update app.py

app.py

@@ -5,12 +5,21 @@ from sentence_transformers import SentenceTransformer, util
 # Load your SentenceTransformer model fine-tuned for NLI
 model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-Nli-Matryoshka")
 
-# Define the labels for NLI
-labels = ["contradiction", "entailment", "neutral"]
 
 # Function to compute similarity and classify relationship
 def predict(mode, sentence1, sentence2=None, sentence3=None, sentence4=None, dimension="64"):
     dimension = int(dimension)
+    result = {
+        "Selected Dimension": dimension,
+        "Input Sentences": {
+            "Sentence 1": sentence1,
+            "Sentence 2": sentence2,
+            "Sentence 3": sentence3,
+            "Sentence 4": sentence4
+        },
+        "Similarity Scores": {}
+    }
+    
     if mode == "Compare one to three":
         if sentence2 is None or sentence3 is None or sentence4 is None:
             return "Please provide three sentences for comparison.", {}
@@ -26,11 +35,13 @@ def predict(mode, sentence1, sentence2=None, sentence3=None, sentence4=None, dim
     if mode == "Compare one to three":
         similarities = util.cos_sim(embeddings[0], embeddings[1:])
         similarity_scores = {f"Sentence {i+2}": float(similarities[0, i]) for i in range(3)}
+        result["Similarity Scores"] = similarity_scores
     else:
         similarity_score = util.cos_sim(embeddings[0], embeddings[1])
         similarity_scores = {"Similarity Score": float(similarity_score)}
+        result["Similarity Scores"] = similarity_scores
     
-    return similarity_scores
+    return result
 
 # Define inputs and outputs for Gradio interface
 mode_dropdown = gr.Dropdown(choices=["Compare two sentences", "Compare one to three"], label="Mode")
@@ -41,7 +52,7 @@ sentence3_input = gr.Textbox(lines=2, placeholder="Enter the third sentence here
 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]
-outputs = gr.JSON(label="Similarity Scores")
+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"],
@@ -57,5 +68,5 @@ gr.Interface(
     examples=examples,
     outputs=outputs,
     cache_examples=False,
-    article="Author: Your Name. Model from Hugging Face Hub: Omartificial-Intelligence-Space/Arabic-Nli-Matryoshka",
+    article="Author: OMER NACAR. Model from Hugging Face Hub: [Omartificial-Intelligence-Space/Arabic-Nli-Matryoshka](https://huggingface.co/Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka)",
 ).launch(debug=True, share=True)