Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from response_scoring import score_response
+from roi_calculator import calculate_roi, Model
+from semantic_memory import SemanticMemory
+
+memory_system = SemanticMemory()
+
+class Scenario:
+    def __init__(self, expected, context):
+        self.expected = expected
+        self.context = context
+
+def evaluate(ai_response, scenario_text, expected_text,
+             usage, current_cost, recommended_cost):
+    
+    scenario = Scenario(expected_text, scenario_text)
+    scores = score_response(ai_response, scenario)
+    
+    current_model = Model("CurrentModel", current_cost)
+    recommended_model = Model("RecommendedModel", recommended_cost)
+    roi = calculate_roi(usage, current_model, recommended_model)
+    
+    memory_system.store_memory(f"Scenario: {scenario_text}\nResponse: {ai_response}")
+    retrieved = memory_system.retrieve_relevant(scenario_text)
+    
+    return scores, roi, retrieved
+
+iface = gr.Interface(
+    fn=evaluate,
+    inputs=[
+        gr.Textbox(label="AI Response", lines=3),
+        gr.Textbox(label="Scenario Context", lines=2),
+        gr.Textbox(label="Expected Output", lines=2),
+        gr.Number(label="Customer Usage per Day"),
+        gr.Number(label="Current Model Cost per Call ($)"),
+        gr.Number(label="Recommended Model Cost per Call ($)")
+    ],
+    outputs=[
+        gr.JSON(label="Response Scores"),
+        gr.JSON(label="ROI Calculation"),
+        gr.JSON(label="Relevant Memories")
+    ],
+    title="AI Evaluator + ROI + Memory Demo"
+)
+
+iface.launch()