Update app.py

app.py

@@ -1,154 +1,155 @@
-import os
-import gradio as gr
-import requests
-import pandas as pd
-from langchain import hub
-from langchain.agents import AgentExecutor, create_react_agent
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_huggingface import HuggingFaceEndpoint
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- New LangChain Agent Definition ---
-class LangChainAgent:
-    def __init__(self):
-        print("Initializing LangChainAgent...")
-
-        # 1. Set up the LLM (The "Brain") using the recommended HuggingFaceEndpoint
-        self.llm = HuggingFaceEndpoint(
-            repo_id="mistralai/Mistral-7B-Instruct-v0.2",
-            task="conversational",
-            max_new_tokens=512,
-            do_sample=False,
-        )
-        print("LLM initialized.")
-
-        # 2. Define the Tools
-        self.tools = [DuckDuckGoSearchRun()]
-        print("Tools initialized.")
-
-        # 3. Get the Prompt Template
-        self.prompt = hub.pull("hwchase17/react")
-        print("Prompt template pulled.")
-
-        # 4. Create the Agent
-        self.agent = create_react_agent(self.llm, self.tools, self.prompt)
-        print("Agent created.")
-
-        # 5. Create the Agent Executor
-        self.agent_executor = AgentExecutor(
-            agent=self.agent,
-            tools=self.tools,
-            verbose=True,
-            handle_parsing_errors=True,
-            max_iterations=5,
-        )
-        print("Agent Executor created. Initialization complete.")
-
-    def __call__(self, question: str) -> str:
-        """
-        This method is executed for each question.
-        """
-        print(f"Agent received question: {question}")
-        
-        try:
-            response = self.agent_executor.invoke({"input": question})
-            final_answer = response.get("output", "Error: Could not parse final answer.")
-        except Exception as e:
-            print(f"Error during agent execution: {e}")
-            final_answer = "Error: Agent failed to execute."
-            
-        print(f"Agent returning answer: {final_answer}")
-        return str(final_answer)
-
-# --- Main Application Logic (Uses our new agent) ---
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the LangChainAgent 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 = LangChainAgent()
-    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"
-
-    # 2. Fetch Questions
-    try:
-        response = requests.get(questions_url, timeout=20)
-        response.raise_for_status()
-        questions_data = response.json()
-    except Exception as e:
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        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})
-
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    
-    # 5. Submit
-    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:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-# --- Gradio Interface (Unchanged) ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Agent Evaluation Runner (LangChain Version)")
-    gr.Markdown(
-        """
-        **Instructions:**
-        1. Log in to your Hugging Face account using the button below.
-        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-        """
-    )
-    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__":
+import os
+import gradio as gr
+import requests
+import pandas as pd
+from langchain import hub
+from langchain.agents import AgentExecutor, create_react_agent
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_huggingface import HuggingFaceEndpoint
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- New LangChain Agent Definition ---
+class LangChainAgent:
+    def __init__(self):
+        print("Initializing LangChainAgent...")
+
+        # 1. Set up the LLM (The "Brain") using a stable model
+        self.llm = HuggingFaceEndpoint(
+            repo_id="google/flan-t5-xxl",
+            task="text-generation",
+            model_kwargs={
+                "max_new_tokens": 512,
+            }
+        )
+        print("LLM initialized with google/flan-t5-xxl.")
+
+        # 2. Define the Tools
+        self.tools = [DuckDuckGoSearchRun()]
+        print("Tools initialized.")
+
+        # 3. Get the Prompt Template
+        self.prompt = hub.pull("hwchase17/react")
+        print("Prompt template pulled.")
+
+        # 4. Create the Agent
+        self.agent = create_react_agent(self.llm, self.tools, self.prompt)
+        print("Agent created.")
+
+        # 5. Create the Agent Executor
+        self.agent_executor = AgentExecutor(
+            agent=self.agent,
+            tools=self.tools,
+            verbose=True,
+            handle_parsing_errors=True,
+            max_iterations=5,
+        )
+        print("Agent Executor created. Initialization complete.")
+
+    def __call__(self, question: str) -> str:
+        """
+        This method is executed for each question.
+        """
+        print(f"Agent received question: {question}")
+        
+        try:
+            response = self.agent_executor.invoke({"input": question})
+            final_answer = response.get("output", "Error: Could not parse final answer.")
+        except Exception as e:
+            print(f"Error during agent execution: {e}")
+            final_answer = "Error: Agent failed to execute."
+            
+        print(f"Agent returning answer: {final_answer}")
+        return str(final_answer)
+
+# --- Main Application Logic (Uses our new agent) ---
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the LangChainAgent 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 = LangChainAgent()
+    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"
+
+    # 2. Fetch Questions
+    try:
+        response = requests.get(questions_url, timeout=20)
+        response.raise_for_status()
+        questions_data = response.json()
+    except Exception as e:
+        return f"An unexpected error occurred fetching questions: {e}", None
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        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})
+
+    # 4. Prepare Submission 
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    
+    # 5. Submit
+    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:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+# --- Gradio Interface (Unchanged) ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Agent Evaluation Runner (LangChain Version)")
+    gr.Markdown(
+        """
+        **Instructions:**
+        1. Log in to your Hugging Face account using the button below.
+        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+        """
+    )
+    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()