add rate limitting

app.py

@@ -2,6 +2,11 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
+import time
+import threading
+import random
+import re
+from litellm import RateLimitError
 from smolagents import (
     CodeAgent,
     DuckDuckGoSearchTool,
@@ -20,11 +25,110 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 
-model = LiteLLMModel(
-    model_id="gemini/gemini-2.5-flash",
-    temperature=0.2
+
+class TokenBucketRateLimiter:
+    """Simple token-bucket rate limiter.
+
+    capacity: max tokens in bucket (burst size)
+    refill_rate: tokens added per second
+    """
+
+    def __init__(self, capacity: int, refill_rate: float):
+        self.capacity = float(capacity)
+        self._tokens = float(capacity)
+        self.refill_rate = float(refill_rate)
+        self._lock = threading.Lock()
+        self._last = time.monotonic()
+
+    def acquire(self, tokens: float = 1.0):
+        with self._lock:
+            now = time.monotonic()
+            elapsed = now - self._last
+            # Refill
+            self._tokens = min(self.capacity, self._tokens + elapsed * self.refill_rate)
+            self._last = now
+            if self._tokens >= tokens:
+                self._tokens -= tokens
+                return 0.0
+            # Need to wait for enough tokens
+            required = tokens - self._tokens
+            wait_time = required / self.refill_rate
+            # consume what will be available after waiting
+            self._tokens = 0.0
+            return wait_time
+
+
+class RateLimitedModel:
+    """Wraps a model-like callable and enforces a TokenBucketRateLimiter before each call."""
+
+    def __init__(self, model_obj, rpm: int = 6, burst: int | None = None):
+        self._model = model_obj
+        # rpm -> tokens per minute
+        capacity = burst if burst is not None else max(1, rpm)
+        refill_rate = float(rpm) / 60.0
+        self._limiter = TokenBucketRateLimiter(
+            capacity=capacity, refill_rate=refill_rate
+        )
+
+    def __call__(self, *args, **kwargs):
+        # Each high-level call consumes 1 token. Internal model calls
+        # inside smolagents may still produce multiple requests; this
+        # aims to protect against too many top-level calls per-minute.
+        wait = self._limiter.acquire(1.0)
+        if wait > 0:
+            # Small jitter to avoid stampede
+            jitter = random.uniform(0.0, 0.5)
+            total_wait = wait + jitter
+            print(f"RateLimitedModel sleeping {total_wait:.2f}s to respect RPM limit")
+            time.sleep(total_wait)
+        return self._model(*args, **kwargs)
+
+    def __getattr__(self, name: str):
+        """Proxy attribute access to the underlying model.
+
+        For callable attributes (like `generate`) we wrap the call so the
+        token-bucket rate limiter is applied consistently.
+        """
+        # Avoid recursion
+        if name.startswith("_"):
+            raise AttributeError(name)
+
+        attr = getattr(self._model, name)
+
+        if callable(attr):
+
+            def wrapped(*args, **kwargs):
+                wait = self._limiter.acquire(1.0)
+                if wait > 0:
+                    jitter = random.uniform(0.0, 0.5)
+                    total_wait = wait + jitter
+                    print(
+                        f"RateLimitedModel sleeping {total_wait:.2f}s to respect RPM limit"
+                    )
+                    time.sleep(total_wait)
+                return attr(*args, **kwargs)
+
+            # Preserve original metadata where possible
+            try:
+                wrapped.__name__ = getattr(attr, "__name__", wrapped.__name__)
+            except Exception:
+                pass
+            return wrapped
+
+        return attr
+
+
+# Wrap the model with a rate-limiter. Default RPM has been lowered to 6 
+# but can be configured via the MODEL_RPM environment variable.
+_configured_rpm = int(os.getenv("MODEL_RPM", "6"))
+_configured_burst = None
+model = RateLimitedModel(
+    LiteLLMModel(model_id="gemini/gemini-2.5-flash", temperature=0.2),
+    rpm=_configured_rpm,
+    burst=_configured_burst,
 )
 
+
 class BasicAgent:
     def __init__(self, name: str = "GGSAgent"):
         self.name = name
@@ -38,21 +142,41 @@ class BasicAgent:
             model=model,
             max_steps=15,
             verbosity_level=1,
-            additional_authorized_imports=["json", "math"]
+            additional_authorized_imports=["json", "math"],
+            add_base_tools=True,
         )
         print("BasicAgent initialized.")
+
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
         try:
-            response = self.code_agent({"input": question})
+            # Use retry wrapper to handle rate limit (429) errors from the model
+            # The CodeAgent is callable and expects a single dict argument; pass it
+            # through the `call_model_with_retry` helper so RateLimitError is
+            # handled with exponential backoff.
+            response = call_model_with_retry(self.code_agent, {"input": question})
             print(f"Agent returning response: {response}")
             return response
         except Exception as e:
             print(f"Error in code agent: {e}")
             return f"AGENT ERROR: {e}"
-    
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+
+def call_model_with_retry(
+    callable_fn, *args, max_retries=10, initial_delay=6.0, **kwargs
+):
+    delay = initial_delay
+    for attempt in range(1, max_retries + 1):
+        try:
+            return callable_fn(*args, **kwargs)
+        except RateLimitError as e:
+            if attempt == max_retries:
+                raise
+            time.sleep(delay)
+            delay = min(delay * 2, 60.0)
+
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.

requirements.txt

@@ -1,7 +1,516 @@
-numpy==1.24.4
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