Added smolgents

app.py

@@ -1,12 +1,14 @@
 import os
 import gradio as gr
-import requests
-import inspect
 import pandas as pd
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
+# Transformers and torch might not be directly needed by BasicAgent if smolgent handles its own LLM
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+# import torch
+import asyncio
+import aiohttp
+
+from smolagents import CodeAgent, HfApiModel, DuckDuckGoSearchTool # BaseTool might not be needed if DuckDuckGoSearchTool is used directly
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
@@ -14,54 +16,49 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
-        # Load the Qwen model and tokenizer
-        self.model_name = "Qwen/Qwen1.5-0.5B-Chat"
+        print("BasicAgent initialized to use smolagents.")
+
+        # --- smolagents Initialization ---
         try:
-            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
-            self.model = AutoModelForCausalLM.from_pretrained(self.model_name)
-            # Move model to GPU if available
-            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-            self.model.to(self.device)
-            print(f"Qwen model '{self.model_name}' loaded successfully on {self.device}.")
+            # Use the provided DuckDuckGoSearchTool from smolagents
+            # You might need to pass configuration to it if required by the library
+            # e.g., API keys if it were a different search tool. For DuckDuckGo, often no key is needed.
+            search_tool = DuckDuckGoSearchTool()
+            
+            # Configure the LLM for smolagents
+            # HfApiModel is used for Hugging Face Inference API models
+            # Ensure you have your HUGGING_FACE_HUB_TOKEN environment variable set if using private/gated models
+            # or if the model requires it.
+            llm_model = HfApiModel(
+                model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+            )
+            
+            self.agent_executor = CodeAgent(
+                model=llm_model,
+                tools=[search_tool],
+            )
+            print("smolagents AgentExecutor initialized successfully with WebSearchTool.")
+
+        except ImportError:
+            print("ERROR: smolagents library not found. Please ensure 'smolagents' is installed and import paths are correct.")
+            raise
         except Exception as e:
-            print(f"Error loading Qwen model: {e}")
+            print(f"Error initializing smolagents: {e}")
             raise
+        # --- END OF smolagents INITIALIZATION REPLACEMENT SECTION ---
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        print(f"\nAgent (smolagents) received question (first 100 chars): {question[:100]}...")
         try:
-            # Create a chat-like prompt for Qwen
-            messages = [
-                {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": question}
-            ]
-            text = self.tokenizer.apply_chat_template(
-                messages,
-                tokenize=False,
-                add_generation_prompt=True
-            )
-            model_inputs = self.tokenizer([text], return_tensors="pt").to(self.device)
-
-            # Generate a response
-            generated_ids = self.model.generate(
-                model_inputs.input_ids,
-                max_new_tokens=500, # Limit the response length
-                do_sample=True,
-                temperature=0.7, # Adjust for creativity vs. consistency
-                top_p=0.9
-            )
-            generated_ids = [
-                output_id[len(model_inputs.input_ids[0]):] for output_id in generated_ids
-            ]
-            response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-            print(f"Agent returning answer (first 50 chars): {response[:50]}...")
-            return response
+            response = self.agent_executor.run(question)
+
+            print(f"Agent (smolagents) returning answer (first 50 chars): {str(response)[:50]}...")
+            return str(response) # Ensure the response is a string
         except Exception as e:
-            print(f"Error during agent's __call__: {e}")
-            return f"Error: Could not process the question. {e}"
+            print(f"Error during smolagents agent's __call__: {e}")
+            return f"Error: Could not process the question with smolagents. {e}"
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+async def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
@@ -90,96 +87,115 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     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
+    questions_data = []
+    async with aiohttp.ClientSession() as session:
+        # 2. Fetch Questions
+        print(f"Fetching questions from: {questions_url}")
+        try:
+            async with session.get(questions_url, timeout=aiohttp.ClientTimeout(total=20)) as response:
+                response.raise_for_status()
+                questions_data = await 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 aiohttp.ClientResponseError as e:
+            err_msg = f"Error fetching questions (HTTP {e.status}): {e.message}"
+            print(err_msg)
+            return err_msg, None
+        except asyncio.TimeoutError:
+            err_msg = "Timeout error fetching questions."
+            print(err_msg)
+            return err_msg, None
+        except aiohttp.ClientError as e: # Includes connection errors, etc.
+            err_msg = f"Error fetching questions (ClientError): {e}"
+            print(err_msg)
+            return err_msg, None
+        except Exception as e: # Catch-all for others like JSONDecodeError
+            err_msg = f"An unexpected error occurred fetching questions: {e}"
+            print(err_msg)
+            if 'response' in locals() and hasattr(response, 'text'):
+                try:
+                    response_text_snippet = (await response.text())[:500]
+                    print(f"Response text snippet: {response_text_snippet}")
+                except: pass
+            return err_msg, None
+
+    # 3. Run your Agent Concurrently
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+
+    async def process_question(item, agent_instance):
         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
+            return None, None
         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})
+            loop = asyncio.get_event_loop()
+            # Run the synchronous agent call in a thread pool executor
+            submitted_answer = await loop.run_in_executor(None, agent_instance, question_text)
+            return {"task_id": task_id, "submitted_answer": submitted_answer}, \
+                   {"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}
         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}"})
+            print(f"Error running agent on task {task_id}: {e}")
+            return None, {"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"}
+
+    tasks = [process_question(item, agent) for item in questions_data]
+    processed_results = await asyncio.gather(*tasks)
+
+    for payload, log_entry in processed_results:
+        if payload: answers_payload.append(payload)
+        if log_entry: results_log.append(log_entry)
 
     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)
-
+    return False
     # 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}")
-    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}."
+    async with aiohttp.ClientSession() as session:
+        # 5. Submit
+        print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
         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
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
+            async with session.post(submit_url, json=submission_data, timeout=aiohttp.ClientTimeout(total=60)) as response:
+                response.raise_for_status()
+                result_data = await 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.")
+                return final_status, pd.DataFrame(results_log)
+        except aiohttp.ClientResponseError as e:
+            error_detail = f"Server responded with status {e.status}."
+            try:
+                # Try to get JSON error detail, then text
+                error_body = await e.json()
+                error_detail += f" Detail: {error_body.get('detail', str(error_body))}"
+            except Exception: # If not JSON or other error
+                try:
+                    error_text = (await response.text())[:500]
+                    error_detail += f" Response: {error_text}"
+                except:
+                     error_detail += " Could not retrieve error response body."
+            status_message = f"Submission Failed: {error_detail}"
+        except asyncio.TimeoutError:
+            status_message = "Submission Failed: The request timed out."
+        except aiohttp.ClientError as e:
+            status_message = f"Submission Failed: Network error - {e}"
+        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 status_message, pd.DataFrame(results_log)
 
 
 # --- Build Gradio Interface using Blocks ---

requirements.txt

@@ -1,4 +1,7 @@
 gradio
 requests
 torch
-transformers 
+transformers 
+duckduckgo-search
+aiohttp
+smolgent # Add smolgent