start from scratch

app.py

@@ -1,304 +1,175 @@
-import requests
-import string
-import inspect
 import os
-import re
-import spacy
-from transformers import pipeline
-from duckduckgo_search import DDGS
-from sklearn.metrics.pairwise import cosine_similarity
-import numpy as np
-import whisper
-import moviepy
 import gradio as gr
+import requests
+import inspect
 import pandas as pd
-from spacy.cli import download
-from transformers import AutoTokenizer, AutoModel
-import torch
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
 
+# (Keep Constants and BasicAgent class as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Basic Agent Definition ---
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
-        try:
-            self.spacy = spacy.load("en_core_web_sm")
-        except OSError:
-            download("en_core_web_sm")
-            self.spacy = spacy.load("en_core_web_sm")
-    
-        self.whisper_model = whisper.load_model("base")
-        self.qa_pipeline = pipeline("question-answering", truncation=True, padding=True)
-        self.ner_pipeline = pipeline("ner", aggregation_strategy="simple")
-    
-        # ✅ FIXED: safer embedding model setup
-        self.embedding_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-        self.embedding_model = AutoModel.from_pretrained("bert-base-uncased")
-
-    def split_text_into_chunks(self, text, max_length=512):
-        """Split text into chunks smaller than `max_length` tokens."""
-        words = text.split()
-        chunks = []
-        chunk = []
-
-        for word in words:
-            chunk.append(word)
-            if len(' '.join(chunk)) > max_length:
-                chunks.append(' '.join(chunk[:-1]))  # Add the chunk and reset
-                chunk = [word]
-
-        if chunk:
-            chunks.append(' '.join(chunk))  # Add the final chunk
-
-        return chunks
-
-    def answer_question(self, question: str, context: str) -> str:
-        try:
-            context_chunks = self.split_text_into_chunks(context, max_length=512)
-            answers = []
-            for chunk in context_chunks:
-                answer = self.qa_pipeline(question=question, context=chunk)["answer"]
-                answers.append(answer)
-
-            return " ".join(answers)  # Combine answers from chunks
-        except Exception as e:
-            return f"Error answering question: {e}"
-            
-    def extract_named_entities(self, text):
-        entities = self.ner_pipeline(text)
-        return [e["word"] for e in entities if e["entity_group"] == "PER"]
-
-    def extract_numbers(self, text):
-        return re.findall(r"\d+", text)
-
-    def extract_keywords(self, text):
-        doc = self.spacy(text)
-        return [token.text for token in doc if token.pos_ in ["NOUN", "PROPN"]]
-
-    def call_whisper(self, video_path: str) -> str:
-        video = moviepy.editor.VideoFileClip(video_path)
-        audio_path = "temp_audio.wav"
-        video.audio.write_audiofile(audio_path)
-        result = self.whisper_model.transcribe(audio_path)
-        os.remove(audio_path)
-        return result["text"]
-
-    def search(self, question: str) -> str:
-        try:
-            with DDGS() as ddgs:
-                results = list(ddgs.text(question, max_results=3))
-                if not results:
-                    return "No relevant search results found."
-                context = results[0]["body"]
-                return context
-        except Exception as e:
-            return f"Search error: {e}"
-
-    def answer_question(self, question: str, context: str) -> str:
-        try:
-            return self.qa_pipeline(question=question, context=context)["answer"]
-        except:
-            return context  # Fallback to context if QA fails
-
-    def handle_logic_riddles(self, question: str) -> str | None:
-        # Normalize the input
-        q = question.lower().strip()
-        q = q.translate(str.maketrans("", "", string.punctuation))  # remove punctuation
-        q = re.sub(r"\s+", " ", q)  # normalize multiple spaces
-    
-        logic_patterns = [
-            {
-                "pattern": r"opposite of the word left",
-                "answer": "right"
-            },
-            {
-                "pattern": r"what comes after a",
-                "answer": "b"
-            },
-            {
-                "pattern": r"first letter of the alphabet",
-                "answer": "a"
-            },
-            {
-                "pattern": r"what is the color of the clear sky",
-                "answer": "blue"
-            },
-            {
-                "pattern": r"how many sides does a triangle have",
-                "answer": "3"
-            },
-            {
-                "pattern": r"how many legs does a spider have",
-                "answer": "8"
-            },
-            {
-                "pattern": r"what is 2 \+ 2",
-                "answer": "4"
-            },
-            {
-                "pattern": r"what is the opposite of up",
-                "answer": "down"
-            },
-            {
-                "pattern": r"if you understand this sentence.*opposite.*left",
-                "answer": "right"
-            }
-        ]
-    
-        for item in logic_patterns:
-            if re.search(item["pattern"], q, re.IGNORECASE):
-                return item["answer"]
-    
-        return None
-
-    
-    def solve_riddle(self, riddle: str) -> str:
-        """Fallback riddle solver using QA pipeline with general logic context."""
-        riddle_context = (
-            "You are a riddle-solving assistant. Try to give a short and logical answer to riddles.\n"
-            "Examples:\n"
-            "Q: What has keys but can't open locks?\nA: A piano\n"
-            "Q: What runs but never walks?\nA: Water\n"
-            "Q: What comes once in a minute, twice in a moment, but never in a thousand years?\nA: The letter M\n"
-            f"Q: {riddle}\nA:"
-        )
-        try:
-            result = self.qa_pipeline(question=riddle, context=riddle_context)
-            return result["answer"]
-        except Exception as e:
-            return f"Could not solve riddle: {e}"
-
-
-
-    def __call__(self, question: str, video_path: str = None) -> str:
-        print(f"Agent received question: {question[:60]}...")
-
-        # Handle logic/riddle questions first
-        logic_answer = self.handle_logic_riddles(question)
-        if logic_answer is not None:
-            return f"🧠 Logic Answer: {logic_answer}"
-        else:
-            riddle_guess = self.solve_riddle(question)
-            return f"🤖 Riddle Guess: {riddle_guess}"
-
-        if video_path:
-            transcription = self.call_whisper(video_path)
-            print(f"Transcribed video: {transcription[:100]}...")
-            return transcription
-
-        context = self.search(question)
-        answer = self.answer_question(question, context)
-        q_lower = question.lower()
-
-        # Enhanced formatting based on question type
-        if "who" in q_lower:
-            people = self.extract_named_entities(context)
-            return f"👤 Who: {', '.join(people) if people else 'No person found'}\n\n🧠 Answer: {answer}"
-
-        elif "how many" in q_lower:
-            numbers = self.extract_numbers(context)
-            return f"🔢 How many: {', '.join(numbers) if numbers else 'No numbers found'}\n\n🧠 Answer: {answer}"
-
-        elif "how" in q_lower:
-            return f"⚙️ How: {answer}"
-
-        elif "what" in q_lower or "where" in q_lower:
-            keywords = self.extract_keywords(context)
-            return f"🗝️ Keywords: {', '.join(keywords[:5])}\n\n🧠 Answer: {answer}"
-
-        else:
-            return f"🧠 Answer: {answer}"
-            
-# --- Submission Function ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
+        self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
+
+        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: FINAL ANSWER: [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 depending of whether the element
+        to be put in the list is a number or a string.
+        """
+        self.agent.prompt_templates["system_prompt"] = self.agent.prompt_templates["system_prompt"] + SYSTEM_PROMPT
+        
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        final_answer = self.agent.run(question)
+        print(f"Agent returning final answer: {final_answer}")
+        return final_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 = profile.username
+        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 = 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(f"Agent repo: {agent_code}")
+    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 Exception as e:
+    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")
-        video_link = item.get("video_link")
-
         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, video_path=video_link)
+            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:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"ERROR: {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}"})
 
     if not answers_payload:
-        return "No answers were submitted.", pd.DataFrame(results_log)
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload
-    }
+    # 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"Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
-            f"Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')})\n"
-            f"Message: {result_data.get('message', '')}"
+            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)
+        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:
-        return f"Submission Failed: {e}", pd.DataFrame(results_log)
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
 
 
-# --- Gradio Interface ---
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
-        """
-        **Instructions:**
-        1. Clone this space and modify the agent logic if desired.
-        2. Log in to Hugging Face with the button below.
-        3. Click 'Run Evaluation & Submit All Answers' to evaluate and submit your agent.
-        ---
-        **Note:** This process may take several minutes depending on the number of questions.
-        """
+        "Please clone this space, then modify the code to define your agent's logic within the `BasicAgent` class. "
+        "Log in to your Hugging Face account using the button below. This uses your HF username for submission. "
+        "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)
+    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -306,22 +177,26 @@ with gr.Blocks() as demo:
         outputs=[status_output, results_table]
     )
 
-
 if __name__ == "__main__":
-    print("-" * 30 + " App Starting " + "-" * 30)
-    space_host = os.getenv("SPACE_HOST")
-    space_id = os.getenv("SPACE_ID")
-
-    if space_host:
-        print(f"✅ SPACE_HOST: {space_host}")
-        print(f"   → https://{space_host}.hf.space")
+    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 No SPACE_HOST set.")
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id:
-        print(f"✅ SPACE_ID: {space_id}")
-        print(f"   → https://huggingface.co/spaces/{space_id}/tree/main")
+    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 No SPACE_ID set.")
+        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)