Update app.py

app.py

@@ -9,6 +9,15 @@ import requests
 from typing import Dict
 import cv2
 from time import sleep
+from langchain_core.prompts import PromptTemplate
+from langchain.chains import LLMChain
+import time
+import functools
+import hashlib
+import re
+import json
+import hashlib
+from typing import Callable
 
 class GeminiLLM(LLM):
     """Wrapper para usar Google Gemini como un LLM de LangChain."""
@@ -422,30 +431,18 @@ def find_non_commutative_pairs(table: Dict[str, Dict[str, str]]) -> List[tuple]:
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-from langchain_core.prompts import PromptTemplate
-from langchain.chains import LLMChain
 
-import time
 
-from langchain_core.prompts import PromptTemplate
-from langchain.chains import LLMChain
-import time
-import functools
-import hashlib
-import json
-
-import re
-import json
-import hashlib
-import time
-from typing import Callable
+# --- Helper para describir las tools ---
+def describe_tool(func: Callable) -> str:
+    name = func.__name__
+    sig = str(inspect.signature(func))
+    doc = func.__doc__.strip().split('\n')[0] if func.__doc__ else "No description"
+    return f"- {name}{sig}: {doc}"
 
 class BasicAgent:
-    def __init__(self, llm=None, prompt_template=None):
+    def __init__(self, llm=None, max_iterations=5):
         self.llm = llm or GeminiLLM()
-        prompt_template = prompt_template or PromptTemplate.from_template("{question}")
-        self.chain = LLMChain(prompt=prompt_template, llm=self.llm)
-
         self.tools = {
             "wiki_search": wiki_search,
             "load_file": load_file,
@@ -468,8 +465,21 @@ class BasicAgent:
             "filter_by_numeric_range": filter_by_numeric_range,
             "classify_items_by_list": classify_items_by_list,
         }
-
+        # Cache para llamadas a tools
         self._cache = {}
+        self.max_iterations = max_iterations
+
+        # Construir prompt dinámico con info de tools
+        tools_desc = "\n".join(describe_tool(f) for f in self.tools.values())
+        prompt_str = (
+            "You can use the following tools by calling them with syntax:\n"
+            "tool:<tool_name>(arg1,arg2,...)\n\n"
+            "Available tools:\n"
+            f"{tools_desc}\n\n"
+            "Question: {{question}}\nAnswer:"
+        )
+        self.prompt_template = PromptTemplate.from_template(prompt_str)
+        self.chain = LLMChain(prompt=self.prompt_template, llm=self.llm)
 
     def register_tool(self, name: str, func: Callable):
         self.tools[name] = func
@@ -482,174 +492,68 @@ class BasicAgent:
 
     def call_tool(self, tool_name: str, *args, **kwargs):
         func = self.tools.get(tool_name)
-        if func is None:
-            msg = f"Tool '{tool_name}' not found."
-            print(f"[LOG] {msg}")
-            return msg
+        if not func:
+            return f"Tool '{tool_name}' not found."
 
         key = self._cache_key(tool_name, args, kwargs)
         if key in self._cache:
-            print(f"[LOG] Returning cached result for tool '{tool_name}'")
             return self._cache[key]
 
-        print(f"[LOG] Calling tool: '{tool_name}' with args={args} kwargs={kwargs}")
         try:
             result = func(*args, **kwargs)
-            print(f"[LOG] Tool '{tool_name}' returned: {result}")
             self._cache[key] = result
             return result
         except Exception as e:
-            print(f"[ERROR] Tool '{tool_name}' raised exception: {e}")
             return f"Error executing tool '{tool_name}': {e}"
 
-    def __call__(self, question: str) -> str:
-        print(f"[LOG] Processing question: {question!r}")
-
-        # Patrón para detectar: tool:<tool_name>(arg1,arg2,...)
-        pattern = r"tool:(\w+)\((.*?)\)"
-        
-        def tool_replacer(match):
-            tool_name = match.group(1)
-            args_str = match.group(2)
-            # Parseamos args separando por comas, eliminamos espacios externos
-            args = [arg.strip() for arg in args_str.split(",")] if args_str else []
-            print(f"[LOG] Detected tool call: {tool_name} with args={args}")
-            result = self.call_tool(tool_name, *args)
-            return str(result)
-
-        # Reemplazamos todas las invocaciones de tools en la pregunta
-        processed_question = re.sub(pattern, tool_replacer, question)
-
-        time.sleep(3)  # Simula latencia opcional
-
+    def _parse_arg(self, arg: str):
+        arg = arg.strip()
+        if arg.lower() == "true":
+            return True
+        if arg.lower() == "false":
+            return False
         try:
-            response = self.chain.run({"question": processed_question})
-            print(f"[LOG] LLM response: {response}")
-            return response
-        except Exception as e:
-            print(f"[ERROR] LLM chain failed: {e}")
-            return f"Error processing question: {e}"
+            return int(arg)
+        except:
+            pass
+        try:
+            return float(arg)
+        except:
+            pass
+        if (arg.startswith('"') and arg.endswith('"')) or (arg.startswith("'") and arg.endswith("'")):
+            return arg[1:-1]
+        # Intentar JSON para listas o dicts
+        try:
+            return json.loads(arg)
+        except:
+            pass
+        return arg
+
+    def _run_once(self, text: str) -> (str, bool):
+        # Ejecuta una iteración: LLM + ejecución tools
+        llm_out = self.chain.run({"question": text})
+        pattern = r"tool:(\w+)\((.*?)\)"
+        tools_called = False
 
+        def repl(m):
+            nonlocal tools_called
+            tools_called = True
+            tool_name = m.group(1)
+            args_raw = m.group(2)
+            args = [self._parse_arg(a) for a in re.findall(r'(?:[^,"]|"(?:\\.|[^"])*")+', args_raw)] if args_raw.strip() else []
+            res = self.call_tool(tool_name, *args)
+            return str(res)
 
-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
+        processed = re.sub(pattern, repl, llm_out)
+        return processed, tools_called
 
-    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 = BasicAgent()
-    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
+    def __call__(self, question: str) -> str:
+        text = question
+        for i in range(self.max_iterations):
+            text, used_tools = self._run_once(text)
+            if not used_tools:
+                break
+        return text
 
 
 # --- Build Gradio Interface using Blocks ---