Update app.py

app.py

@@ -4,19 +4,15 @@ import traceback
 import gradio as gr
 import requests
 import pandas as pd
-from openai import OpenAI
 from smolagents import CodeAgent, DuckDuckGoSearchTool, tool
-from smolagents.models import OpenAIServerModel
+from smolagents.models import BaseModel
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
 logger = logging.getLogger(__name__)
 
 SUBMISSION_URL = "https://agents-course-unit4-scoring.hf.space"
-GITHUB_TOKEN = os.getenv("GITHUB_TOKEN")
-if not GITHUB_TOKEN:
-    raise ValueError("CRITICAL: GITHUB_TOKEN environment variable not set.")
-GITHUB_ENDPOINT = "https://models.github.ai/inference"
-MODEL_ID = os.getenv("MODEL_ID", "openai/gpt-4o-mini")
+POLLINATIONS_API = "https://text.pollinations.ai/"
+MODEL_ID = os.getenv("MODEL_ID", "openai-large")
 
 try:
     search_tool_instance = DuckDuckGoSearchTool()
@@ -29,7 +25,6 @@ except Exception as e:
 def web_search(query: str) -> str:
     """
     Performs a web search using DuckDuckGo. Use this for general questions or current info.
-
     Args:
         query (str): The search query string.
     """
@@ -49,7 +44,6 @@ def web_search(query: str) -> str:
 def wikipedia_lookup(page_title: str) -> str:
     """
     Fetches the summary intro text of an English Wikipedia page. Use exact titles.
-
     Args:
         page_title (str): The exact title of the Wikipedia page (e.g., 'Albert Einstein').
     """
@@ -72,8 +66,8 @@ def wikipedia_lookup(page_title: str) -> str:
                 logger.warning(f"Wikipedia page '{title}' is disambiguation.")
                 return f"Wikipedia Error: '{title}' is a disambiguation page. Try a more specific title."
             else:
-                 logger.warning(f"Wikipedia page '{title}' found but has no summary.")
-                 return f"Wikipedia Error: Page '{title}' found but has no summary."
+                logger.warning(f"Wikipedia page '{title}' found but has no summary.")
+                return f"Wikipedia Error: Page '{title}' found but has no summary."
     except requests.exceptions.HTTPError as e:
         if e.response.status_code == 404:
             logger.warning(f"Wikipedia page not found: {page_safe}")
@@ -86,11 +80,9 @@ def wikipedia_lookup(page_title: str) -> str:
         return f"Wikipedia Error: Unexpected error: {e}"
 
 REACT_INSTRUCTION_PROMPT = """You are a helpful assistant using tools to answer questions.
-
 Available Tools:
 - web_search(query: str): Searches the web. Use for general info or current events.
 - wikipedia_lookup(page_title: str): Looks up a specific English Wikipedia page. Use exact titles (e.g., 'Berlin').
-
 Follow these steps:
 1. Thought: Plan which tool to use and why.
 2. Action: Call ONE tool (e.g., web_search(query="...") or wikipedia_lookup(page_title="...")).
@@ -99,28 +91,58 @@ Follow these steps:
 5. Repeat Action/Observation/Thought until answered or determined impossible.
 6. Thought: Summarize findings based ONLY on observations.
 7. Final Answer: Provide the answer starting exactly with "FINAL ANSWER: " using the required format (number, short string, or comma-separated list).
-
 Formatting Rules for FINAL ANSWER:
 - Numbers: Just the number (e.g., `42`).
 - Strings: Minimal words, no articles. Digits as words (e.g., `seven`).
 - Lists: Comma-separated (e.g., `paris,london,three`).
-
 Let's begin!
 """
 
-logger.info(f"Initializing LLM connection: {MODEL_ID} @ {GITHUB_ENDPOINT}")
+# Benutzerdefiniertes Modell für Pollinations API
+class PollinationsModel(BaseModel):
+    def __init__(self, model_id="openai-large", max_tokens=8196, seed=42):
+        self.model_id = model_id
+        self.max_tokens = max_tokens
+        self.seed = seed
+        self.api_url = POLLINATIONS_API
+        logger.info(f"Initialized PollinationsModel with model_id={model_id}")
+
+    def generate(self, prompt, **kwargs):
+        logger.info(f"Generating with PollinationsModel. Prompt length: {len(prompt)}")
+        try:
+            payload = {
+                "messages": [
+                    {"role": "user", "content": prompt}
+                ],
+                "model": self.model_id,
+                "max_tokens": self.max_tokens,
+                "seed": self.seed,
+                "jsonMode": False,
+                "private": True
+            }
+            response = requests.post(self.api_url, json=payload, timeout=120)
+            response.raise_for_status()
+            result = response.json()
+            if "content" in result:
+                return result["content"]
+            else:
+                logger.error(f"Unexpected response structure: {result}")
+                return "Error: Unexpected API response format"
+        except Exception as e:
+            logger.exception(f"PollinationsModel generate failed: {e}")
+            return f"Error generating response: {str(e)}"
+
+logger.info(f"Initializing Pollinations LLM connection: {MODEL_ID}")
 try:
-    logger.info("Attempting to configure OpenAIServerModel with 'api_base' (and no request_timeout)...")
-    llm_model = OpenAIServerModel(
+    llm_model = PollinationsModel(
         model_id=MODEL_ID,
-        api_key=GITHUB_TOKEN,
-        api_base=GITHUB_ENDPOINT
-        # Removed request_timeout=60
+        max_tokens=8196,
+        seed=42
     )
-    logger.info("LLM connection configured using 'api_base'.")
+    logger.info("LLM connection configured using Pollinations API.")
 except Exception as e:
-    logger.exception("CRITICAL: Failed to configure OpenAIServerModel (tried with api_base)")
-    raise RuntimeError(f"Could not configure SmolAgents model using api_base: {e}") from e
+    logger.exception("CRITICAL: Failed to configure PollinationsModel")
+    raise RuntimeError(f"Could not configure PollinationsModel: {e}") from e
 
 logger.info("Initializing CodeAgent...")
 try:
@@ -175,7 +197,7 @@ def evaluate_and_submit():
         logger.info(f"Processing Q {i+1}/{len(questions)} (ID: {task_id})...")
         raw_agent_output = run_agent_on_question(question_text)
         final_answer = "AGENT_ERROR: No 'FINAL ANSWER:' marker."
-        marker = "FINAL ANSWER:";
+        marker = "FINAL ANSWER:"
         if marker in raw_agent_output: final_answer = raw_agent_output.split(marker, 1)[1].strip()
         elif "AGENT_ERROR:" in raw_agent_output: final_answer = raw_agent_output
         results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": final_answer, "Full Output": raw_agent_output})
@@ -202,7 +224,7 @@ def evaluate_and_submit():
 
 logger.info("Setting up Gradio interface...")
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🚀 Agent Evaluation Runner 🚀\nEnsure `GITHUB_TOKEN` secret is set. Click Run to start.")
+    gr.Markdown("# 🚀 Agent Evaluation Runner 🚀\nEnsure all configurations are set. Click Run to start.")
     run_button = gr.Button("▶️ Run Evaluation & Submit All Answers", variant="primary")
     status_textbox = gr.Textbox(label="📊 Status", lines=4, interactive=False)
     results_df_display = gr.DataFrame(label="📋 Detailed Log", headers=["Task ID", "Question", "Submitted Answer", "Full Output"], wrap=True, column_widths=["10%", "25%", "20%", "45%"])
@@ -213,4 +235,3 @@ if __name__ == "__main__":
     logger.info("Launching Gradio application...")
     demo.launch(debug=True, share=False)
     logger.info("Gradio application launched.")
-