first version

app.py

@@ -1,7 +1,94 @@
 import gradio as gr
+from root import RootSignals, skills
 
-def greet(name):
-    return "Hello " + name + "!!"
+client = None  # Initialize client as None
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+ROOT_EVALUATORS = ['Answer Correctness',
+ 'Answer Relevance',
+ 'Clarity',
+ 'Coherence',
+ 'Conciseness',
+ 'Confidentiality',
+ 'Engagingness',
+ 'Formality',
+ 'Harmlessness',
+ 'Helpfulness',
+ 'Non-toxicity',
+ 'Originality',
+ 'Persuasiveness',
+ 'Politeness',
+ 'Precision',
+ 'Quality of Writing Creative',
+ 'Quality of Writing Professional',
+ 'Relevance',
+ 'Safety for Children',
+ 'Sentiment recognition',
+ ]
+
+def initialize_client(api_key):
+    global client
+    client = RootSignals(api_key=api_key)
+    return gr.Dropdown(choices=ROOT_EVALUATORS)
+
+def process_and_evaluate(api_key, user_input, llm_response, selected_evaluator):
+    global client
+    if not client:
+        evaluator_dropdown = initialize_client(api_key)
+    
+    # Get the evaluator instance by name
+    evaluator = client.evaluators.get_by_name(name=selected_evaluator)
+    
+    # Run evaluation using selected evaluator
+    evaluation_result = evaluator.run(request=user_input, response=llm_response)
+    score = evaluation_result.score
+    justification = evaluation_result.justification
+    return score, justification
+
+
+# Create the interface with a custom layout
+with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as demo:
+    with gr.Row():
+        gr.Image(value="https://lh3.googleusercontent.com/d/1e49skZjYGTZscWCVrhf6Ad1bRP2pvwpI", height=60)
+        gr.Markdown("<div>&nbsp;</div>")  # Add some space below the image
+    gr.Markdown("# Root Evaluators Demo by Root Signals")
+    
+    api_key = gr.Textbox(
+        label="🔑 Root Signals API Key",
+        placeholder="Enter your Root Signals API key...",
+        type="password",
+        show_label=True,
+    )
+    
+    with gr.Row():
+        # Left column
+        with gr.Column():
+            user_input = gr.Textbox(label="👤 User Instruction or Question (Optional)", placeholder="Enter user input here...", interactive=True)
+            llm_response = gr.Textbox(label="🤖 LLM Response (to be evaluated)", placeholder="Enter the LLM response to be evaluated here...", interactive=True)
+            evaluator_dropdown = gr.Dropdown(
+                label="🔎 Select Evaluator",
+                choices=[],  # Will be populated after client initialization
+                interactive=True
+            )
+        
+        # Right column
+        with gr.Column():
+            score = gr.Textbox(label="📊 Score (between 0 and 1)", interactive=False)
+            justification = gr.TextArea(label="💬 Justification", interactive=False)
+    
+    # Initialize client and update dropdown when API key is provided
+    api_key.change(
+        fn=initialize_client,
+        inputs=[api_key],
+        outputs=[evaluator_dropdown]
+    )
+    
+    # Button to trigger the process
+    submit_btn = gr.Button("🧐 EVALUATE", variant="primary")
+    submit_btn.click(
+        fn=process_and_evaluate,
+        inputs=[api_key, user_input, llm_response, evaluator_dropdown],
+        outputs=[score, justification]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+gradio==5.13.2
+root-signals==1.3.3