Add feedback tab

app.py

@@ -1,12 +1,66 @@
+from typing import List, Literal, Tuple, TypedDict
 import os
 
 import gradio as gr
 
-from common import org_search_component as oss
-from formatting import process_reasons, parse_pcs_descriptions, parse_geo_descriptions
-from services import RfpRecommend
+try:
+    from common import org_search_component as oss
+    from formatting import process_reasons, parse_pcs_descriptions, parse_geo_descriptions
+    from services import RfpRecommend, RfpFeedback
+except ImportError:
+    from ..common import org_search_component as oss
+    from .formatting import process_reasons, parse_pcs_descriptions, parse_geo_descriptions
+    from .services import RfpRecommend, RfpFeedback
 
 api = RfpRecommend()
+reporting = RfpFeedback()
+
+class LoggedComponents(TypedDict):
+    recommendations: gr.components.Component
+    ratings: List[gr.components.Component]
+    correctness: gr.components.Component
+    sufficiency: gr.components.Component
+    comments: gr.components.Component
+    email: gr.components.Component
+
+
+def single_recommendation_response(
+    item_number: int,
+    rec_type: Literal["RFP"] = "RFP"
+) -> gr.Radio:
+    """Generates a radio button group to provide feedback for single recommendation indexed by `item_number`.
+    Since the index values start from `0` we add `1` to indicate the ordinal value in the info text.
+
+    Parameters
+    ----------
+    item_number : int
+        Recommendation index starting from 0
+
+    Returns
+    -------
+    gr.Radio
+    """
+
+    ordinal = str(item_number + 1)
+
+    suffix = "th"
+    if ordinal.endswith('1') and not ordinal.endswith('11'):
+        suffix = "st"
+    elif ordinal.endswith('2') and not ordinal.endswith('12'):
+        suffix = "nd"
+    elif ordinal.endswith('3') and not ordinal.endswith('13'):
+        suffix = "rd"
+
+    elem = gr.Radio(
+        choices=[
+            "Not relevant and not useful",
+            "Relevant but not useful",
+            "Relevant and useful"
+        ],
+        label=f"Recommendation #{ordinal}",
+        info=f"Evaluate the {ordinal}{suffix} {rec_type} (if applicable)"
+    )
+    return elem
 
 
 def recommend_invoke(recipient: gr.State):
@@ -27,14 +81,14 @@ def recommend_invoke(recipient: gr.State):
             parse_pcs_descriptions(rfp["taxonomy"]),
             parse_geo_descriptions(rfp["area_served"])
         ])
-    return (
-        output,
-        process_reasons(response.get("meta", {}) or {}),
-        response.get("recommendations", [])
-    )
 
+    if len(output) == 0:
+        raise gr.Error("No relevant RFPs were found, please try again in the future as new RFPs become available.")
 
-def build_demo():
+    return output, process_reasons(response.get("meta", {}) or {}), response
+
+
+def build_recommender() -> Tuple[LoggedComponents, gr.Blocks]:
     with gr.Blocks(theme=gr.themes.Soft(), title="RFP recommendations") as demo:
         gr.Markdown(
             """
@@ -82,8 +136,7 @@ def build_demo():
             interactive=False
         )
 
-        with gr.Accordion("JSON output", open=False):
-            recommendations_json = gr.JSON(label="Recommended RFPs JSON")
+        recommendations_json = gr.JSON(label="Recommended RFPs JSON", visible=False)
 
         # pylint: disable=no-member
         recommend.click(
@@ -92,9 +145,105 @@ def build_demo():
             outputs=[rec_outputs, reasons_output, recommendations_json]
         )
 
+    logged = LoggedComponents(
+        recommendations=recommendations_json
+    )
+
+    return logged, demo
+
+
+def build_feedback(
+    components: LoggedComponents,
+    N: int = 5,
+    rec_type: Literal["RFP"] = "RFP",
+) -> gr.Blocks:
+
+    def handle_feedback(*args):
+        try:
+            reporting(
+                recommendation_data=args[0],
+                ratings=list(args[1: (N + 1)]),
+                info_is_correct=args[N + 1],
+                info_is_sufficient=args[N + 2],
+                comments=args[N + 3],
+                email=args[N + 4]
+            )
+            gr.Info("Thank you for providing feedback!")
+        except Exception as ex:
+            if hasattr(ex, "response"):
+                error_msg = ex.response.json().get("response", {}).get("error")
+                raise gr.Error(f"Failed to submit feedback: {error_msg}")
+            raise gr.Error("Failed to submit feedback")
+
+    feedback_components = []
+    with gr.Blocks(theme=gr.themes.Soft(), title="Candid AI demo") as demo:
+        gr.Markdown("""
+            <h1>Help us improve this tool with your valuable feedback</h1>
+
+            Please provide feedback for the recommendations on the previous tab.
+
+            It is not required to provide feedback on all recommendations before submitting.
+            """
+        )
+
+        with gr.Row():
+            with gr.Column():
+                with gr.Group():
+                    for i in range(N):
+                        f = single_recommendation_response(i, rec_type=rec_type)
+                        feedback_components.append(f)
+                        if "ratings" not in components:
+                            components["ratings"] = [f]
+                        else:
+                            components["ratings"].append(f)
+
+                correctness = gr.Radio(
+                    choices=["True", "False"],
+                    label="Information is correct?",
+                    info="Are the displayed RFP details correct?"
+                )
+                sufficiency = gr.Radio(
+                    choices=["True", "False"],
+                    label="Sufficient data?",
+                    info="Is enough RFP data available to provide meaningful recommendations?"
+                )
+
+                comment = gr.Textbox(label="Additional comments (optional)", lines=4)
+                email = gr.Textbox(label="Your email (optional)", lines=1)
+
+                components["correctness"] = correctness
+                components["sufficiency"] = sufficiency
+                components["comments"] = comment
+                components["email"] = email
+
+                with gr.Row():
+                    submit = gr.Button("Submit Feedback", variant='primary', scale=5)
+                    gr.ClearButton(components=feedback_components, variant="stop")
+
+        # pylint: disable=no-member
+        submit.click(
+            fn=handle_feedback,
+            inputs=[comp for k, cl in components.items() for comp in (cl if isinstance(cl, list) else [cl])],
+            outputs=None,
+            show_api=False,
+            api_name=False,
+            preprocess=False,
+        )
     return demo
 
 
+
+def build_demo():
+    logger, recommender = build_recommender()
+    feedback = build_feedback(logger)
+    return gr.TabbedInterface(
+        interface_list=[recommender, feedback],
+        tab_names=["RFP recommendations", "Feedback"],
+        title="Candid's RFP recommendation engine",
+        theme=gr.themes.Soft()
+    )
+
+
 if __name__ == '__main__':
     app = build_demo()
     app.queue(max_size=5).launch(