Update app.py

app.py

@@ -1,22 +1,38 @@
 import gradio as gr
-from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+import numpy as np
 
-# Load pre-trained model for CoLA (linguistic acceptability)
-model_name = "preetidav/salesforce-similarity-model"
-classifier = pipeline("text-classification", model=model_name)
+# Load the Sentence Transformer model
+model = SentenceTransformer("preetidav/salesforce-similarity-model")
 
-def classify_sentence(sentence):
-    result = classifier(sentence)[0]
-    return f"Label: {result['label']} (Confidence: {result['score']:.2f})"
+# Function to generate embeddings
+def get_embedding(sentence):
+    embedding = model.encode(sentence)
+    return str(embedding)  # Convert NumPy array to string for display
+
+# Function to calculate similarity between two sentences
+def get_similarity(sentence1, sentence2):
+    emb1 = model.encode(sentence1)
+    emb2 = model.encode(sentence2)
+    similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
+    return f"Similarity Score: {similarity:.2f}"
 
 # Create Gradio interface
-iface = gr.Interface(
-    fn=classify_sentence,
-    inputs=gr.Textbox(lines=2, placeholder="Enter a sentence..."),
-    outputs="text",
-    title="Sentence Acceptability Classifier",
-    description="This model classifies whether a sentence is linguistically acceptable (LABEL_1) or not (LABEL_0).",
-)
+with gr.Blocks() as demo:
+    gr.Markdown("## Sentence Embedding & Similarity App 🚀")
+    
+    with gr.Tab("Get Sentence Embedding"):
+        input_text = gr.Textbox(label="Enter a sentence:")
+        output_embedding = gr.Textbox(label="Embedding:")
+        btn = gr.Button("Generate Embedding")
+        btn.click(get_embedding, inputs=input_text, outputs=output_embedding)
+    
+    with gr.Tab("Compare Sentence Similarity"):
+        text1 = gr.Textbox(label="Sentence 1:")
+        text2 = gr.Textbox(label="Sentence 2:")
+        output_similarity = gr.Textbox(label="Similarity Score:")
+        btn2 = gr.Button("Compare")
+        btn2.click(get_similarity, inputs=[text1, text2], outputs=output_similarity)
 
-# Launch app
-iface.launch()
+# Launch the Gradio app
+demo.launch()