Update app.py

app.py

@@ -1,196 +1,625 @@
+# ============================================================
+# INSTALL REQUIRED LIBRARIES
+# ============================================================
+
+import subprocess
+import sys
+
+subprocess.check_call([
+    sys.executable, "-m", "pip", "install", "-q",
+    "smolagents[toolkit]",
+    "gradio",
+    "requests",
+    "pandas"
+])
+
+
+# ============================================================
+# IMPORT LIBRARIES
+# ============================================================
+
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+from smolagents import (
+    CodeAgent,
+    InferenceClientModel,
+    DuckDuckGoSearchTool,
+    PythonInterpreterTool
+)
+
+
+# ============================================================
+# CONSTANTS
+# ============================================================
+
+DEFAULT_API_URL = "https://huggingface.co/Shrutipanchal086/structrural_AI_agent"
+
+
+# ============================================================
+# STRUCTURALGPT / GAIA AGENT
+# ============================================================
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
+
     def __init__(self):
-        print("BasicAgent initialized.")
+
+        print("Initializing StructuralGPT GAIA Agent...")
+
+        # ----------------------------------------------------
+        # Hugging Face token
+        # Add HF_TOKEN in Space Settings -> Secrets
+        # ----------------------------------------------------
+
+        hf_token = os.getenv("hf_token")
+
+        if not hf_token:
+            raise ValueError(
+                "HF_TOKEN not found. "
+                "Please add HF_TOKEN in Space Settings -> Secrets."
+            )
+
+        # ----------------------------------------------------
+        # Hugging Face model
+        # ----------------------------------------------------
+
+        self.model = InferenceClientModel(
+            model_id="Qwen/Qwen3-Next-80B-A3B-Thinking",
+            token=hf_token,
+            max_tokens=3000
+        )
+
+        # ----------------------------------------------------
+        # Web search
+        # ----------------------------------------------------
+
+        self.search_tool = DuckDuckGoSearchTool(
+            max_results=8
+        )
+
+        # ----------------------------------------------------
+        # Python calculator / reasoning tool
+        # ----------------------------------------------------
+
+        self.python_tool = PythonInterpreterTool(
+            authorized_imports=[
+                "math",
+                "statistics",
+                "datetime",
+                "json",
+                "re"
+            ]
+        )
+
+        # ----------------------------------------------------
+        # Agent
+        # ----------------------------------------------------
+
+        self.agent = CodeAgent(
+            model=self.model,
+            tools=[
+                self.search_tool,
+                self.python_tool
+            ],
+            max_steps=10
+        )
+
+        print("StructuralGPT GAIA Agent initialized successfully.")
+
+
     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):
+        print("\n" + "=" * 60)
+        print("QUESTION:")
+        print(question)
+        print("=" * 60)
+
+        prompt = f"""
+You are a powerful general-purpose AI agent participating
+in the GAIA benchmark.
+
+Your job is to solve the user's question accurately.
+
+IMPORTANT RULES:
+
+1. Understand the question completely before answering.
+
+2. If the question requires current, factual, or external
+   information, use the web search tool.
+
+3. If calculations are required, use the Python tool.
+   Do not rely on mental arithmetic for complicated calculations.
+
+4. Break difficult problems into smaller steps.
+
+5. Verify important calculations and facts before producing
+   the final answer.
+
+6. If multiple pieces of information are required, collect
+   all necessary information before answering.
+
+7. Do not invent facts, sources, numbers, or results.
+
+8. Give ONLY the final answer required by the question.
+   Do not unnecessarily explain your internal reasoning.
+
+9. Pay very close attention to:
+   - units
+   - dates
+   - names
+   - numerical values
+   - percentages
+   - requested formats
+
+10. If the question asks for a specific format, follow that
+    format exactly.
+
+You are also knowledgeable in civil and structural engineering,
+including RCC design, steel design, structural analysis,
+foundation engineering, transportation engineering,
+water resources engineering, and construction management.
+
+For engineering questions, use Indian Standards when relevant,
+including IS 456, IS 875, IS 1893, IS 800 and IS 13920.
+
+USER QUESTION:
+{question}
+"""
+
+        try:
+
+            result = self.agent.run(prompt)
+
+            answer = str(result).strip()
+
+            print("\nFINAL ANSWER:")
+            print(answer)
+
+            return answer
+
+        except Exception as e:
+
+            print("Agent error:", e)
+
+            return f"Unable to solve the question because of an agent error: {e}"
+
+
+# ============================================================
+# RUN AND SUBMIT ALL
+# ============================================================
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Fetch all GAIA questions,
+    run the agent,
+    submit answers,
+    and display 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
+
+    # --------------------------------------------------------
+    # Check login
+    # --------------------------------------------------------
 
     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
+
+        return (
+            "Please login to Hugging Face using the Login button.",
+            None
+        )
+
+
+    # --------------------------------------------------------
+    # API URLs
+    # --------------------------------------------------------
 
     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)
+
+    # --------------------------------------------------------
+    # Space information
+    # --------------------------------------------------------
+
+    space_id = os.getenv("SPACE_ID")
+
+    if space_id:
+
+        agent_code = (
+            f"https://huggingface.co/spaces/"
+            f"{space_id}/tree/main"
+        )
+
+    else:
+
+        agent_code = "Local/Unknown-Space"
+
+
+    print("Agent code URL:")
+    print(agent_code)
+
+
+    # ========================================================
+    # 1. CREATE 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}")
+        print("Error creating agent:", e)
+
+        return (
+            f"Error initializing agent: {e}",
+            None
+        )
+
+
+    # ========================================================
+    # 2. FETCH QUESTIONS
+    # ========================================================
+
+    print("\nFetching GAIA questions...")
+
     try:
-        response = requests.get(questions_url, timeout=15)
+
+        response = requests.get(
+            questions_url,
+            timeout=30
+        )
+
         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
+
+            return (
+                "No questions were received.",
+                None
+            )
+
+        print(
+            f"Fetched {len(questions_data)} questions."
+        )
+
     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
+        print("Error fetching questions:", e)
+
+        return (
+            f"Error fetching questions: {e}",
+            None
+        )
+
+
+    # ========================================================
+    # 3. RUN AGENT
+    # ========================================================
+
     results_log = []
+
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+
+    print("\nRunning agent...")
+
+    for number, item in enumerate(
+        questions_data,
+        start=1
+    ):
+
         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}")
+
+            print(
+                "Skipping invalid question:",
+                item
+            )
+
             continue
+
+
+        print(
+            f"\nProcessing question "
+            f"{number}/{len(questions_data)}"
+        )
+
+
         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})
+
+            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}"})
+
+            print(
+                f"Agent error on task {task_id}: {e}"
+            )
+
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": question_text,
+                    "Submitted Answer":
+                        f"AGENT ERROR: {e}"
+                }
+            )
+
+
+    # ========================================================
+    # 4. CHECK ANSWERS
+    # ========================================================
 
     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}'..."
+        return (
+            "Agent did not produce any answers.",
+            pd.DataFrame(results_log)
+        )
+
+
+    # ========================================================
+    # 5. PREPARE SUBMISSION
+    # ========================================================
+
+    submission_data = {
+
+        "username":
+            username.strip(),
+
+        "agent_code":
+            agent_code,
+
+        "answers":
+            answers_payload
+    }
+
+
+    status_update = (
+        f"Agent finished. "
+        f"Submitting {len(answers_payload)} answers..."
+    )
+
     print(status_update)
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+
+    # ========================================================
+    # 6. SUBMIT TO GAIA
+    # ========================================================
+
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+
+        response = requests.post(
+            submit_url,
+            json=submission_data,
+            timeout=120
+        )
+
         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
+
+            "Submission Successful!\n\n"
+
+            f"User: "
+            f"{result_data.get('username')}\n"
+
+            f"Overall Score: "
+            f"{result_data.get('score', 'N/A')}%\n"
+
+            f"Correct: "
+            f"{result_data.get('correct_count', '?')}/"
+            f"{result_data.get('total_attempted', '?')}\n\n"
+
+            f"Message: "
+            f"{result_data.get('message', '')}"
+        )
+
+
+        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}."
+
+        error_detail = (
+            f"Server responded with "
+            f"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
+
+            error_detail += (
+                f" Detail: "
+                f"{error_json.get('detail', '')}"
+            )
+
+        except Exception:
+
+            error_detail += (
+                f" Response: "
+                f"{e.response.text[:500]}"
+            )
+
+
+        return (
+            f"Submission Failed: {error_detail}",
+            pd.DataFrame(results_log)
+        )
+
+
     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
+
+        return (
+            "Submission Failed: Request timed out.",
+            pd.DataFrame(results_log)
+        )
+
+
     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 (
+            f"Submission Failed: Network error - {e}",
+            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"Unexpected submission error: {e}",
+            pd.DataFrame(results_log)
+        )
 
 
-# --- Build Gradio Interface using Blocks ---
+# ============================================================
+# GRADIO INTERFACE
+# ============================================================
+
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+
     gr.Markdown(
-        """
-        **Instructions:**
+        "# 🚀 StructuralGPT - GAIA Agent"
+    )
 
-        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.
+    gr.Markdown(
+        """
+        ### Instructions
 
-        ---
-        **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.
+        1. Login to Hugging Face.
+        2. The agent uses Qwen through Hugging Face.
+        3. The agent can search the web.
+        4. The agent can perform calculations using Python.
+        5. Click **Run Evaluation & Submit All Answers**.
         """
     )
 
     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 = gr.Button(
+        "Run Evaluation & Submit All Answers"
+    )
+
+
+    status_output = gr.Textbox(
+        label="Run Status / Submission Result",
+        lines=8,
+        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
+        ]
     )
 
+
+# ============================================================
+# START APPLICATION
+# ============================================================
+
 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(
+        "\n" +
+        "-" * 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}"
+        )
+
+    if space_id:
+
+        print(
+            f"SPACE_ID: {space_id}"
+
+        )
+
+        print(
+            "Repository:"
+        )
+
+        print(
+            f"https://huggingface.co/spaces/{space_id}"
+        )
+
+
+    print(
+        "\nLaunching StructuralGPT..."
+    )
 
-    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
+    )