Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load a summarization model from Hugging Face
+summarizer = pipeline("summarization")
+
+def evaluate_text_against_rubric(rubric, text):
+    # Split rubric into criteria
+    criteria = rubric.split('\n')
+    
+    # Use summarizer for each criterion as a dummy placeholder
+    evaluations = {}
+    for i, criterion in enumerate(criteria):
+        if criterion.strip():
+            summary = summarizer(text, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
+            evaluations[f'Criteria {i+1}'] = {
+                "Criterion": criterion,
+                "Score": 3,  # Dummy score for now
+                "Comment": f"Evaluation based on criterion: {criterion}",
+                "Example": summary
+            }
+    return evaluations
+
+def evaluate(rubric, text):
+    evaluation = evaluate_text_against_rubric(rubric, text)
+    evaluation_text = ""
+    for criterion, details in evaluation.items():
+        evaluation_text += f"{criterion}:\n"
+        evaluation_text += f"  Criterion: {details['Criterion']}\n"
+        evaluation_text += f"  Score: {details['Score']}\n"
+        evaluation_text += f"  Comment: {details['Comment']}\n"
+        evaluation_text += f"  Example: {details['Example']}\n\n"
+    return evaluation_text
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=evaluate,
+    inputs=[
+        gr.Textbox(lines=10, placeholder="Enter your rubric here..."),
+        gr.Textbox(lines=10, placeholder="Paste the text to be evaluated here...")
+    ],
+    outputs="text",
+    title="Text Evaluator",
+    description="Upload or enter a rubric and paste text for evaluation. The system will grade the text against the rubric and provide feedback."
+)
+
+# Launch the interface
+interface.launch()