
+    similarities.sort(key=lambda x: x[1], reverse=True)
+    return [emoji for emoji, _ in similarities[:top_n]]
+
+def get_emoji_kitchen_url(emoji1, emoji2, size=256):
+    return f"https://emojik.vercel.app/s/{emoji1}_{emoji2}?size={size}"
+
+def fetch_image(url):
+    try:
+        response = requests.get(url)
+        if response.status_code == 200 and "image" in response.headers.get("Content-Type", ""):
+            return Image.open(BytesIO(response.content))
+        else:
+            return None
+    except:
+        return None
+
+# Main function for Gradio
+def sentence_to_emojis(sentence):
+    embedding = model.encode(sentence)
+    
+    top_emotion = find_top_emojis(embedding, emotion_embeddings, top_n=1)[0]
+    top_event = find_top_emojis(embedding, event_embeddings, top_n=1)[0]
+    
+    mashup_url = get_emoji_kitchen_url(top_emotion, top_event)
+    mashup_image = fetch_image(mashup_url)
+    
+    return top_emotion, top_event, mashup_image
+
+# Gradio interface
+demo = gr.Interface(
+    fn=sentence_to_emojis,
+    inputs=gr.Textbox(lines=2, placeholder="Type a sentence..."),
+    outputs=[
+        gr.Text(label="Top Emotion Emoji"),
+        gr.Text(label="Top Event Emoji"),
+        gr.Image(label=" Kitchen Emoji")
+    ],
+    title="Sentence → Emoji Mashup",
+    description="Get the top emotion and event emoji from your sentence, and view the mashup!"
+)
+
+demo.launch(share=True)