Update app.py

app.py

@@ -1,128 +1,36 @@
-import os
-import requests
-import gradio as gr
-
-#
-# 1) Retrieve your Hugging Face token (with read or write access) for the private space
-#
-HF_TOKEN = os.getenv("HF_TOKEN", "").strip()
-HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-#
-# 2) Private space endpoint
-#
-# Make sure this EXACT domain matches "Embed this Space" or "View App" from your private space.
-# If your private space is "jtdearmon-rag-dal-budget",
-# and the embed link is "https://jtdearmon-rag-dal-budget.hf.space",
-# then use that domain here:
-#
-PRIVATE_SPACE_ROOT = "https://dearmonanalytics-rag-dallas-budget-private"
-
-#
-# 3) Utility to call a specific Gradio function in the private space.
-#    function_index=0 => /run/predict
-#    function_index=1 => /run/predict_1
-#
-def call_private_space(function_index, data_list):
-    """
-    Sends a POST request to the private Space's Gradio endpoints:
-      - function_index=0 => /run/predict
-      - function_index=1 => /run/predict_1
-    data_list => the "data": [...] array matching that function’s inputs
-    """
-    url = f"{PRIVATE_SPACE_ROOT}/run/predict"
-    if function_index > 0:
-        url += f"_{function_index}"
+# run_encrypted.py
 
-    payload = {"data": data_list}
-    try:
-        # If you see SSL errors, keep verify=False. 
-        # If you'd like to enable normal SSL checks, set verify=True or remove it:
-        resp = requests.post(url, headers=HEADERS, json=payload, verify=False)
-        resp.raise_for_status()  # Raises HTTPError on 4xx/5xx
-        json_resp = resp.json()
-        # Typically the function’s first return is in json_resp["data"][0]
-        return json_resp["data"][0]
-    except Exception as e:
-        return f"Error calling private space: {e}"
-
-#
-# 4) The two main interactive functions that mirror your private code:
-#
-def run_query(user_query, use_summary):
-    # This calls /run/predict (function_index=0)
-    return call_private_space(0, [user_query, use_summary])
-
-def handle_feedback(user_query, answer, feedback_option):
-    # This calls /run/predict_1 (function_index=1)
-    return call_private_space(1, [user_query, answer, feedback_option])
+import os
+import base64
+from cryptography.fernet import Fernet
 
-#
-# 5) Build a UI to demonstrate the calls
-#
-with gr.Blocks() as demo:
-    # Top row
-    with gr.Row():
-        with gr.Column(scale=1, min_width=100):
-            gr.Image(
-                value="dal_logo.png",
-                show_label=False,
-                interactive=False,
-                width=80,
-                height=80
-            )
-        with gr.Column(scale=6):
-            gr.Markdown("## Dallas Budget RAG Model - Public Space")
-            gr.Markdown(
-                "Calls the private Space for actual processing."
-            )
+# Replace this with the ciphertext from your local encryption step:
+ENCRYPTED_CODE_B64 = """
+<PASTE THAT MASSIVE BASE64 STRING HERE>
+""".strip()
 
-    user_query = gr.Textbox(
-        label="Your Query",
-        lines=1,
-        placeholder="Ask about the Dallas budget..."
-    )
-    use_summary_chk = gr.Checkbox(
-        value=False,
-        visible=False,
-        label="Use Graph Summaries?"
-    )
-    get_answer_btn = gr.Button("Get Answer")
+def main():
+    # 1) Read the key from environment
+    secret_key = os.getenv("ENCRYPTION_KEY")
+    if not secret_key:
+        raise ValueError("Missing ENCRYPTION_KEY in environment variables!")
 
-    # Output area
-    answer_output = gr.Markdown(label="AI Answer")
+    # 2) Decode from Base64 -> bytes
+    ciphertext = base64.b64decode(ENCRYPTED_CODE_B64)
 
-    # Feedback
-    feedback_radio = gr.Radio(
-        choices=["👍", "⚖️", "👎"],
-        value="⚖️",
-        label="Feedback"
-    )
-    feedback_btn = gr.Button("Submit Feedback")
-    feedback_result = gr.Markdown()
+    # 3) Decrypt with the provided key
+    fernet = Fernet(secret_key.encode())
+    decrypted_bytes = fernet.decrypt(ciphertext)
 
-    # Wire the buttons
-    get_answer_btn.click(
-        fn=run_query,
-        inputs=[user_query, use_summary_chk],
-        outputs=answer_output
-    )
-    feedback_btn.click(
-        fn=handle_feedback,
-        inputs=[user_query, answer_output, feedback_radio],
-        outputs=feedback_result
-    )
+    # 4) Execute in memory
+    #    If your code defines a Gradio demo, it should be launched here
+    exec_globals = {}
+    exec(decrypted_bytes, exec_globals)
 
-    # Footer
-    gr.HTML(
-        """
-        <div style="text-align: center; margin-top: 20px;">
-            Built by 
-            <a href="https://dearmonanalytics.com" target="_blank">Dearmon Analytics</a> | 
-            <a href="https://huggingface.co/spaces" target="_blank">HF Spaces</a>
-        </div>
-        """
-    )
+    # If your decrypted script defines a 'demo' (gr.Blocks),
+    # it might automatically launch. If not, call:
+    if "demo" in exec_globals:
+        exec_globals["demo"].launch(server_name="0.0.0.0", server_port=7860)
 
 if __name__ == "__main__":
-    demo.launch()
+    main()