update app.py

app.py

@@ -2,12 +2,22 @@ import numpy as np
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 
-# Load your SentenceTransformer model fine-tuned for NLI
-model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-Nli-Matryoshka")
+# Available models
+model_dict = {
+    "Arabic-mpnet-base-all-nli-triplet": "Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet",
+    "Arabic-all-nli-triplet-Matryoshka": "Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka",
+    "Arabert-all-nli-triplet-Matryoshka": "Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka",
+    "Arabic-labse-Matryoshka": "Omartificial-Intelligence-Space/Arabic-labse-Matryoshka",
+    "Marbert-all-nli-triplet-Matryoshka": "Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka"
+}
 
+# Function to load the selected model
+def load_model(model_name):
+    return SentenceTransformer(model_dict[model_name])
 
 # Function to compute similarity and classify relationship
-def predict(mode, sentence1, sentence2=None, sentence3=None, sentence4=None, dimension="64"):
+def predict(model_name, mode, sentence1, sentence2=None, sentence3=None, sentence4=None, dimension="64"):
+    model = load_model(model_name)
     dimension = int(dimension)
     result = {
         "Selected Dimension": dimension,
@@ -41,9 +51,24 @@ def predict(mode, sentence1, sentence2=None, sentence3=None, sentence4=None, dim
         similarity_scores = {"Similarity Score": float(similarity_score)}
         result["Similarity Scores"] = similarity_scores
     
+    # Word-level similarity
+    if mode == "Compare two sentences" and sentence2 is not None:
+        words1 = sentence1.split()
+        words2 = sentence2.split()
+        word_pairs = [(w1, w2) for w1 in words1 for w2 in words2]
+        word_embeddings1 = model.encode(words1)[..., :dimension]
+        word_embeddings2 = model.encode(words2)[..., :dimension]
+        word_similarities = {
+            f"{w1} - {w2}": float(util.cos_sim(we1, we2))
+            for (w1, we1) in zip(words1, word_embeddings1)
+            for (w2, we2) in zip(words2, word_embeddings2)
+        }
+        result["Word-level Similarities"] = word_similarities
+    
     return result
 
 # Define inputs and outputs for Gradio interface
+model_dropdown = gr.Dropdown(choices=list(model_dict.keys()), label="Model")
 mode_dropdown = gr.Dropdown(choices=["Compare two sentences", "Compare one to three"], label="Mode")
 dimension_dropdown = gr.Dropdown(choices=["768", "512", "256", "128", "64"], label="Embedding Dimension")
 sentence1_input = gr.Textbox(lines=2, placeholder="Enter the first sentence here...", label="Sentence 1")
@@ -51,19 +76,19 @@ sentence2_input = gr.Textbox(lines=2, placeholder="Enter the second sentence her
 sentence3_input = gr.Textbox(lines=2, placeholder="Enter the third sentence here...", label="Sentence 3")
 sentence4_input = gr.Textbox(lines=2, placeholder="Enter the fourth sentence here...", label="Sentence 4")
 
-inputs = [mode_dropdown, sentence1_input, sentence2_input, sentence3_input, sentence4_input, dimension_dropdown]
+inputs = [model_dropdown, mode_dropdown, sentence1_input, sentence2_input, sentence3_input, sentence4_input, dimension_dropdown]
 outputs = gr.JSON(label="Detailed Similarity Scores")
 
 examples = [
-    ["Compare one to three", "يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.", "ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه", "الشاب نائم بينما الأم تقود ابنتها إلى الحديقة", "رجل يقرأ الجريدة في الحديقة", "64"],
-    ["Compare two sentences", "يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.", "ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه", None, None, "64"]
+    ["Arabic-all-nli-triplet-Matryoshka", "Compare one to three", "يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.", "ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه", "الشاب نائم بينما الأم تقود ابنتها إلى الحديقة", "رجل يقرأ الجريدة في الحديقة", "64"],
+    ["Arabic-all-nli-triplet-Matryoshka", "Compare two sentences", "يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.", "ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه", None, None, "64"]
 ]
 
 # Create Gradio interface
 gr.Interface(
     fn=predict,
     title="Arabic Sentence Similarity with Matryoshka Model",
-    description="Compute the semantic similarity between Arabic sentences using the Matryoshka SentenceTransformer model.",
+    description="Compute the semantic similarity between Arabic sentences using various SentenceTransformer models.",
     inputs=inputs,
     examples=examples,
     outputs=outputs,