build error for audio fixed

Gradio_UI.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import json
+from agent import build_graph
+from langchain_core.messages import HumanMessage
+
+# Load questions from metadata.jsonl
+def load_questions(jsonl_path):
+	questions = []
+	with open(jsonl_path, 'r', encoding='utf-8') as f:
+		for line in f:
+			try:
+				obj = json.loads(line)
+				if 'Question' in obj:
+					questions.append(obj['Question'])
+			except Exception:
+				continue
+	return questions
+
+questions_list = load_questions('metadata.jsonl')
+# Basic filter for violence/intimate (simple keyword check)
+def is_safe_question(q):
+	unsafe_keywords = ['kill', 'murder', 'sex', 'intimate', 'violence', 'abuse']
+	q_lower = q.lower()
+	return not any(word in q_lower for word in unsafe_keywords)
+
+# Backend logic: send question to agent
+def get_answer(question, history):
+	if not is_safe_question(question):
+		return "Sorry, this question is not allowed.", history
+	try:
+		graph = build_graph(provider="groq")
+		messages = [HumanMessage(content=question)]
+		result = graph.invoke({"messages": messages})
+		# Get last message as answer
+		answer = result["messages"][-1].content if result["messages"] else "No answer."
+		history = history + [(question, answer)]
+		return answer, history
+	except Exception as e:
+		return f"Error: {str(e)}", history
+
+# Gradio UI
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="purple")) as demo:
+	gr.Markdown("# AI Agent Q&A", elem_id="title")
+	gr.Markdown("Ask your own question or select one from the list below.")
+	with gr.Row():
+		question_box = gr.Textbox(label="Type your question", lines=3)
+		question_list = gr.Dropdown(choices=questions_list, label="Or choose a question", interactive=True)
+	submit_btn = gr.Button("Submit", elem_id="submit-btn")
+	reset_btn = gr.Button("Reset", elem_id="reset-btn")
+	answer_box = gr.Textbox(label="Answer", interactive=False)
+	with gr.Accordion("Show previous Q&A", open=False):
+		history_box = gr.Dataframe(headers=["Question", "Answer"], datatype=["str", "str"], interactive=False)
+
+	state = gr.State([])
+	def submit_fn(q_text, q_list, history):
+		question = q_text if q_text else q_list
+		if not question:
+			return "Please enter or select a question.", history
+		return get_answer(question, history)
+
+	def reset_fn():
+		return "", []
+
+	submit_btn.click(
+	submit_fn,
+	inputs=[question_box, question_list, state],
+	outputs=[answer_box, state],
+		api_name="submit",
+	)
+	reset_btn.click(
+	reset_fn,
+	inputs=[],
+	outputs=[answer_box, state],
+		api_name="reset",
+	)
+	state.change(lambda h: h, inputs=state, outputs=history_box)
+
+if __name__ == "__main__":
+	demo.launch()

README.md

@@ -6,7 +6,7 @@ colorTo: pink
 sdk: gradio
 sdk_version: 5.44.1
 hf_oauth: true
-app_file: app.py
+app_file: Gradio_UI.py
 pinned: false
 ---
 

app.py

@@ -1,79 +1,169 @@
+import os
 import gradio as gr
-import json
+import requests
+import pandas as pd
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Import your custom agent graph from agent.py ---
 from agent import build_graph
 from langchain_core.messages import HumanMessage
 
-# Load questions from metadata.jsonl
-def load_questions(jsonl_path):
-	questions = []
-	with open(jsonl_path, 'r', encoding='utf-8') as f:
-		for line in f:
-			try:
-				obj = json.loads(line)
-				if 'Question' in obj:
-					questions.append(obj['Question'])
-			except Exception:
-				continue
-	return questions
-
-questions_list = load_questions('metadata.jsonl')
-# Basic filter for violence/intimate (simple keyword check)
-def is_safe_question(q):
-	unsafe_keywords = ['kill', 'murder', 'sex', 'intimate', 'violence', 'abuse']
-	q_lower = q.lower()
-	return not any(word in q_lower for word in unsafe_keywords)
-
-# Backend logic: send question to agent
-def get_answer(question, history):
-	if not is_safe_question(question):
-		return "Sorry, this question is not allowed.", history
-	try:
-		graph = build_graph(provider="groq")
-		messages = [HumanMessage(content=question)]
-		result = graph.invoke({"messages": messages})
-		# Get last message as answer
-		answer = result["messages"][-1].content if result["messages"] else "No answer."
-		history = history + [(question, answer)]
-		return answer, history
-	except Exception as e:
-		return f"Error: {str(e)}", history
-
-# Gradio UI
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="purple")) as demo:
-	gr.Markdown("# AI Agent Q&A", elem_id="title")
-	gr.Markdown("Ask your own question or select one from the list below.")
-	with gr.Row():
-		question_box = gr.Textbox(label="Type your question", lines=3)
-		question_list = gr.Dropdown(choices=questions_list, label="Or choose a question", interactive=True)
-	submit_btn = gr.Button("Submit", elem_id="submit-btn")
-	reset_btn = gr.Button("Reset", elem_id="reset-btn")
-	answer_box = gr.Textbox(label="Answer", interactive=False)
-	with gr.Accordion("Show previous Q&A", open=False):
-		history_box = gr.Dataframe(headers=["Question", "Answer"], datatype=["str", "str"], interactive=False)
-
-	state = gr.State([])
-	def submit_fn(q_text, q_list, history):
-		question = q_text if q_text else q_list
-		if not question:
-			return "Please enter or select a question.", history
-		return get_answer(question, history)
-
-	def reset_fn():
-		return "", []
-
-	submit_btn.click(
-	submit_fn,
-	inputs=[question_box, question_list, state],
-	outputs=[answer_box, state],
-		api_name="submit",
-	)
-	reset_btn.click(
-	reset_fn,
-	inputs=[],
-	outputs=[answer_box, state],
-		api_name="reset",
-	)
-	state.change(lambda h: h, inputs=state, outputs=history_box)
+# --- Basic Agent Definition (wrapper around your graph) ---
+class BasicAgent:
+    def __init__(self):
+        print("Initializing BasicAgent with agent.py graph...")
+        self.graph = build_graph()
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question: {question[:50]}...")
+        try:
+            messages = [HumanMessage(content=question)]
+            result = self.graph.invoke({"messages": messages})
+            answer = result["messages"][-1].content
+            if answer.lower().startswith("final answer"):
+                answer = answer.split(":", 1)[-1].strip()
+            print(f"Agent returning: {answer}")
+            return answer
+        except Exception as e:
+            print(f"Error inside agent: {e}")
+            return f"AGENT ERROR: {e}"
+
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    space_id = os.getenv("SPACE_ID")
+
+    if profile:
+        username = f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "N/A"
+    print(f"Agent code repo: {agent_code}")
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+            return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
+    except Exception as e:
+        return f"Error fetching questions: {e}", None
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            continue
+        submitted_answer = agent(question_text)
+        answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+        results_log.append({
+            "Task ID": task_id,
+            "Question": question_text,
+            "Submitted Answer": submitted_answer
+        })
+
+    if not answers_payload:
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    # 4. Prepare Submission
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload,
+    }
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"Submission Successful!\n"
+            f"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', 'No message received.')}"
+        )
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except Exception as e:
+        return f"Submission Failed: {e}", pd.DataFrame(results_log)
+
+
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+
+        1. Clone this space and implement your logic in `agent.py`.
+        2. Log in with your Hugging Face account.
+        3. Click **Run Evaluation & Submit All Answers**.
+
+        ---
+        ⚠️ The process may take a while (agent needs to answer all questions).
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
 
 if __name__ == "__main__":
-	demo.launch()
+    print("\n--- App Starting ---")
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID")
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST: {space_host_startup}")
+        print(f"   Runtime URL: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️ SPACE_HOST not found (running locally?).")
+
+    if space_id_startup:
+        print(f"✅ SPACE_ID: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+    else:
+        print("ℹ️ SPACE_ID not found (running locally?).")
+
+    print("--------------------\n")
+    print("Launching Gradio Interface...")
+    demo.launch(debug=True, share=False)

evaluation_app.py

@@ -1,169 +0,0 @@
-import os
-import gradio as gr
-import requests
-import pandas as pd
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Import your custom agent graph from agent.py ---
-from agent import build_graph
-from langchain_core.messages import HumanMessage
-
-# --- Basic Agent Definition (wrapper around your graph) ---
-class BasicAgent:
-    def __init__(self):
-        print("Initializing BasicAgent with agent.py graph...")
-        self.graph = build_graph()
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question: {question[:50]}...")
-        try:
-            messages = [HumanMessage(content=question)]
-            result = self.graph.invoke({"messages": messages})
-            answer = result["messages"][-1].content
-            if answer.lower().startswith("final answer"):
-                answer = answer.split(":", 1)[-1].strip()
-            print(f"Agent returning: {answer}")
-            return answer
-        except Exception as e:
-            print(f"Error inside agent: {e}")
-            return f"AGENT ERROR: {e}"
-
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    space_id = os.getenv("SPACE_ID")
-
-    if profile:
-        username = f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "N/A"
-    print(f"Agent code repo: {agent_code}")
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-            return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except Exception as e:
-        return f"Error fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            continue
-        submitted_answer = agent(question_text)
-        answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-        results_log.append({
-            "Task ID": task_id,
-            "Question": question_text,
-            "Submitted Answer": submitted_answer
-        })
-
-    if not answers_payload:
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    # 4. Prepare Submission
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload,
-    }
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except Exception as e:
-        return f"Submission Failed: {e}", pd.DataFrame(results_log)
-
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-
-        1. Clone this space and implement your logic in `agent.py`.
-        2. Log in with your Hugging Face account.
-        3. Click **Run Evaluation & Submit All Answers**.
-
-        ---
-        ⚠️ The process may take a while (agent needs to answer all questions).
-        """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-
-if __name__ == "__main__":
-    print("\n--- App Starting ---")
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID")
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST: {space_host_startup}")
-        print(f"   Runtime URL: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️ SPACE_HOST not found (running locally?).")
-
-    if space_id_startup:
-        print(f"✅ SPACE_ID: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-    else:
-        print("ℹ️ SPACE_ID not found (running locally?).")
-
-    print("--------------------\n")
-    print("Launching Gradio Interface...")
-    demo.launch(debug=True, share=False)