codigo inicial agente com langchain

app.py

@@ -4,22 +4,60 @@ import requests
 import inspect
 import pandas as pd
 
+# --- LangChain Imports ---
+import constants
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.agents import AgentExecutor, create_tool_calling_agent
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import SystemMessage
+
+# --- Custom Tools ---
+from wikipedia_tool import wikipedia_revision_by_year_keyword
+from count_max_distinct_bird_species_tool import count_max_distinct_bird_species_in_video
+from image_to_text_tool import image_to_text
+from internet_search_tool import internet_search
+from botanical_classification_tool import get_botanical_classification
+from excel_parser_tool import parse_excel
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-#testing #test
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
+class LangChainAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        llm = ChatGoogleGenerativeAI(
+            model=constants.MODEL,
+            api_key=constants.API_KEY, 
+            temperature=0.7)
+
+        tools = [
+            wikipedia_revision_by_year_keyword,
+            count_max_distinct_bird_species_in_video,
+            image_to_text,
+            internet_search,
+            get_botanical_classification,
+            parse_excel
+        ]
+
+        prompt = ChatPromptTemplate.from_messages([
+            SystemMessage(content=(constants.PROMPT_LIMITADOR_LLM)),
+            MessagesPlaceholder(variable_name="chat_history"),
+            ("human", "{input}"),
+            MessagesPlaceholder(variable_name="agent_scratchpad"),
+        ])
+
+        agent = create_tool_calling_agent(llm, tools, prompt=prompt)
+        self.executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
     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
+        print(f"LangChain agent received: {question[:50]}...")
+        result = self.executor.invoke({
+            "input": question,
+            "chat_history": []
+        })
+        answer = result.get("output", "No answer returned.")
+        print(f"Agent response: {answer}")
+        return answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -42,10 +80,11 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 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
+        agent = LangChainAgent()
+	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)

constants.py

@@ -1,3 +1,5 @@
+import os
+
 # MODEL = 'gemini-2.0-flash'
 MODEL = 'gemini-2.0-flash-exp'
 # MODEL = 'gemini-2.5-pro-exp-03-25'
@@ -5,9 +7,7 @@ MODEL = 'gemini-2.0-flash-exp'
 # gemini-2.0-flash
 # gemini-2.5-pro-exp-03-25
 
-API_KEY = 'AIzaSyDfVJVwfEhMpZCFNg91TWwXBvs0BZsMpPo'
-
-OPENAI_KEY = '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'
+API_KEY =  os.getenv("GEMINI_API_KEY")
 
 PROMPT_LIMITADOR_LLM = """
   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.

requirements.txt

@@ -1,2 +1,17 @@
 gradio
-requests
+requests
+certifi
+duckduckgo_search
+httpx
+langchain
+langchain-core
+langchain-google-genai
+langchain-openai
+langgraph
+numpy
+openai
+pandas
+pytube
+ultralytics
+whisper
+yt-dlp