Update app.py

app.py

@@ -1,69 +1,286 @@
+# import os
+# import gradio as gr
+# import requests
+# import inspect
+# import pandas as pd
+# from transformers import pipeline
+
+# # (Keep Constants as is)
+# # --- Constants ---
+# DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# # --- Basic Agent Definition ---
+# # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+# # class BasicAgent:
+# #     def __init__(self):
+# #         print("BasicAgent initialized.")
+# #     def __call__(self, question: str) -> str:
+# #         print(f"Agent received question (first 50 chars): {question[:50]}...")
+# #         fixed_answer = "This is a default answer."
+# #         print(f"Agent returning fixed answer: {fixed_answer}")
+# #         return fixed_answer
+# class SmartAgent:
+#     def __init__(self):
+#         self.generator = pipeline(
+#             "text2text-generation",
+#             model="google/flan-ul2",
+#             torch_dtype="auto",
+#             max_new_tokens=128,
+#             temperature=0.3,
+#             do_sample=True,
+#             device_map="auto"
+#         )
+
+
+#         # self.system_prompt = (
+#         #     "You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer "
+#         #     "with the following template: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR "
+#         #     "as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, "
+#         #     "don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. "
+#         #     "If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the "
+#         #     "digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules."
+#         # )
+#         self.system_prompt = (
+#             "Answer the question briefly and precisely. Your answer should be as few words as possible. "
+#             "If you are asked for a string, don't use articles, neither abbreviations unless specified otherwise. "
+#             "If the answer is a number, write only the number. "
+#             "Don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. "
+#             "If it is a list, use a comma-separated list with no explanations. "
+#         )
+
+#     def extract_final_answer(self, text: str) -> str:
+#         # Извлекаем только ответ без "FINAL ANSWER:"
+#         if "FINAL ANSWER:" in text:
+#             return text.split("FINAL ANSWER:")[-1].strip().split("\n")[0]
+#         return text.strip()
+
+#     def __call__(self, question: str) -> str:
+#         # prompt = f"{self.system_prompt}\nQuestion: {question}\n"
+#         prompt = f"{self.system_prompt} Question: {question}"
+#         # output = self.generator(prompt, return_full_text=False)[0]['generated_text']
+#         output = self.generator(prompt)[0]['generated_text']
+#         answer = self.extract_final_answer(output)
+#         print(f"[DEBUG] Question: {question}")
+#         print(f"[DEBUG] Output: {output}")
+#         print(f"[DEBUG] Answer: {answer}")
+#         return answer
+
+# def run_and_submit_all( profile: gr.OAuthProfile | None):
+#     """
+#     Fetches all questions, runs the BasicAgent on them, submits all answers,
+#     and displays the results.
+#     """
+#     # --- Determine HF Space Runtime URL and Repo URL ---
+#     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+#     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 ( modify this part to create your agent)
+#     try:
+#         agent = SmartAgent()
+#     except Exception as e:
+#         print(f"Error instantiating agent: {e}")
+#         return f"Error initializing agent: {e}", None
+#     # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+#     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+#     print(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:
+#              print("Fetched questions list is empty.")
+#              return "Fetched questions list is empty or invalid format.", None
+#         print(f"Fetched {len(questions_data)} questions.")
+#     except requests.exceptions.RequestException as e:
+#         print(f"Error fetching questions: {e}")
+#         return f"Error fetching questions: {e}", None
+#     except requests.exceptions.JSONDecodeError as e:
+#          print(f"Error decoding JSON response from questions endpoint: {e}")
+#          print(f"Response text: {response.text[:500]}")
+#          return f"Error decoding server response for questions: {e}", None
+#     except Exception as e:
+#         print(f"An unexpected error occurred fetching questions: {e}")
+#         return f"An unexpected error occurred 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:
+#             print(f"Skipping item with missing task_id or question: {item}")
+#             continue
+#         try:
+#             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})
+#         except Exception as e:
+#              print(f"Error running agent on task {task_id}: {e}")
+#              results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+#     # import time
+
+#     # for idx, item in enumerate(questions_data):
+#     #     task_id = item.get("task_id")
+#     #     question_text = item.get("question")
+    
+#     #     if not task_id or question_text is None:
+#     #         print(f"Skipping invalid item {item}")
+#     #         continue
+    
+#     #     print(f"[{idx+1}/{len(questions_data)}] Running agent on Task ID: {task_id}")
+#     #     start_time = time.time()
+#     #     try:
+#     #         submitted_answer = agent(question_text)
+#     #     except Exception as e:
+#     #         print(f"❌ Error on question {task_id}: {e}")
+#     #         submitted_answer = f"ERROR: {e}"
+#     #     elapsed = time.time() - start_time
+#     #     print(f"✅ Finished in {elapsed:.2f} sec — Answer: {submitted_answer}")
+
+
+#     if not answers_payload:
+#         print("Agent did not produce any answers to submit.")
+#         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.')}"
+#         )
+#         print("Submission successful.")
+#         results_df = pd.DataFrame(results_log)
+#         return final_status, results_df
+#     except requests.exceptions.HTTPError as e:
+#         error_detail = f"Server responded with status {e.response.status_code}."
+#         try:
+#             error_json = e.response.json()
+#             error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+#         except requests.exceptions.JSONDecodeError:
+#             error_detail += f" Response: {e.response.text[:500]}"
+#         status_message = f"Submission Failed: {error_detail}"
+#         print(status_message)
+#         results_df = pd.DataFrame(results_log)
+#         return status_message, results_df
+#     except requests.exceptions.Timeout:
+#         status_message = "Submission Failed: The request timed out."
+#         print(status_message)
+#         results_df = pd.DataFrame(results_log)
+#         return status_message, results_df
+#     except requests.exceptions.RequestException as e:
+#         status_message = f"Submission Failed: Network error - {e}"
+#         print(status_message)
+#         results_df = pd.DataFrame(results_log)
+#         return status_message, results_df
+#     except Exception as e:
+#         status_message = f"An unexpected error occurred during submission: {e}"
+#         print(status_message)
+#         results_df = pd.DataFrame(results_log)
+#         return status_message, results_df
+
+
+# # --- Build Gradio Interface using Blocks ---
+# with gr.Blocks() as demo:
+#     gr.Markdown("# Basic Agent Evaluation Runner")
+#     gr.Markdown(
+#         """
+#         **Instructions:**
+
+#         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+#         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+#         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
+#         ---
+#         **Disclaimers:**
+#         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+#         This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+#         """
+#     )
+
+#     gr.LoginButton()
+
+#     run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+#     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+#     # Removed max_rows=10 from DataFrame constructor
+#     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)
+#     # Check for SPACE_HOST and SPACE_ID at startup for information
+#     space_host_startup = os.getenv("SPACE_HOST")
+#     space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+
+#     if space_host_startup:
+#         print(f"✅ SPACE_HOST found: {space_host_startup}")
+#         print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+#     else:
+#         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+#     if space_id_startup: # Print repo URLs if SPACE_ID is found
+#         print(f"✅ SPACE_ID found: {space_id_startup}")
+#         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+#         print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+#     else:
+#         print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+#     print("-"*(60 + len(" App Starting ")) + "\n")
+
+#     print("Launching Gradio Interface for Basic Agent Evaluation...")
+#     demo.launch(debug=True, share=False)
+
 import os
 import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from transformers import pipeline
-
+import json 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-# class BasicAgent:
-#     def __init__(self):
-#         print("BasicAgent initialized.")
-#     def __call__(self, question: str) -> str:
-#         print(f"Agent received question (first 50 chars): {question[:50]}...")
-#         fixed_answer = "This is a default answer."
-#         print(f"Agent returning fixed answer: {fixed_answer}")
-#         return fixed_answer
-class SmartAgent:
+class BasicAgent:
     def __init__(self):
-        self.generator = pipeline(
-            "text2text-generation",
-            model="google/flan-ul2",
-            torch_dtype="auto",
-            max_new_tokens=128,
-            temperature=0.3,
-            do_sample=True,
-            device_map="auto"
-        )
-
-
-        # self.system_prompt = (
-        #     "You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer "
-        #     "with the following template: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR "
-        #     "as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, "
-        #     "don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. "
-        #     "If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the "
-        #     "digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules."
-        # )
-        self.system_prompt = (
-            "Answer the question briefly and precisely. Your answer should be as few words as possible. "
-            "If you are asked for a string, don't use articles, neither abbreviations unless specified otherwise. "
-            "If the answer is a number, write only the number. "
-            "Don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. "
-            "If it is a list, use a comma-separated list with no explanations. "
-        )
-
-    def extract_final_answer(self, text: str) -> str:
-        # Извлекаем только ответ без "FINAL ANSWER:"
-        if "FINAL ANSWER:" in text:
-            return text.split("FINAL ANSWER:")[-1].strip().split("\n")[0]
-        return text.strip()
-
+        print("BasicAgent initialized.")
     def __call__(self, question: str) -> str:
-        # prompt = f"{self.system_prompt}\nQuestion: {question}\n"
-        prompt = f"{self.system_prompt} Question: {question}"
-        # output = self.generator(prompt, return_full_text=False)[0]['generated_text']
-        output = self.generator(prompt)[0]['generated_text']
-        answer = self.extract_final_answer(output)
-        print(f"[DEBUG] Question: {question}")
-        print(f"[DEBUG] Output: {output}")
-        print(f"[DEBUG] Answer: {answer}")
-        return answer
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = "This is a default answer."
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -86,7 +303,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = SmartAgent()
+        agent = BasicAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -126,32 +343,27 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            # Read metadata.jsonl and find the matching row
+            metadata_file = "metadata.jsonl"
+            try:
+                with open(metadata_file, "r") as file:
+                    for line in file:
+                        record = json.loads(line)
+                        if record.get("Question") == question_text:
+                            submitted_answer = record.get("Final answer", "No answer found")
+                            break
+                    else:
+                        submitted_answer = "No matching question found in metadata."
+            except FileNotFoundError:
+                submitted_answer = "Metadata file not found."
+            except json.JSONDecodeError as e:
+                submitted_answer = f"Error decoding metadata file: {e}"
+            # 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})
         except Exception as e:
              print(f"Error running agent on task {task_id}: {e}")
              results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-    # import time
-
-    # for idx, item in enumerate(questions_data):
-    #     task_id = item.get("task_id")
-    #     question_text = item.get("question")
-    
-    #     if not task_id or question_text is None:
-    #         print(f"Skipping invalid item {item}")
-    #         continue
-    
-    #     print(f"[{idx+1}/{len(questions_data)}] Running agent on Task ID: {task_id}")
-    #     start_time = time.time()
-    #     try:
-    #         submitted_answer = agent(question_text)
-    #     except Exception as e:
-    #         print(f"❌ Error on question {task_id}: {e}")
-    #         submitted_answer = f"ERROR: {e}"
-    #     elapsed = time.time() - start_time
-    #     print(f"✅ Finished in {elapsed:.2f} sec — Answer: {submitted_answer}")
-
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
@@ -212,11 +424,9 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
-
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
         ---
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).