Update agent.py

agent.py

@@ -1,153 +1,37 @@
 import os
-import json
-import re
-from typing import Tuple, Dict, Any
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM # Import AutoTokenizer and AutoModelForSeq2SeqLM
+from transformers import pipeline
+# Assuming you still want to use your local Flan-T5 model
+# from tools.search_tool import search_duckduckgo # REMOVE THIS LINE
 
-from tools.asr_tool import transcribe_audio
-from tools.excel_tool import analyze_excel
-from tools.search_tool import search_duckduckgo
-from tools.math_tool import calculate_math # Make sure to import your math tool
+# NEW IMPORTS for smolagents
+from smolagents import CodeAgent, DuckDuckGoSearchTool
+from smolagents import TransformersModel # To use your local Hugging Face model
 
 class GaiaAgent:
-    def __init__(self):
-        token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-        if not token:
-            raise ValueError("Missing HUGGINGFACEHUB_API_TOKEN environment variable.")
-
-        # Specify the model and load tokenizer and model separately for better control
-        model_name = "google/flan-t5-large"
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name, token=token)
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name, token=token)
-
-        # Use the pipeline with the loaded model and tokenizer
-        self.llm = pipeline(
-            "text2text-generation",
-            model=self.model,
-            tokenizer=self.tokenizer,
-            device="cpu", # Consider "cuda" if you have a GPU
-            max_new_tokens=256,
-            do_sample=False, # Set to True if you want to use temperature and top_p/k
-            # temperature=0.1, # Removed, as it's not a valid pipeline initialization flag here
+    def __init__(self, model_id: str = "google/flan-t5-large"):
+        # Initialize your LLM using smolagents's TransformersModel
+        # This allows smolagents to manage the interaction with your local model
+        self.llm_model = TransformersModel(model_id=model_id)
+
+        # Initialize the smolagents CodeAgent
+        # Pass the DuckDuckGoSearchTool directly to the agent's tools list
+        # You can add other tools here if needed
+        self.agent = CodeAgent(
+            model=self.llm_model,
+            tools=[DuckDuckGoSearchTool()],
+            # 'add_base_tools=True' can add common basic tools (like a Python interpreter)
+            # You might need to experiment with this. For now, let's keep it explicit.
+            add_base_tools=False, 
+            verbose=True # This is helpful for debugging on Hugging Face Spaces logs
         )
 
-        self.system_prompt = """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""
-
-    def extract_final_answer(self, text: str) -> str:
-        """Extrahera det slutliga svaret från modellens output"""
-        final_answer_match = re.search(r'FINAL ANSWER:\s*(.+?)(?:\n|$)', text, re.IGNORECASE)
-        if final_answer_match:
-            return final_answer_match.group(1).strip()
-        sentences = text.strip().split('\n')
-        return sentences[-1].strip() if sentences else text.strip()
-
-    def needs_tool(self, question: str) -> Tuple[str, bool]:
-        """Bestäm vilket verktyg som behövs baserat på frågan"""
-        question_lower = question.lower()
-
-        if any(ext in question_lower for ext in ['.mp3', '.wav', '.m4a', '.flac']):
-            return 'audio', True
-        if any(ext in question_lower for ext in ['.xlsx', '.xls', '.csv']):
-            return 'excel', True
-        if any(keyword in question_lower for keyword in ['search', 'find', 'lookup', 'http', 'www.', 'wikipedia', 'albums', 'discography', 'published', 'website']):
-            return 'search', True
-        if any(keyword in question_lower for keyword in ['calculate', 'compute', 'sum', 'average', 'count', 'what is', 'solve']):
-            return 'math', True
-        return 'llm', False
-
-    def process_with_tools(self, question: str, tool_type: str) -> Tuple[str, str]:
-        """Bearbeta frågan med specifika verktyg"""
-        trace_log = f"Detected {tool_type} task. Processing...\n"
-
+    def process_task(self, task_description: str) -> str:
+        # The smolagents agent.run() method handles the entire process
+        # of planning, tool use, and generating a final answer.
         try:
-            if tool_type == 'audio':
-                audio_files = re.findall(r'\b[\w\-_]+\.(mp3|wav|m4a|flac)\b', question, re.IGNORECASE)
-                if audio_files:
-                    result = transcribe_audio(audio_files[0])
-                    trace_log += f"Audio transcription: {result}\n"
-                    return result, trace_log
-                else:
-                    return "No audio file mentioned in the question.", trace_log
-
-            elif tool_type == 'excel':
-                excel_files = re.findall(r'\b[\w\-_]+\.(xlsx|xls|csv)\b', question, re.IGNORECASE)
-                if excel_files:
-                    result = analyze_excel(excel_files[0])
-                    trace_log += f"Excel analysis: {result}\n"
-                    return result, trace_log
-                else:
-                    return "No Excel file mentioned in the question.", trace_log
-
-            elif tool_type == 'search':
-                search_query = question # This might need refinement to extract just the search query
-                result = search_duckduckgo(search_query)
-                trace_log += f"Search results: {result}\n"
-                return result, trace_log
-
-            elif tool_type == 'math':
-                math_expression_match = re.search(r'calculate (.+)', question, re.IGNORECASE)
-                if math_expression_match:
-                    expression = math_expression_match.group(1).strip()
-                    result = calculate_math(expression)
-                    trace_log += f"Math calculation: {result}\n"
-                    return result, trace_log
-                else:
-                    return "No clear mathematical expression found in the question.", trace_log
-
+            # The agent will decide when to use DuckDuckGoSearchTool based on the prompt
+            response = self.agent.run(task_description)
+            return response
         except Exception as e:
-            trace_log += f"Error using {tool_type} tool: {str(e)}\n"
-            return f"Error: {str(e)}", trace_log
-
-        return "No valid input found for tool", trace_log
-
-    def reason_with_llm(self, question: str, context: str = "") -> Tuple[str, str]:
-        """Använd LLM för reasoning med kontext"""
-        trace_log = "Using LLM for reasoning...\n"
-
-        # Combine system prompt, context, and question, ensuring it fits token limit
-        if context:
-            prompt = f"{self.system_prompt}\n\nContext: {context}\n\nQuestion: {question}\n\nPlease analyze this step by step and provide your final answer."
-        else:
-            prompt = f"{self.system_prompt}\n\nQuestion: {question}\n\nPlease analyze this step by step and provide your final answer."
-
-        # Tokenize and truncate if necessary
-        inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=self.tokenizer.model_max_length)
-
-        try:
-            # Generate response using the model's generate method for more control
-            # You can add generation arguments here, e.g., temperature, top_k, etc.
-            outputs = self.model.generate(
-                inputs.input_ids,
-                max_new_tokens=256,
-                do_sample=False, # Set to True to enable temperature and other sampling parameters
-                # temperature=0.1, # Example: Only if do_sample is True
-            )
-            response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-            trace_log += f"LLM response: {response}\n"
-            return response, trace_log
-        except Exception as e:
-            trace_log += f"Error with LLM: {str(e)}\n"
-            return f"Error: {str(e)}", trace_log
-
-    def __call__(self, question: str) -> Tuple[str, str]:
-        """Huvudfunktion som bearbetar frågan"""
-        total_trace = f"Processing question: {question}\n"
-
-        tool_type, needs_tool = self.needs_tool(question)
-        total_trace += f"Tool needed: {tool_type}\n"
-
-        context = ""
-        if needs_tool and tool_type != 'llm':
-            tool_result, tool_trace = self.process_with_tools(question, tool_type)
-            total_trace += tool_trace
-            context = tool_result
-
-        llm_response, llm_trace = self.reason_with_llm(question, context)
-        total_trace += llm_trace
-
-        final_answer = self.extract_final_answer(llm_response)
-        total_trace += f"Final answer extracted: {final_answer}\n"
-
-        return final_answer, total_trace
-        
+            return f"An error occurred during agent processing: {e}"
+