Update app.py

app.py

@@ -1,82 +1,89 @@
 import os
 import requests
 import gradio as gr
+import base64
+import io
+from PIL import Image
 
-# 1) Grab your personal (read-access) token from HF Secrets
+#
+# 1) Retrieve your Hugging Face token (with read access) for the private space
+#
 HF_TOKEN = os.environ.get("HF_TOKEN")
 HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
 
-# 2) Root URL for your PRIVATE space (jtdearmon/rag_dal_budget)
-#    Note how the subdomain is: jtdearmon--rag_dal_budget.hf.space
+#
+# 2) Private space endpoint:
+#    The subdomain is typically "<username>--<space_name>.hf.space"
+#
 PRIVATE_SPACE_ROOT = "https://jtdearmon--rag_dal_budget.hf.space"
 
+#
+# 3) Utility to call a specific Gradio function index (0, 1, 2...) in the private space.
+#
 def call_private_space(function_index, data_list):
     """
-    A helper that sends a POST request to one of the Gradio endpoints
-    in the private rag_dal_budget space.
-
-    By default:
-      - The 1st .click() function is at /run/predict
-      - The 2nd .click() function is at /run/predict_1
-      - 3rd at /run/predict_2
+    Sends a POST request to the private Space's Gradio endpoint:
+      - function_index=0 => /run/predict
+      - function_index=1 => /run/predict_1
       etc.
-
-    Inputs:
-      function_index: 0 for run_query, 1 for handle_feedback, etc.
-      data_list:      The 'data' array to send, matching the function’s inputs.
-
-    Returns:
-      The first output in the JSON payload (result["data"][0]) or an error message.
+    data_list => the "data": [...] array matching that function’s inputs
     """
-    # Build the correct endpoint: /run/predict, /run/predict_1, /run/predict_2...
     url = f"{PRIVATE_SPACE_ROOT}/run/predict"
     if function_index > 0:
         url += f"_{function_index}"
 
-    # Gradio expects a JSON body of the form {"data": [...]}
     payload = {"data": data_list}
     try:
         resp = requests.post(url, headers=HEADERS, json=payload)
         resp.raise_for_status()
         json_resp = resp.json()
-        # Typically, the first (and often only) return value is in json_resp["data"][0]
+        # Typically the function’s first return is json_resp["data"][0]
         return json_resp["data"][0]
     except Exception as e:
-        return f"Error: {e}"
+        return f"Error calling private space: {e}"
 
-# -------------------------------------------------------------------------
-# Correspond to the private Space’s two main interactive functions:
-#    1) run_query(user_query, use_summary)
-#    2) handle_feedback(user_query, answer, feedback_option)
-# -------------------------------------------------------------------------
+#
+# 4) The two main interactive functions from your private code:
+#    - run_query(user_query, use_summary)
+#    - handle_feedback(user_query, answer, feedback_option)
+#
 def run_query(user_query, use_summary):
-    """
-    Calls the first Gradio function (index 0) in the private space,
-    which is the 'run_query' pipeline.
-    """
     return call_private_space(0, [user_query, use_summary])
 
 def handle_feedback(user_query, answer, feedback_option):
-    """
-    Calls the second Gradio function (index 1) in the private space,
-    which is 'handle_feedback'.
-    """
     return call_private_space(1, [user_query, answer, feedback_option])
 
-# -------------------------------------------------------------------------
-# Build a minimal Gradio UI that calls the above 2 endpoints
-# -------------------------------------------------------------------------
-with gr.Blocks() as demo:
-    gr.Markdown("## Public Interface → Private `rag_dal_budget`")
-
-    gr.Markdown(
-        "This public Space relays requests to a **private** Gradio app "
-        "(`rag_dal_budget`) so the underlying code is hidden."
-    )
-
-    # Inputs for the first function
+#
+
+#
+# 6) Build the UI *layout* to mirror your private space.
+#
+demo = gr.Blocks()
+
+with demo:
+    # A row with the logo on the left, and a title on the right
+    with gr.Row():
+        with gr.Column(scale=1, min_width=100):
+            if DAL_LOGO_IMG:
+                gr.Image(
+                    value=DAL_LOGO_IMG,
+                    show_label=False,
+                    interactive=False,
+                    width=80,
+                    height=80
+                )
+        with gr.Column(scale=6):
+            gr.Markdown("## Dallas RAG + Graph Demo")
+            gr.Markdown(
+                "Loads node/edge data from a Dallas budget network. "
+                "You can **toggle** whether the subgraph is summarized via OpenAI "
+                "or used 'as is'. Only the last 5 queries are kept in the conversation."
+            )
+    
+    # User input row
     user_query = gr.Textbox(
         label="Your Query",
+        lines=1,
         placeholder="Ask about the Dallas budget..."
     )
     use_summary_chk = gr.Checkbox(
@@ -84,11 +91,9 @@ with gr.Blocks() as demo:
         label="Use Graph Summaries?"
     )
     get_answer_btn = gr.Button("Get Answer")
-
-    # Output from the first function
     answer_output = gr.Markdown(label="AI Answer")
 
-    # Feedback components for the second function
+    # Feedback row
     feedback_radio = gr.Radio(
         choices=["👍", "⚖️", "👎"],
         value="⚖️",
@@ -97,7 +102,7 @@ with gr.Blocks() as demo:
     feedback_btn = gr.Button("Submit Feedback")
     feedback_result = gr.Markdown()
 
-    # Wire up the .click() events
+    # Wire up the .click() events to the private space calls
     get_answer_btn.click(
         fn=run_query,
         inputs=[user_query, use_summary_chk],
@@ -109,4 +114,21 @@ with gr.Blocks() as demo:
         outputs=feedback_result
     )
 
-demo.launch()
+    # Footer, same as private space
+    gr.HTML(
+        """
+        <div style="text-align: center; margin-top: 20px;">
+            Built with 
+            <a href="https://gradio.app" target="_blank">Gradio</a>
+            &nbsp;|&nbsp;
+            Built by 
+            <a href="https://dearmonanalytics.com" target="_blank">Dearmon Analytics</a>
+        </div>
+        """
+    )
+
+#
+# 7) Launch
+#
+if __name__ == "__main__":
+    demo.launch()