Update app.py

app.py

@@ -6,55 +6,105 @@ from utils.generator import generate
 # ---------------------------------------------------------------------
 
 
-# Only run once to download full requirements
 import subprocess
 from pathlib import Path
+import gradio as gr
+from utils.generator import generate
+
+# File to store frozen packages
+FREEZE_FILE = "requirements_full.txt"
 
 def dump_installed_packages():
-    output_file = Path("requirements_full.txt")
+    """Generate the list of installed packages"""
     try:
-        with output_file.open("w") as f:
+        with open(FREEZE_FILE, "w") as f:
             subprocess.run(["pip", "freeze"], stdout=f, check=True)
-        print(f"[INFO] requirements_full.txt created at: {output_file.resolve()}")
+        return FREEZE_FILE
     except Exception as e:
-        print(f"[ERROR] Could not write requirements: {e}")
+        return f"Error generating requirements list: {e}"
 
+# Generate once at startup
 dump_installed_packages()
 
+# ---------------------------------------------------------------------
+# Unified Gradio App (Blocks with Interface + File)
+# ---------------------------------------------------------------------
 
+with gr.Blocks() as demo:
+    gr.Markdown("📦 Download the list of installed Python packages:")
+    gr.File(value=FREEZE_FILE, label="requirements_full.txt")
 
-ui = gr.Interface(
-    fn=generate,
-    inputs=[
-        gr.Textbox(
-            label="Query", 
-            lines=2, 
-            placeholder="Enter query here",
-            info="The query to search for in the vector database"
-        ),
-        gr.Textbox(
-            label="Context", 
-            lines=8, 
-            placeholder="Paste relevant context here",
-            info="Provide the context/documents to use for answering. The API expects a list of dictionaries, but the UI should except anything"
+    gr.Markdown("## 🔍 ChatFed Generation Module")
+
+    ui = gr.Interface(
+        fn=generate,
+        inputs=[
+            gr.Textbox(
+                label="Query", 
+                lines=2, 
+                placeholder="Enter query here",
+                info="The query to search for in the vector database"
+            ),
+            gr.Textbox(
+                label="Context", 
+                lines=8, 
+                placeholder="Paste relevant context here",
+                info="Provide the context/documents to use for answering."
+            ),
+        ],
+        outputs=gr.Textbox(
+            label="Generated Answer",
+            lines=6,
+            show_copy_button=True
         ),
-    ],
-    outputs=gr.Textbox(
-        label="Generated Answer",
-        lines=6,
-        show_copy_button=True
-    ),
-    title="ChatFed Generation Module",
-    description="Ask questions based on provided context. Intended for use in RAG pipelines as an MCP server with other ChatFed modules (i.e. context supplied by semantic retriever service).",
-    api_name="generate"
-)
-
-# Launch with MCP server enabled
+        api_name="generate"
+    )
+
+    ui.render()
+
+# Launch with MCP support
 if __name__ == "__main__":
-    ui.launch(
+    demo.launch(
         server_name="0.0.0.0",
         server_port=7860,
         mcp_server=True,
         show_error=True
     )
 
+
+
+# ui = gr.Interface(
+#     fn=generate,
+#     inputs=[
+#         gr.Textbox(
+#             label="Query", 
+#             lines=2, 
+#             placeholder="Enter query here",
+#             info="The query to search for in the vector database"
+#         ),
+#         gr.Textbox(
+#             label="Context", 
+#             lines=8, 
+#             placeholder="Paste relevant context here",
+#             info="Provide the context/documents to use for answering. The API expects a list of dictionaries, but the UI should except anything"
+#         ),
+#     ],
+#     outputs=gr.Textbox(
+#         label="Generated Answer",
+#         lines=6,
+#         show_copy_button=True
+#     ),
+#     title="ChatFed Generation Module",
+#     description="Ask questions based on provided context. Intended for use in RAG pipelines as an MCP server with other ChatFed modules (i.e. context supplied by semantic retriever service).",
+#     api_name="generate"
+# )
+
+# # Launch with MCP server enabled
+# if __name__ == "__main__":
+#     ui.launch(
+#         server_name="0.0.0.0",
+#         server_port=7860,
+#         mcp_server=True,
+#         show_error=True
+#     )
+