Update app.py

app.py

@@ -1,196 +1,143 @@
 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"
-
-# --- 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):
-    """
-    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"
+from duckduckgo_search import DDGS
+import gradio as gr
 
-    # 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(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
+# ===============================
+# CONFIG
+# ===============================
+
+# Hardcoded GAIA API URL
+BASE_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# Hugging Face username
+HF_USERNAME = "jatinror"  # replace with your HF username if different
+
+# Your Space ID
+SPACE_ID = "jatinror/Final_Assignment_Template"
+AGENT_CODE_URL = f"https://huggingface.co/spaces/{SPACE_ID}/tree/main"
+
+print("Using SPACE_ID:", SPACE_ID)
+print("Agent code URL:", AGENT_CODE_URL)
+
+# ===============================
+# SIMPLE SEARCH TOOL
+# ===============================
+
+def web_search(query, max_results=3):
+    results = []
+    with DDGS() as ddgs:
+        for r in ddgs.text(query, max_results=max_results):
+            results.append(r["body"])
+    return "\n".join(results)
+
+# ===============================
+# DOWNLOAD FILE IF TASK HAS ONE
+# ===============================
+
+def download_task_file(task_id):
+    url = f"{BASE_URL}/files/{task_id}"
+    response = requests.get(url)
+    if response.status_code == 200:
+        file_path = f"/tmp/{task_id}"
+        with open(file_path, "wb") as f:
+            f.write(response.content)
+        return file_path
+    return None
+
+# ===============================
+# BASIC REASONING
+# ===============================
+
+def solve_question(question, task_id):
+    file_path = download_task_file(task_id)
+    context = ""
+    if file_path and os.path.exists(file_path):
+        try:
+            with open(file_path, "r", errors="ignore") as f:
+                context = f.read()
+        except:
+            context = ""
+    else:
+        context = web_search(question)
+    return extract_answer(context)
+
+# ===============================
+# ANSWER EXTRACTION
+# ===============================
+
+def extract_answer(text):
+    import re
+    numbers = re.findall(r"\b\d+(?:\.\d+)?\b", text)
+    if numbers:
+        return numbers[0]
+    words = text.split()
+    return " ".join(words[:6]).strip()
+
+# ===============================
+# FETCH QUESTIONS
+# ===============================
+
+def get_questions():
+    response = requests.get(f"{BASE_URL}/questions")
+    response.raise_for_status()
+    return response.json()
+
+# ===============================
+# SUBMIT ANSWERS
+# ===============================
+
+def submit_answers(answers):
+    payload = {
+        "username": HF_USERNAME,
+        "agent_code": AGENT_CODE_URL,
+        "answers": answers
+    }
+    print("Submitting payload...")
+    response = requests.post(f"{BASE_URL}/submit", json=payload)
+    print("Server response:", response.text)
+
+# ===============================
+# MAIN PIPELINE
+# ===============================
+
+def run_agent():
+    print("Fetching GAIA questions...")
+    questions = get_questions()
+    answers = []
+    for q in questions:
+        task_id = q["task_id"]
+        question = q["question"]
+        print("Solving:", task_id)
         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})
+            result = solve_question(question, task_id)
         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}")
+            print("Error:", e)
+            result = ""
+        answers.append({
+            "task_id": task_id,
+            "submitted_answer": result.strip()
+        })
+    submit_answers(answers)
+    print("Finished submission.")
+
+# ===============================
+# GRADIO BLOCKS UI
+# ===============================
+
+def run_pipeline():
     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
+        run_agent()
+        return "✅ GAIA submission completed. Check leaderboard."
     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"❌ Error occurred: {str(e)}"
 
-# --- 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]
-    )
+    run_button = gr.Button("Run GAIA Agent")
+    output_text = gr.Textbox(label="Output", lines=4)
+    
+    # Link button click to function
+    run_button.click(fn=run_pipeline, inputs=[], outputs=output_text)
 
 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(server_name="0.0.0.0", server_port=7860)