add app

app.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import torch
+from transformers import pipeline
+import os
+
+#load_dotenv()
+key=os.environ("HF_KEY")
+
+
+def load_model():
+    pipe=pipeline(task="fill-mask",model="atlasia/xlm-roberta-large-ft-alatlas",token=key)
+    return pipe
+
+print("[INFO] load model ...")
+pipe=load_model()
+print("[INFO] model loaded")
+
+# def predict(text):
+#     predictions=pipe(text)
+#     return predictions[0]["sequence"],predictions
+
+
+def predict(text):
+    
+    # Get prediction
+    with torch.no_grad():
+        outputs = pipe(text)
+        scores= [x["score"] for x in outputs]
+        tokens= [x["token_str"] for x in outputs]
+       # scores= [x["score"] for x in outputs]
+    # Convert to percentages and create label-probability pairs
+    #probs = probabilities[0].tolist()
+    return {label: float(prob) * 100 for label, prob in zip(tokens, scores)}
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            # Input text box
+            input_text = gr.Textbox(
+                label="Input",
+                placeholder="Enter text here..."
+            )
+            
+            # Button row
+            with gr.Row():
+                clear_btn = gr.Button("Clear")
+                submit_btn = gr.Button("Submit", variant="primary")
+            
+            # Examples section
+            gr.Examples(
+                examples=["Hugging Face is the AI community, working together, to [MASK] the future."],
+                inputs=input_text
+            )
+        
+        with gr.Column():
+            # Output label
+            gr.Label("Classification")
+            
+            # Output probabilities
+            output_labels = gr.Label(
+                label="Classification Results",
+                show_label=False
+            )
+
+    # Button actions
+    submit_btn.click(
+        predict,
+        inputs=input_text,
+        outputs=output_labels
+    )
+    
+    clear_btn.click(
+        lambda: "",
+        outputs=input_text
+    )
+
+# Launch the app
+demo.launch()