MOdifica applicazione per usare un modello locale

app.py

@@ -1,10 +1,14 @@
 import os
+import json
 import gradio as gr
 import requests
 import inspect
 import pandas as pd
 
-from smolagents import CodeAgent, HfApiModel, VisitWebpageTool, PythonInterpreterTool, WebSearchTool, WikipediaSearchTool, FinalAnswerTool, Tool, tool # GoogleSearchTool (usa SERPAPI_API_KEY), DuckDuckGoSearchTool
+from smolagents import CodeAgent, InferenceClientModel, VisitWebpageTool, PythonInterpreterTool, WebSearchTool, WikipediaSearchTool, FinalAnswerTool, Tool, tool # GoogleSearchTool (usa SERPAPI_API_KEY), DuckDuckGoSearchTool
+from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from litellm import LiteLLM
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -35,25 +39,29 @@ class FirstAgent:
 
 		# 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
+			model_id = "microsoft/DialoGPT-small"
+			tokenizer = AutoTokenizer.from_pretrained(model_id)
+			model = AutoModelForCausalLM.from_pretrained(model_id) # ~500MB
+			model = pipeline(
+				task="text-generation",
+				tokenizer=tokenizer,
+				model=model
 			)
-			print("Using local DialoGPT model")
+			print(f"Using local {model_id} model")
 		except Exception as e:
 			print(f"Local model failed: {e}")
-			
+
 			# Try 2: Modello remoto gratuito
 			try:
-				model = LiteLLMModel(
+				model = LiteLLM(
 					model_id="groq/mixtral-8x7b-32768"  # Gratuito con registrazione
 				)
 				print("Using Groq remote model")
-			except Exception as e:
-				print(f"Remote model failed: {e}")
+			except Exception as ex:
+				print(f"Remote model failed: {ex}")
 				raise Exception("No working model configuration found")
 		
 		self.agent = CodeAgent(
@@ -125,19 +133,55 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 		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("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
+		print(f"Error fetching questions from API: {e}")
+		print("Attempting to load questions from local file 'questions.json'...")
+		try:
+			with open("questions.json", "r", encoding="utf-8") as f:
+				questions_data = json.load(f)
+			if not questions_data:
+				return "Both API and local questions file are empty.", None
+			print(f"Successfully loaded {len(questions_data)} questions from local file.")
+		except FileNotFoundError:
+			return "Error: Could not fetch questions from API and 'questions.json' file not found.", None
+		except json.JSONDecodeError as json_e:
+			return f"Error: Could not fetch questions from API and local file has invalid JSON: {json_e}", None
+		except Exception as file_e:
+			return f"Error: Could not fetch questions from API and failed to read local file: {file_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
+		print(f"Error decoding JSON response from questions endpoint: {e}")
+		print(f"Response text: {response.text[:500]}")
+		print("Attempting to load questions from local file 'questions.json'...")
+		try:
+			with open("questions.json", "r", encoding="utf-8") as f:
+				questions_data = json.load(f)
+			if not questions_data:
+				return "Both API response is invalid and local questions file is empty.", None
+			print(f"Successfully loaded {len(questions_data)} questions from local file.")
+		except FileNotFoundError:
+			return "Error: Could not decode API response and 'questions.json' file not found.", None
+		except json.JSONDecodeError as json_e:
+			return f"Error: Could not decode API response and local file has invalid JSON: {json_e}", None
+		except Exception as file_e:
+			return f"Error: Could not decode API response and failed to read local file: {file_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
+		print("Attempting to load questions from local file 'questions.json'...")
+		try:
+			with open("questions.json", "r", encoding="utf-8") as f:
+				questions_data = json.load(f)
+			if not questions_data:
+				return "Unexpected API error occurred and local questions file is empty.", None
+			print(f"Successfully loaded {len(questions_data)} questions from local file.")
+		except FileNotFoundError:
+			return "Error: Unexpected API error occurred and 'questions.json' file not found.", None
+		except json.JSONDecodeError as json_e:
+			return f"Error: Unexpected API error occurred and local file has invalid JSON: {json_e}", None
+		except Exception as file_e:
+			return f"Error: Unexpected API error occurred and failed to read local file: {file_e}", None
 
 	# 3. Run your Agent
 	results_log = []

requirements.txt

@@ -9,9 +9,11 @@ torch
 tokenizers
 # Dipendenze per LiteLLM (modelli multipli)
 litellm
-# Hugging Face Hub per download modelli
+# Hugging Face Hub per download modelli e API
 huggingface_hub
+# Dipendenze per HfApiModel e integrazione completa HF
+datasets
+accelerate
 # Opzionali ma utili
 # duckduckgo-search
-# accelerate  # Per modelli più grandi
 # bitsandbytes  # Per quantizzazione