Aggiungi utilizzo di modello locale

app.py

@@ -4,7 +4,7 @@ import requests
 import inspect
 import pandas as pd
 
-from smolagents import CodeAgent, InferenceClientModel, VisitWebpageTool, PythonInterpreterTool, WebSearchTool, WikipediaSearchTool, FinalAnswerTool, Tool, tool # GoogleSearchTool (usa SERPAPI_API_KEY), DuckDuckGoSearchTool
+from smolagents import CodeAgent, HfApiModel, VisitWebpageTool, PythonInterpreterTool, WebSearchTool, WikipediaSearchTool, FinalAnswerTool, Tool, tool # GoogleSearchTool (usa SERPAPI_API_KEY), DuckDuckGoSearchTool
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -12,232 +12,255 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 @tool
 def invert_sentence(sentence: str) -> str:
-    """
-    Inverts the order of all characters in a sentence.
-    Args:
-        sentence (str): The sentence to invert.
-    Returns:
-        str: The sentence with characters in reverse order.
-    """
-    return sentence[::-1]
+	"""
+	Inverts the order of all characters in a sentence.
+	Args:
+		sentence (str): The sentence to invert.
+	Returns:
+		str: The sentence with characters in reverse order.
+	"""
+	return sentence[::-1]
 
 # --- First Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class FirstAgent:
-    ### First Agent is the first attempt to develop an agent for the course. ###
-    def __init__(self):
-        # Usa un modello Hugging Face gratuito
-        token = os.getenv(os.getenv("TOKEN_NAME"))
-        os.environ["HF_TOKEN"] = token
-        model = InferenceClientModel(
-            token=token
-        )
-        
-        self.agent = CodeAgent(
-            model=model,
-            tools=[
-                # DuckDuckGoSearchTool(),
-                # GoogleSearchTool(),
-                WebSearchTool(),
-                PythonInterpreterTool(),
-                WikipediaSearchTool(),
-                VisitWebpageTool() #,
-                # FinalAnswerTool #,
-                # Tool(name="invert_sentence", func=invert_sentence, description="Inverts the order of characters in a sentence.")
-            ]
-        )
-        print("FirstAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        answer = self.agent.run(question)
-        print(f"Agent returning fixed answer: {answer}")
-        return answer
+	### First Agent is the first attempt to develop an agent for the course. ###
+	def __init__(self):
+		# # Usa un modello Hugging Face gratuito
+		# token = os.getenv(os.getenv("TOKEN_NAME"))
+		# os.environ["HF_TOKEN"] = token
+		# model = InferenceClientModel(
+		#	 token=token
+		# )
+
+		# Configurazione con fallback multipli
+		model = None
+		
+		# Try 1: Modello locale via Transformers
+		try:
+			from transformers import pipeline
+			model = HfApiModel(
+				model_id="microsoft/DialoGPT-small"  # ~500MB, scaricato automaticamente
+			)
+			print("Using local DialoGPT model")
+		except Exception as e:
+			print(f"Local model failed: {e}")
+			
+			# Try 2: Modello remoto gratuito
+			try:
+				model = LiteLLMModel(
+					model_id="groq/mixtral-8x7b-32768"  # Gratuito con registrazione
+				)
+				print("Using Groq remote model")
+			except Exception as e:
+				print(f"Remote model failed: {e}")
+				raise Exception("No working model configuration found")
+		
+		self.agent = CodeAgent(
+			model=model,
+			tools=[
+				# DuckDuckGoSearchTool(),
+				# GoogleSearchTool(),
+				WebSearchTool(),
+				PythonInterpreterTool(),
+				WikipediaSearchTool(),
+				VisitWebpageTool() #,
+				# FinalAnswerTool #,
+				# Tool(name="invert_sentence", func=invert_sentence, description="Inverts the order of characters in a sentence.")
+			]
+		)
+		print("FirstAgent initialized.")
+	def __call__(self, question: str) -> str:
+		print(f"Agent received question (first 50 chars): {question[:50]}...")
+		answer = self.agent.run(question)
+		print(f"Agent returning fixed answer: {answer}")
+		return answer
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
-    ### Basic Agent is a placeholder for a simple agent that always returns a fixed answer. ###
-    ### It is used to demonstrate the structure of an agent. ###
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+	### Basic Agent is a placeholder for a simple agent that always returns a fixed answer. ###
+	### It is used to demonstrate the structure of an agent. ###
+	def __init__(self):
+		print("BasicAgent initialized.")
+	def __call__(self, question: str) -> str:
+		print(f"Agent received question (first 50 chars): {question[:50]}...")
+		fixed_answer = "This is a default answer."
+		print(f"Agent returning fixed answer: {fixed_answer}")
+		return fixed_answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-    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"
-
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = FirstAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    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
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    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:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-        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})
-        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}"})
-
-    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)
-
-    # 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}."
-        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}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+	"""
+	Fetches all questions, runs the BasicAgent on them, submits all answers,
+	and displays the results.
+	"""
+	# --- Determine HF Space Runtime URL and Repo URL ---
+	space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+	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"
+
+	# 1. Instantiate Agent ( modify this part to create your agent)
+	try:
+		agent = FirstAgent()
+	except Exception as e:
+		print(f"Error instantiating agent: {e}")
+		return f"Error initializing agent: {e}", None
+	# In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+	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
+	results_log = []
+	answers_payload = []
+	print(f"Running agent on {len(questions_data)} questions...")
+	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:
+			print(f"Skipping item with missing task_id or question: {item}")
+			continue
+		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})
+		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}"})
+
+	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)
+
+	# 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}."
+		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}"
+		print(status_message)
+		results_df = pd.DataFrame(results_log)
+		return status_message, results_df
 
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
+	gr.Markdown("# Basic Agent Evaluation Runner")
+	gr.Markdown(
+		"""
+		**Instructions:**
 
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+		1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+		2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+		3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
 
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        """
-    )
+		---
+		**Disclaimers:**
+		Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+		This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+		"""
+	)
 
-    gr.LoginButton()
+	gr.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+	run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+	status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+	# Removed max_rows=10 from DataFrame constructor
+	results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+	run_button.click(
+		fn=run_and_submit_all,
+		outputs=[status_output, results_table]
+	)
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    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 repo URLs if SPACE_ID is found
-        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)
+	print("\n" + "-"*30 + " App Starting " + "-"*30)
+	# Check for SPACE_HOST and SPACE_ID at startup for information
+	space_host_startup = os.getenv("SPACE_HOST")
+	space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+
+	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 repo URLs if SPACE_ID is found
+		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)

requirements.txt

@@ -3,5 +3,15 @@ requests
 pandas
 smolagents
 wikipedia-api
+# Dipendenze per modelli locali
+transformers
+torch
+tokenizers
+# Dipendenze per LiteLLM (modelli multipli)
+litellm
+# Hugging Face Hub per download modelli
+huggingface_hub
+# Opzionali ma utili
 # duckduckgo-search
-# huggingface_hub
+# accelerate  # Per modelli più grandi
+# bitsandbytes  # Per quantizzazione