Update app.py

app.py

@@ -34,7 +34,6 @@ def transcribe_audio_from_task_id(task_id: str) -> str:
         audio_response.raise_for_status()
 
         # Step 2: Prepare the file for the Groq API
-        # The API expects a file-like object with a name.
         audio_bytes = BytesIO(audio_response.content)
         audio_bytes.name = f"{task_id}.mp3" # Give the file-like object a name
 
@@ -63,4 +62,115 @@ def transcribe_audio_from_task_id(task_id: str) -> str:
 class LangChainAgent:
     def __init__(self, groq_api_key: str, tavily_api_key: str):
         print("Initializing LangChainAgent...")
-        self.llm = ChatGroq(model_name="llama3-70b-8192", groq_api_key=groq
+        
+        # THIS IS THE CORRECTED LINE
+        self.llm = ChatGroq(model_name="llama3-70b-8192", groq_api_key=groq_api_key, temperature=0.0)
+
+        # Define all available tools
+        audio_tool = Tool(
+            name="audio_transcriber",
+            func=transcribe_audio_from_task_id,
+            description="Use this tool to transcribe an audio file. The input must be the task_id of the question.",
+        )
+        self.tools = [
+            TavilySearchResults(max_results=3, tavily_api_key=tavily_api_key, name="web_search"),
+            audio_tool,
+        ]
+        
+        # Define the strict system prompt
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", (
+                "You are a powerful problem-solving agent. Your goal is to answer the user's question accurately. "
+                "You have access to the following tools: a web search tool and an audio transcription tool.\n"
+                "RULES:\n"
+                "- Carefully analyze the user's question to determine if a tool is needed.\n"
+                "- For questions requiring current information or facts, use the 'web_search' tool.\n"
+                "- For questions that mention an audio file (.mp3, recording, voice memo, etc.), use the 'audio_transcriber' tool with the provided 'task_id'.\n"
+                "- Once you have all the necessary information, you MUST provide ONLY THE FINAL ANSWER to the user's question. Do not include any extra conversation, explanations, apologies, or introductory phrases."
+            )),
+            ("human", "Question: {input}\nTask ID: {task_id}"),
+            ("placeholder", "{agent_scratchpad}"),
+        ])
+
+        agent = create_tool_calling_agent(self.llm, self.tools, prompt)
+        self.agent_executor = AgentExecutor(agent=agent, tools=self.tools, verbose=True, handle_parsing_errors=True)
+        print("LangChainAgent initialized.")
+
+    def __call__(self, question: str, task_id: str) -> str:
+        print(f"Agent received question (ID: {task_id}): {question[:50]}...")
+        try:
+            response = self.agent_executor.invoke({"input": question, "task_id": task_id})
+            answer = response.get("output", "Agent failed to produce an answer.")
+        except Exception as e:
+            answer = f"Agent execution failed with an error: {e}"
+        print(f"Agent generated answer: {answer}")
+        return answer
+
+
+# --- Main Application Logic ---
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    space_id = os.getenv("SPACE_ID")
+    if not profile:
+        return "Please Login to Hugging Face with the button.", None
+    username = profile.username
+    print(f"User logged in: {username}")
+
+    try:
+        groq_api_key = os.getenv("GROQ_API_KEY")
+        tavily_api_key = os.getenv("TAVILY_API_KEY")
+        if not all([groq_api_key, tavily_api_key]):
+            raise ValueError("An API key secret (GROQ or TAVILY) is missing.")
+        agent = LangChainAgent(groq_api_key=groq_api_key, tavily_api_key=tavily_api_key)
+    except Exception as e:
+        return f"Error initializing agent: {e}", None
+
+    questions_url = f"{DEFAULT_API_URL}/questions"
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=20)
+        response.raise_for_status()
+        questions_data = response.json()
+    except Exception as e:
+        return f"Error fetching questions: {e}", None
+
+    results_log, answers_payload = [], []
+    for item in questions_data:
+        task_id, question_text = item.get("task_id"), item.get("question")
+        if not task_id or not question_text: continue
+        submitted_answer = agent(question=question_text, task_id=task_id)
+        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})
+
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
+    submit_url = f"{DEFAULT_API_URL}/submit"
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=90) # Increased timeout
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (f"Submission Successful!\nUser: {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.')}")
+        return final_status, pd.DataFrame(results_log)
+    except Exception as e:
+        return f"Submission Failed: {e}", pd.DataFrame(results_log)
+
+
+# --- Gradio Interface ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Advanced Agent Evaluation Runner (Search + Groq Audio)")
+    gr.Markdown("This agent can search the web with Tavily and transcribe audio with Groq's Whisper.")
+    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" + "-"*30 + " App Starting " + "-"*30)
+    for key in ["GROQ_API_KEY", "TAVILY_API_KEY"]:
+        print(f"✅ {key} secret is set." if os.getenv(key) else f"⚠️ WARNING: {key} secret is not set.")
+    print("-"*(60 + len(" App Starting ")) + "\n")
+    demo.launch(debug=True, share=False)