Update app.py

app.py

@@ -1,266 +1,246 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 from transformers import pipeline
 import re
-import math
 
-# (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 ------
+# -----------------------------------------
+#                BASIC AGENT
+# -----------------------------------------
 
 class BasicAgent:
     def __init__(self):
-        print("Loading stronger instruction model...")
+        print("Loading lightweight GAIA agent model...")
+
+        # Lightweight model for HF CPU Spaces (stable)
         self.generator = pipeline(
             "text-generation",
-            model="mistralai/Mistral-7B-Instruct-v0.2",
-            max_new_tokens=256,
+            model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+            max_new_tokens=32,
             do_sample=False,
+            temperature=0.0,
         )
-        print("Model loaded.")
 
-    # ---- Simple math tool ----
+        print("Model loaded successfully.")
+
+    # -------------------------
+    # TOOL 1: Reverse text
+    # -------------------------
+    def try_reverse(self, question: str):
+        q = question.strip()
+
+        # Only reverse if clearly reversed (starts with dot)
+        if q.startswith("."):
+            return q[::-1]
+
+        return None
+
+    # -------------------------
+    # TOOL 2: Safe arithmetic
+    # -------------------------
     def try_math(self, question: str):
         try:
-            # Detect simple arithmetic expressions
-            expression = re.findall(r"[\d\.\+\-\*\/\(\) ]+", question)
-            if expression:
-                candidate = expression[0]
-                result = eval(candidate)
+            pattern = r"\d+\.?\d*\s*[\+\-\*\/]\s*\d+\.?\d*"
+            match = re.search(pattern, question)
+            if match:
+                expression = match.group()
+                result = eval(expression)
+
+                if float(result).is_integer():
+                    return str(int(result))
                 return str(result)
         except:
             pass
+
         return None
 
-    # ---- Clean output for EXACT MATCH ----
+    # -------------------------
+    # STRICT CLEANING (Exact Match)
+    # -------------------------
     def clean_answer(self, text: str) -> str:
         text = text.strip()
 
         if "Answer:" in text:
             text = text.split("Answer:")[-1]
 
-        text = text.split("\n")[0]
-        text = text.strip()
+        text = text.split("\n")[0].strip()
 
-        # Remove trailing punctuation
+        # Remove quotes and trailing punctuation
+        text = text.strip('"').strip("'")
         text = re.sub(r"[\.]$", "", text)
 
         return text.strip()
 
+    # -------------------------
+    # MODEL CALL
+    # -------------------------
     def ask_model(self, question: str):
-        prompt = f"""
-You are solving a benchmark evaluation problem.
-
-Think step by step internally.
-But output ONLY the final answer.
-Do NOT explain.
-Do NOT add extra words.
+        prompt = f"""You are answering a benchmark question.
+Return ONLY the exact final answer.
+No explanation.
+No extra words.
+If number → return number only.
+If word → return word only.
 
-Question:
-{question}
+Question: {question}
+Answer:"""
 
-Final Answer:
-"""
         output = self.generator(prompt)[0]["generated_text"]
         answer = output.replace(prompt, "")
+
         return self.clean_answer(answer)
 
+    # -------------------------
+    # MAIN LOGIC
+    # -------------------------
     def __call__(self, question: str) -> str:
-        print(f"Processing question: {question[:60]}...")
+        print(f"Processing: {question[:60]}...")
+
+        # 1️⃣ Reverse tool
+        reversed_q = self.try_reverse(question)
+        if reversed_q:
+            print("Used reverse tool.")
+            return self.ask_model(reversed_q)
 
+        # 2️⃣ Math tool
         math_result = self.try_math(question)
         if math_result:
             print("Used math tool.")
             return math_result
-            
+
+        # 3️⃣ LLM reasoning
         answer = self.ask_model(question)
 
-        if len(answer.split()) > 6:
-            print("Retrying due to long answer...")
+        # Retry once if output too long
+        if len(answer.split()) > 5:
+            print("Retrying for shorter answer...")
             answer = self.ask_model(question)
 
         print(f"Final 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
+# -----------------------------------------
+#         RUN + SUBMIT FUNCTION
+# -----------------------------------------
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+
+    space_id = os.getenv("SPACE_ID")
 
     if profile:
-        username= f"{profile.username}"
+        username = 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)
+    # Instantiate agent
     try:
         agent = BasicAgent()
     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}")
+    # Fetch Questions
     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
+        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:
-            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})
+            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}"})
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": question_text,
+                    "Submitted Answer": f"AGENT ERROR: {e}",
+                }
+            )
 
     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)
+        return "Agent did not produce any answers.", 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)
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload,
+    }
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    # 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.')}"
+            f"({result_data.get('correct_count', '?')}/"
+            f"{result_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', '')}"
         )
-        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
+
+        return final_status, pd.DataFrame(results_log)
+
     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
+        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.  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)
+    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]
+        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)
+    demo.launch(debug=True, share=False)