use gemini as llm

.env

@@ -0,0 +1 @@
+GEMINI_API_KEY=AIzaSyA7UYtFpPRUwCV0D4fPz1Kg3XqEsSvuxas

.gitignore

@@ -0,0 +1 @@
+test.py

app.py

@@ -4,13 +4,22 @@ import requests
 import inspect
 import pandas as pd
 from smolagents import CodeAgent, HfApiModel, LiteLLMModel, Tool, DuckDuckGoSearchTool
+import litellm
 from agent_tools import calculator_tool, visit_webpage
 import numpy as np
 import time
 import datetime
 from smolagents import InferenceClientModel, TransformersModel
+from utils import download_file
+from dotenv import load_dotenv
 
+load_dotenv()
 
+llm_model = LiteLLMModel(
+    model_id="gemini/gemini-2.0-flash", # you can see other model names here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models. It is important to prefix the name with "gemini/"
+    api_key=os.getenv("GEMINI_API_KEY"),
+    max_tokens=8192
+)
 
 
 # (Keep Constants as is)
@@ -20,11 +29,11 @@ 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):
+    def __init__(self, model):
 
         print("BasicAgent initialized.")
         # Initialize the Hugging Face model
-        self.model = HfApiModel()
+        self.model = model
         search_tool = DuckDuckGoSearchTool()
 
         self.web_agent = CodeAgent(
@@ -35,9 +44,6 @@ class BasicAgent:
             description="Runs web searches for you.",
         )
 
-        self.check_model = TransformersModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")
-
-
     def check_final_answer(self, final_answer, agent_memory):
         prompt = f"""Here is a user-given task and the agent steps: {agent_memory.get_succinct_steps()}. \ 
                     Report your thoughts, and finish your answer with the following template:
@@ -57,8 +63,15 @@ class BasicAgent:
 
         #model = InferenceClientModel()
 
-        response = self.check_model(messages=messages)
-        return response
+        response = litellm.completion(
+            model="gemini/gemini-2.0-flash",
+            messages=messages,
+        )
+
+        # Extract the response content
+        content = response.get('choices', [{}])[0].get('message', {}).get('content')
+
+        return content
     
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
@@ -135,6 +148,8 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
+        if len(item.get("file_name")):
+            download_file(item)
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue

utils.py

@@ -0,0 +1,40 @@
+import requests
+
+def download_file(record):
+    DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+    task_id = record['task_id']
+    url = DEFAULT_API_URL+f'/files/{task_id}'
+
+    filename = record['file_name']
+
+    if filename.endswith('.mp3'):
+        # Local path where the audio will be saved        
+        # Send a GET request to fetch the audio file
+        response = requests.get(url, stream=True)
+        
+        # Check if the request was successful
+        if response.status_code == 200:
+            # Open a local file in binary write mode
+            with open(filename, 'wb') as f:
+                for chunk in response.iter_content(chunk_size=1024):
+                    if chunk:  # filter out keep-alive new chunks
+                        f.write(chunk)
+            print(f"Audio file downloaded successfully and saved as {filename}")
+        else:
+            print(f"Failed to download audio file. Status code: {response.status_code}")
+
+    elif filename.endswith('.png') or filename.endswith('.py') or filename.endswith('.xlsx'):
+        # Send a GET request to fetch the image file
+        response = requests.get(url, stream=True)
+        
+        # Check if the request was successful
+        if response.status_code == 200:
+            # Open a local file in binary write mode
+            with open(filename, 'wb') as f:
+                for chunk in response.iter_content(chunk_size=1024):
+                    if chunk:  # filter out keep-alive new chunks
+                        f.write(chunk)
+            print(f"Image file downloaded successfully and saved as {filename}")
+        else:
+            print(f"Failed to download image file. Status code: {response.status_code}")