Update agent.py

agent.py

@@ -1,46 +1,130 @@
-import mimetypes
-from transformers import pipeline
-from tools.asr_tool import transcribe_audio
-from tools.excel_tool import analyze_excel
-from tools.search_tool import search_duckduckgo
-
-class GaiaAgent:
-    def __init__(self):
-        print("Loading model...")
-        self.llm = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.2", max_new_tokens=512, device="cpu")
-
-    def __call__(self, question: str, files: list = None):
-        trace = []
-        context = ""
-
-        if files:
-            for file in files:
-                mime, _ = mimetypes.guess_type(file.name)
-                if mime and mime.startswith("audio"):
-                    transcription = transcribe_audio(file.name)
-                    trace.append(f"Transcribed audio: {transcription}")
-                    context += f"\nTranscription: {transcription}"
-                elif mime and ("spreadsheet" in mime or file.name.endswith(".xlsx")):
-                    result = analyze_excel(file.name)
-                    trace.append(f"Excel analysis: {result}")
-                    context += f"\nSpreadsheet data: {result}"
-
-        if "http" in question or "Wikipedia" in question or "YouTube" in question or "search" in question.lower():
-            trace.append("Performing DuckDuckGo search...")
-            search_result = search_duckduckgo(question)
-            trace.append(f"Summary from search: {search_result}")
-            context += f"\nSearch Result: {search_result}"
-
-        # Include the original question
-        prompt = f"""
-Answer the question based on the context below.
-Context: {context}
-Question: {question}
-Answer:
-"""
-        response = self.llm(prompt)[0]['generated_text'].split("Answer:")[-1].strip()
-        trace.append(response)
-
-        return response, "\n".join(trace)
+import os
+import gradio as gr
+import requests
+import pandas as pd
+from agent import GaiaAgent
+
+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")
+
+    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"
+
+    try:
+        agent = GaiaAgent()
+    except Exception as e:
+        return f"Error initializing agent: {e}", None
+
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+    except Exception as e:
+        return f"Error fetching questions: {e}", None
+
+    results_log = []
+    answers_payload = []
+
+    print("\n--- STARTING AGENT RUN ---")
+    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:
+            continue
+        try:
+            final_answer, trace = agent(question_text)
+
+            print("\n--- QUESTION ---")
+            print(f"Task ID: {task_id}")
+            print(f"Question: {question_text}")
+            print("\n--- REASONING TRACE ---")
+            print(trace)
+            print("\n--- FINAL ANSWER (SUBMITTED) ---")
+            print(final_answer)
+
+            answers_payload.append({
+                "task_id": task_id,
+                "submitted_answer": final_answer,
+                "reasoning_trace": trace
+            })
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": final_answer})
+        except Exception as e:
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"ERROR: {e}"})
+
+    if not answers_payload:
+        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
+    }
+
+    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.')}"
+        )
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except Exception as e:
+        return f"Submission Failed: {e}", pd.DataFrame(results_log)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# GAIA Agent Submission Interface")
+    gr.Markdown("""
+        Logga in och kör agenten.\n
+        Du behöver INTE en OpenAI API-nyckel längre. Agenten kör en lokal modell.
+    """)
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    status_output = gr.Textbox(label="Submission Result")
+    results_table = gr.DataFrame(label="Answers")
+
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
+
+if __name__ == "__main__":
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID")
+
+    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(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)
+