Update app.py

app.py

@@ -6,60 +6,34 @@ from utils.generator import generate
 # ---------------------------------------------------------------------
 
 
-import subprocess
-from pathlib import Path
-import gradio as gr
-
-FREEZE_FILE = "requirements_full.txt"
-
-def dump_installed_packages():
-    """Generate the list of installed packages into a file."""
-    try:
-        with open(FREEZE_FILE, "w") as f:
-            subprocess.run(["pip", "freeze"], stdout=f, check=True)
-    except Exception as e:
-        print(f"[ERROR] Could not write requirements: {e}")
-
-# Generate it once at runtime
-dump_installed_packages()
-
-with gr.Blocks() as demo:
-    gr.Markdown("📦 Download the list of installed Python packages:")
-    gr.File(value=FREEZE_FILE, label="requirements_full.txt")
-
-# IMPORTANT: This is required in Hugging Face Spaces using Docker
+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.Text(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__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-
-
-# 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.Text(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
-#     )
+    ui.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        #mcp_server=True,
+        show_error=True
+    )