Create app.py

app.py

@@ -0,0 +1,39 @@
+from transformers import pipeline
+import gradio as gr
+
+# Attempt to load the model and run a test prediction
+try:
+    sentiment_analysis = pipeline(model=("HuggingFaceFW/fineweb-edu-classifier"))#"finiteautomata/bertweet-base-sentiment-analysis")
+    test_output = sentiment_analysis("Testing the model with a simple sentence.")
+    print("Model test output:", test_output)
+except Exception as e:
+    print(f"Failed to load or run model: {e}")
+
+# Prediction function with error handling
+def predict_sentiment(text):
+    try:
+        predictions = sentiment_analysis(text)
+        return f"Label: {predictions[0]['label']}, Score: {predictions[0]['score']:.4f}"
+    except Exception as e:
+        return f"Error processing input: {e}"
+
+
+# Define example inputs
+exams = [
+    "I absolutely love this product! It has changed my life.",
+    "This is the worst movie I have ever seen. Completely disappointing.",
+    "I'm not sure how I feel about this new update. It has some good points, but also many drawbacks.",
+    "The customer service was fantastic! Very helpful and polite.",
+    "Honestly, this was quite a mediocre experience. Nothing special.",
+    "Learning new skills in mathematics can significantly improve problem-solving abilities."
+]
+
+# Gradio interface setup
+iface = gr.Interface(fn=predict_sentiment,
+                     title="education_text_recognizer",
+                     description="Enter text to analyze education relation. Powered by Hugging Face Transformers.",
+                     inputs="text",
+                     outputs="text",
+                     examples=exams)
+
+iface.launch()