Update app.py

app.py

@@ -1,196 +1,139 @@
 import os
-import gradio as gr
+import re
 import requests
-import inspect
-import pandas as pd
-
-# (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
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+import gradio as gr
+from PIL import Image
+from pypdf import PdfReader
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, tool
+
+# ১. মডেল সেটআপ
+HF_TOKEN = os.getenv("HF_TOKEN")
+model = HfApiModel(
+    model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+    token=HF_TOKEN
+)
+
+# ২. কাস্টম টুলস
+@tool
+def read_pdf_content(file_path: str) -> str:
+    """Reads and extracts text from a PDF file.
+    
+    Args:
+        file_path: Path to the PDF file.
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    try:
+        reader = PdfReader(file_path)
+        text = ""
+        for p in reader.pages:
+            text += p.extract_text() or ""
+        return text[:3000] if text else "No text found."
+    except Exception as e:
+        return f"Error reading PDF: {e}"
+
+@tool
+def inspect_image(file_path: str) -> str:
+    """Inspects image dimensions and metadata.
+    
+    Args:
+        file_path: Path to the image.
     """
-    # --- 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
+    try:
+        with Image.open(file_path) as img:
+            return f"Format: {img.format}, Size: {img.size}, Mode: {img.mode}"
+    except Exception as e:
+        return f"Error reading image: {e}"
+
+# ৩. এজেন্ট ইনিশিয়ালাইজেশন
+gaia_agent = CodeAgent(
+    tools=[DuckDuckGoSearchTool(), read_pdf_content, inspect_image],
+    model=model,
+    max_steps=5,
+    additional_authorized_imports=["math", "statistics", "re", "json", "datetime"]
+)
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+def solve_single_task(question: str, file_path: str = None) -> str:
+    prompt = f"Question: {question}\n"
+    if file_path:
+        prompt += f"Local file available at: {file_path}\n"
+    prompt += "Provide ONLY the concise final answer without explanations or 'FINAL ANSWER' prefix."
     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)
+        raw = str(gaia_agent.run(prompt)).strip()
+        return re.sub(r'^(FINAL ANSWER|Answer)[:\s]*', '', raw, flags=re.IGNORECASE).strip()
+    except Exception:
+        return "N/A"
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    if not profile:
+        return "Please Login to Hugging Face with the button.", None
+
+    username = profile.username
+    space_id = os.getenv("SPACE_ID", "Learnix-AI-Lab/Final_Assignment_Template")
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+    
+    questions_url = f"{DEFAULT_API_URL}/questions"
+    submit_url = f"{DEFAULT_API_URL}/submit"
 
-    # 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
+        resp = requests.get(questions_url, timeout=20)
+        resp.raise_for_status()
+        questions_data = resp.json()
     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...")
+    display_rows = []
+
     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}"})
-
-    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}")
+        q_text = item.get("question")
+        file_name = item.get("file_name")
+        local_path = None
+
+        if file_name:
+            file_dl_url = f"{DEFAULT_API_URL}/files/{task_id}"
+            dl_resp = requests.get(file_dl_url)
+            if dl_resp.status_code == 200:
+                local_path = f"tmp_{file_name}"
+                with open(local_path, "wb") as f:
+                    f.write(dl_resp.content)
+
+        ans = solve_single_task(q_text, local_path)
+        answers_payload.append({"task_id": task_id, "submitted_answer": ans})
+        display_rows.append([q_text, ans])
+
+        if local_path and os.path.exists(local_path):
+            os.remove(local_path)
+
+    # সাবমিট করা
+    submit_data = {
+        "username": username,
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
+    
     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
+        sub_resp = requests.post(submit_url, json=submit_data, timeout=30)
+        sub_resp.raise_for_status()
+        result_json = sub_resp.json()
+        score = result_json.get("score", "N/A")
+        return f"Successfully submitted! Your Score: {score}", display_rows
     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 error: {e}", display_rows
 
-# --- Build Gradio Interface using Blocks ---
+# ৪. Gradio UI
 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.Markdown("# 🤖 GAIA Benchmark Evaluator")
     gr.LoginButton()
+    run_btn = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
+    status_box = gr.Textbox(label="Status / Result", lines=3)
+    results_table = gr.DataFrame(headers=["Question", "Agent Answer"], label="Answers")
 
-    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(
+    run_btn.click(
         fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+        outputs=[status_box, 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()