feat: integrate QA dataset loading and display gold answers in Gradio interface

app.py

@@ -21,7 +21,7 @@ from src import (
     format_search_results,
     get_system_prompt,
 )
-from src.search_module import load_vectorstore, search
+from src.search_module import get_qa_dataset, load_vectorstore, search
 
 # TODO: check if can reuse tokenizer adapter
 
@@ -96,6 +96,20 @@ def create_interface(model_path: str, temperature: float = 0.7, system_prompt: s
     system_prompt = system_prompt or get_system_prompt()
     tokenizer_for_template = cast(PreTrainedTokenizer, tokenizer)
 
+    # Load QA dataset for examples and gold answers
+    try:
+        _, test_dataset = get_qa_dataset()
+        qa_map = {q: a for q, a in zip(test_dataset["prompt"], test_dataset["answer"])}
+        example_questions = list(qa_map.keys())
+        logger.info(f"Loaded {len(example_questions)} QA examples.")
+    except Exception as e:
+        logger.error(f"Failed to load QA dataset: {e}")
+        qa_map = {}
+        example_questions = [
+            "What year was the document approved by the Mission Evaluation Team?",
+            "Failed to load dataset examples.",
+        ]  # Provide fallback examples
+
     def get_chat_num_tokens(current_chat_state: dict) -> int:
         """Helper to get number of tokens in chat state."""
         try:
@@ -112,6 +126,7 @@ def create_interface(model_path: str, temperature: float = 0.7, system_prompt: s
         temp: float,
         max_iter: int = 20,
         num_search_results: int = 2,
+        gold_answer_state: str | None = None,
     ) -> Iterator[list[gr.ChatMessage]]:
         """Stream agent responses following agent.py/inference.py logic."""
         chat_state = {
@@ -326,6 +341,20 @@ def create_interface(model_path: str, temperature: float = 0.7, system_prompt: s
 
         logger.info(f"Processing finished in {total_time:.2f} seconds.")
 
+        # ---> Display Gold Answer if available (passed via gold_answer_state)
+        if gold_answer_state:
+            logger.info("Displaying gold answer.")
+            messages.append(
+                gr.ChatMessage(
+                    role="assistant",
+                    content=gold_answer_state,
+                    metadata={"title": "✅ Correct Answer (For comparison)"},
+                )
+            )
+            yield messages
+        else:
+            logger.info("No gold answer to display for this query.")
+
     with gr.Blocks(title="DeepSearch - Visible Thinking") as interface:
         gr.Markdown("# 🧠 DeepSearch with Visible Thinking")
         gr.Markdown("Watch as the AI thinks, searches, and processes information to answer your questions.")
@@ -345,13 +374,13 @@ def create_interface(model_path: str, temperature: float = 0.7, system_prompt: s
                     placeholder="Type your message here...", show_label=False, container=False, elem_id="msg-input"
                 )
 
-                example_questions = [
+                # Use questions from dataset as examples
-                    "What year was the document approved by the Mission Evaluation Team?",
+                gr.Examples(
-                    "Summarize the key findings regarding the oxygen tank failure.",
+                    examples=example_questions,
-                    "Who was the commander of Apollo 13?",
+                    inputs=msg,
-                    "What were the main recommendations from the review board?",
+                    label="Example Questions with correct answer for comparison",
-                ]
+                    examples_per_page=4,
-                gr.Examples(examples=example_questions, inputs=msg, label="Example Questions", examples_per_page=4)
+                )
 
                 with gr.Row():
                     clear = gr.Button("Clear Chat")
@@ -387,16 +416,24 @@ def create_interface(model_path: str, temperature: float = 0.7, system_prompt: s
             return "", history
 
         submitted_msg_state = gr.State("")
+        gold_answer_state = gr.State(None)  # State to hold the gold answer
+
+        # Function to check if submitted message is an example and store gold answer
+        def check_if_example_and_store_answer(msg_text):
+            gold_answer = qa_map.get(msg_text)  # Returns None if msg_text is not in examples
+            logger.info(f"Checking for gold answer for: '{msg_text[:50]}...'. Found: {bool(gold_answer)}")
+            return gold_answer
 
-        # Chain events:
-        # 1. User submits -> store msg text in state
-        # 2. .then() -> add_user_message (updates chatbot UI history, clears textbox)
-        # 3. .then() -> stream_agent_response (takes stored msg text and updated chatbot history)
         submit.click(
             lambda msg_text: msg_text,
             inputs=[msg],
             outputs=[submitted_msg_state],
             queue=False,
+        ).then(  # Check for gold answer immediately after storing submitted message
+            check_if_example_and_store_answer,
+            inputs=[submitted_msg_state],
+            outputs=[gold_answer_state],
+            queue=False,
         ).then(
             add_user_message,
             inputs=[submitted_msg_state, chatbot],
@@ -404,7 +441,14 @@ def create_interface(model_path: str, temperature: float = 0.7, system_prompt: s
             queue=False,
         ).then(
             stream_agent_response,
-            inputs=[submitted_msg_state, chatbot, temp_slider, max_iter_slider, num_results_slider],
+            inputs=[
+                submitted_msg_state,
+                chatbot,
+                temp_slider,
+                max_iter_slider,
+                num_results_slider,
+                gold_answer_state,
+            ],  # Pass gold answer state
             outputs=chatbot,
         )
 
@@ -413,6 +457,11 @@ def create_interface(model_path: str, temperature: float = 0.7, system_prompt: s
             inputs=[msg],
             outputs=[submitted_msg_state],
             queue=False,
+        ).then(  # Check for gold answer immediately after storing submitted message
+            check_if_example_and_store_answer,
+            inputs=[submitted_msg_state],
+            outputs=[gold_answer_state],
+            queue=False,
         ).then(
             add_user_message,
             inputs=[submitted_msg_state, chatbot],
@@ -420,11 +469,18 @@ def create_interface(model_path: str, temperature: float = 0.7, system_prompt: s
             queue=False,
         ).then(
             stream_agent_response,
-            inputs=[submitted_msg_state, chatbot, temp_slider, max_iter_slider, num_results_slider],
+            inputs=[
+                submitted_msg_state,
+                chatbot,
+                temp_slider,
+                max_iter_slider,
+                num_results_slider,
+                gold_answer_state,
+            ],  # Pass gold answer state
             outputs=chatbot,
         )
 
-        clear.click(lambda: ([], ""), None, [chatbot, submitted_msg_state])
+        clear.click(lambda: ([], None), None, [chatbot, gold_answer_state])  # Also clear gold answer state
 
         system_prompt_state = gr.State(system_prompt)
         # TODO: Currently, changing the system prompt mid-chat won't affect the ongoing stream_agent_response.

pyproject.toml

@@ -23,8 +23,8 @@ dependencies = [
     "langchain-community",
     "Markdown",
     "tokenizers",
-    "unsloth==2025.2.14",
+    "unsloth",
-    "unsloth_zoo==2025.2.7",
+    "unsloth_zoo",
     "unstructured",
     "vllm==0.7.2",
     "transformers==4.49.0",
@@ -34,5 +34,7 @@ dependencies = [
     "gradio",
     "tensorboard",
     "pytest",
-    "wandb"
+    "wandb",
+    "requests>=2.31.0",
+    "tqdm>=4.66.1"
 ]