added spaces import.

main.py

@@ -1,89 +1,90 @@
-# main.py
-
-import subprocess
-import sys
-import webbrowser
-import time
-import gradio as gr
-from question_runner import run_tool
-from config import MODEL_PRIORITY, SYNTAX_DOC_URL, MORPHOLOGY_DOC_URL
-from doc_utils import get_questions_from_doc
-
-# Auto-install required packages if missing
-def install_missing_packages():
-    from importlib.metadata import distributions
-    required = {"gradio", "requests"}
-    installed = {dist.metadata['Name'].lower() for dist in distributions()}
-    missing = required - installed
-
-    if missing:
-        print(f"Installing missing packages: {missing}")
-        subprocess.check_call([sys.executable, "-m", "pip", "install", *missing])
-
-install_missing_packages()
-
-# Estimate runtime based on # of questions
-def estimate_runtime(passage, doc_type):
-    if not passage.strip() or not doc_type:
-        return ""
-    doc_url = SYNTAX_DOC_URL if doc_type.lower() == "syntax" else MORPHOLOGY_DOC_URL
-    questions = get_questions_from_doc(doc_url)
-    if not questions or questions[0].startswith("Error"):
-        return "Unable to load questions."
-    est_seconds = round(len(questions) * 2.5, 1)
-    return f"Estimated generation time: ~{est_seconds} seconds"
-
-def launch_app():
-    with gr.Blocks(theme="soft") as demo:
-        gr.Markdown("""
-        ## **Classical Language Query Assistant**
-        Submit a Latin or Greek passage and select the question type.
-        Answers are generated using a rotating chain of hosted AI models via OpenRouter.
-
-        - Models are attempted in descending priority, starting from the most accurate.
-        - The model that answers each question is recorded in the response.
-        - Model quota or errors may trigger automatic fallback to the next-best option.
-        """)
-
-        with gr.Row():
-            passage_input = gr.Textbox(label="Latin or Greek Passage", lines=4)
-            question_type = gr.Radio(["Syntax", "Morphology"], label="Question Type")
-
-        top_model = MODEL_PRIORITY[0]
-        full_model_list = "\n".join(f"- `{m}`" for m in MODEL_PRIORITY)
-        demo_model_info = gr.Markdown(
-            f"""
-**Currently prioritized model:** `{top_model}`  
-**Model fallback chain (if needed):**  
-{full_model_list}
-""")
-
-        with gr.Row():
-            output_text = gr.Textbox(label="Generated Answers", lines=25, interactive=False)
-            output_file = gr.File(label="Download Answers (.txt)", interactive=False)
-
-        estimated_time_box = gr.Textbox(label="Estimated Time", interactive=False)
-
-        # Trigger time estimate dynamically
-        passage_input.change(fn=estimate_runtime, inputs=[passage_input, question_type], outputs=estimated_time_box)
-        question_type.change(fn=estimate_runtime, inputs=[passage_input, question_type], outputs=estimated_time_box)
-
-        submit_button = gr.Button("Generate Answers")
-
-        submit_button.click(
-            fn=run_tool,
-            inputs=[passage_input, question_type],
-            outputs=[output_text, output_file, estimated_time_box]
-        )
-
-        # Launch app and open browser
-        _, _, share_url = demo.launch(share=True, prevent_thread_lock=True)
-        if share_url:
-            webbrowser.open(share_url)
-
-        # Keep app running
-        while True:
-            time.sleep(1)
-
-if __name__ == "__main__":
-    launch_app()
+# main.py
+
+import subprocess
+import sys
+import webbrowser
+import time
+import gradio as gr
+import spaces
+from question_runner import run_tool
+from config import MODEL_PRIORITY, SYNTAX_DOC_URL, MORPHOLOGY_DOC_URL
+from doc_utils import get_questions_from_doc
+
+# Auto-install required packages if missing
+def install_missing_packages():
+    from importlib.metadata import distributions
+    required = {"gradio", "requests"}
+    installed = {dist.metadata['Name'].lower() for dist in distributions()}
+    missing = required - installed
+
+    if missing:
+        print(f"Installing missing packages: {missing}")
+        subprocess.check_call([sys.executable, "-m", "pip", "install", *missing])
+
+install_missing_packages()
+
+# Estimate runtime based on # of questions
+def estimate_runtime(passage, doc_type):
+    if not passage.strip() or not doc_type:
+        return ""
+    doc_url = SYNTAX_DOC_URL if doc_type.lower() == "syntax" else MORPHOLOGY_DOC_URL
+    questions = get_questions_from_doc(doc_url)
+    if not questions or questions[0].startswith("Error"):
+        return "Unable to load questions."
+    est_seconds = round(len(questions) * 2.5, 1)
+    return f"Estimated generation time: ~{est_seconds} seconds"
+
+def launch_app():
+    with gr.Blocks(theme="soft") as demo:
+        gr.Markdown("""
+        ## **Classical Language Query Assistant**
+        Submit a Latin or Greek passage and select the question type.
+        Answers are generated using a rotating chain of hosted AI models via OpenRouter.
+
+        - Models are attempted in descending priority, starting from the most accurate.
+        - The model that answers each question is recorded in the response.
+        - Model quota or errors may trigger automatic fallback to the next-best option.
+        """)
+
+        with gr.Row():
+            passage_input = gr.Textbox(label="Latin or Greek Passage", lines=4)
+            question_type = gr.Radio(["Syntax", "Morphology"], label="Question Type")
+
+        top_model = MODEL_PRIORITY[0]
+        full_model_list = "\n".join(f"- `{m}`" for m in MODEL_PRIORITY)
+        demo_model_info = gr.Markdown(
+            f"""
+**Currently prioritized model:** `{top_model}`  
+**Model fallback chain (if needed):**  
+{full_model_list}
+""")
+
+        with gr.Row():
+            output_text = gr.Textbox(label="Generated Answers", lines=25, interactive=False)
+            output_file = gr.File(label="Download Answers (.txt)", interactive=False)
+
+        estimated_time_box = gr.Textbox(label="Estimated Time", interactive=False)
+
+        # Trigger time estimate dynamically
+        passage_input.change(fn=estimate_runtime, inputs=[passage_input, question_type], outputs=estimated_time_box)
+        question_type.change(fn=estimate_runtime, inputs=[passage_input, question_type], outputs=estimated_time_box)
+
+        submit_button = gr.Button("Generate Answers")
+
+        submit_button.click(
+            fn=run_tool,
+            inputs=[passage_input, question_type],
+            outputs=[output_text, output_file, estimated_time_box]
+        )
+
+        # Launch app and open browser
+        _, _, share_url = demo.launch(share=True, prevent_thread_lock=True)
+        if share_url:
+            webbrowser.open(share_url)
+
+        # Keep app running
+        while True:
+            time.sleep(1)
+
+if __name__ == "__main__":
+    launch_app()