fix : frontend back url

.env.example

@@ -3,6 +3,7 @@ REDIS_BASE_URL="redis://redis:6379/0"
 WORK_DIR="/Users/username/Documents/workspace_with_my_files"
 OLLAMA_PORT="11434"
 LM_STUDIO_PORT="1234"
+BACKEND_PORT="7777"
 CUSTOM_ADDITIONAL_LLM_PORT="11435"
 OPENAI_API_KEY='xxxxx'
 DEEPSEEK_API_KEY='xxxxx'

README.md

@@ -4,7 +4,7 @@
 <img align="center" src="./media/agentic_seek_logo.png" width="300" height="300" alt="Agentic Seek Logo">
 <p>
 
-  English | [中文](./README_CHS.md) | [繁體中文](./README_CHT.md) | [Français](./README_FR.md) | [日本語](./README_JP.md) | [Português (Brasil)](./README_PTBR.md)
+  English | [中文](./README_CHS.md) | [繁體中文](./README_CHT.md) | [Français](./README_FR.md) | [日本語](./README_JP.md) | [Português (Brasil)](./README_PTBR.md) | [Español](./README_ES.md)
 
 *A **100% local alternative to Manus AI**, this voice-enabled AI assistant autonomously browses the web, writes code, and plans tasks while keeping all data on your device. Tailored for local reasoning models, it runs entirely on your hardware, ensuring complete privacy and zero cloud dependency.*
 
@@ -167,7 +167,7 @@ Next step: [Start services and run AgenticSeek](#Start-services-and-Run)
 
 *See the **Run with an API** section if your hardware can't run deepseek locally*
 
-*See the **Config** section for detailled config file explanation.*
+*See the **Config** section for detailed config file explanation.*
 
 ---
 
@@ -206,7 +206,7 @@ Next step: [Start services and run AgenticSeek](#Start-services-and-Run)
 
 *See the **Known issues** section if you are having issues*
 
-*See the **Config** section for detailled config file explanation.*
+*See the **Config** section for detailed config file explanation.*
 
 ---
 

api.py

@@ -255,5 +255,5 @@ if __name__ == "__main__":
     if envport:
         port = int(envport)
     else:
-        port = 8000
+        port = 7777
-    uvicorn.run(api, host="0.0.0.0", port=8000)
+    uvicorn.run(api, host="0.0.0.0", port=7777)

docker-compose.yml

@@ -65,7 +65,7 @@ services:
     environment:
       - NODE_ENV=development
       - CHOKIDAR_USEPOLLING=true
-      - REACT_APP_BACKEND_URL=http://0.0.0.0:${BACKEND_PORT:-8000}
+      - REACT_APP_BACKEND_URL=http://host.docker.internal:${BACKEND_PORT:-7777}
     networks:
       - agentic-seek-net
 
@@ -88,6 +88,7 @@ services:
       - SEARXNG_URL=${SEARXNG_BASE_URL:-http://searxng:8080}
       - REDIS_URL=${REDIS_BASE_URL:-redis://redis:6379/0}
       - WORK_DIR=/opt/workspace
+      - BACKEND_PORT=${BACKEND_PORT}
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}
       - OPENROUTER_API_KEY=${OPENROUTER_API_KEY}
@@ -96,7 +97,8 @@ services:
       - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
       - HUGGINGFACE_API_KEY=${HUGGINGFACE_API_KEY}
       - DSK_DEEPSEEK_API_KEY=${DSK_DEEPSEEK_API_KEY}
-    network_mode: "host"
+    networks:
+      - agentic-seek-net
   
 volumes:
   redis-data:

frontend/agentic-seek-front/src/App.js

@@ -4,7 +4,8 @@ import axios from 'axios';
 import './App.css';
 import { colors } from './colors';
 
-const BACKEND_URL = process.env.BACKEND_PORT || 'http://0.0.0.0:8000';
+const BACKEND_URL = 'http://localhost:7777' || process.env.REACT_APP_BACKEND_URL;
+console.log('Using backend URL:', BACKEND_URL);
 
 function App() {
     const [query, setQuery] = useState('');

llm_router/dl_safetensors.sh

@@ -1,6 +1,6 @@
 ##########
 # Dummy script to download the model
-# Because dowloading with hugging face does not seem to work, maybe I am doing something wrong?
+# Because downloading with hugging face does not seem to work, maybe I am doing something wrong?
 # AdaptiveClassifier.from_pretrained("adaptive-classifier/llm-router") ----> result in config.json not found
 # Therefore, I put all the files in llm_router and download the model file with this script, If you know a better way please raise an issue
 #########

llm_router/examples.json

@@ -6966,7 +6966,7 @@
         0.010188529267907143
       ],
       "label": "LOW",
-      "text": "You are AoControl, a helpful and knowledgeable agent. To achieve your goal of answering complex questions correctly, you have access to the following tools:\n    AoInnovus: An agent with knowledge of the Cadence Innovus User Guide and corresponding Tcl command usage\n    AoTcl: An agent with knowledge of the Tcl language\nTo answer questions, you'll need to go through multiple steps involving step-by-step thinking and selecting appropriate tools and their inputs; tools will respond with observations.\nWhen you are ready for a final answer, respond with the `Final Answer:`\nUse the following format:\nQuestion: The question to be answered\nTHought: Reason if you have the final answer. If yes, anwer the question. If not, find out the missing information needed to answer it.\nTool: Pick one of {AoInnovus, AoTcl}\nTool Input: The input for the tool\nObservation: The tool will respond with the result\n...\nFinal Answer: The final answer to the question, make it short (50-100 words)\nThought, Tool, Tool Input, and Observation steps can be repeated multiple times, but sometimes we can find an answer in the first pass.\n---\nQuestion: How do I identify the impact of metal fill on my timing violations?\nThought: Let's think step-by-step, I first need to"
+      "text": "You are AoControl, a helpful and knowledgeable agent. To achieve your goal of answering complex questions correctly, you have access to the following tools:\n    AoInnovus: An agent with knowledge of the Cadence Innovus User Guide and corresponding Tcl command usage\n    AoTcl: An agent with knowledge of the Tcl language\nTo answer questions, you'll need to go through multiple steps involving step-by-step thinking and selecting appropriate tools and their inputs; tools will respond with observations.\nWhen you are ready for a final answer, respond with the `Final Answer:`\nUse the following format:\nQuestion: The question to be answered\nTHought: Reason if you have the final answer. If yes, answer the question. If not, find out the missing information needed to answer it.\nTool: Pick one of {AoInnovus, AoTcl}\nTool Input: The input for the tool\nObservation: The tool will respond with the result\n...\nFinal Answer: The final answer to the question, make it short (50-100 words)\nThought, Tool, Tool Input, and Observation steps can be repeated multiple times, but sometimes we can find an answer in the first pass.\n---\nQuestion: How do I identify the impact of metal fill on my timing violations?\nThought: Let's think step-by-step, I first need to"
     },
     {
       "embedding": [

prompts/base/coder_agent.txt

@@ -20,7 +20,7 @@ toto.py
 You can execute bash command using the bash tag :
 ```bash
 #!/bin/bash
-ls -la # exemple
+ls -la # example
 ```
 
 You can execute python using the python tag
@@ -48,5 +48,5 @@ Some rules:
 - You do not ever need to use bash to execute code.
 - Do not ever tell user how to run it. user know it.
 - If using gui, make sure echap or exit button close the program
-- No lazyness, write and rewrite full code every time
+- No laziness, write and rewrite full code every time
 - If query is unclear say REQUEST_CLARIFICATION

prompts/base/planner_agent.txt

@@ -29,7 +29,7 @@ You: Sure, here is the plan:
 
 ## Task 3: I will setup the project using the file agent 
 
-## Task 4: I asign the coding agent to make a weather app in python
+## Task 4: I assign the coding agent to make a weather app in python
 
 ```json
 {
@@ -74,7 +74,7 @@ Rules:
 - Put your plan in a json with the key "plan".
 - specify work folder name to all coding or file agents.
 - You might use a file agent before code agent to setup a project properly. specify folder name.
-- Give clear, detailled order to each agent and how their task relate to the previous task (if any).
+- Give clear, detailed order to each agent and how their task relate to the previous task (if any).
 - The file agent can only conduct one action at the time. successive file agent could be needed.
 - Only use web agent for finding necessary informations.
 - Always tell the coding agent where to save file.

sources/agents/planner_agent.py

@@ -200,7 +200,7 @@ class PlannerAgent(Agent):
         {last_agent_work}
         Agent {id} work was a {tool_success_str} according to system interpreter.
         {next_task}
-        Is the work done for task {id} leading to sucess or failure ? Did an agent fail with a task?
+        Is the work done for task {id} leading to success or failure ? Did an agent fail with a task?
         If agent work was good: answer "NO_UPDATE"
         If agent work is leading to failure: update the plan.
         If a task failed add a task to try again or recover from failure. You might have near identical task twice.

sources/text_to_speech.py

@@ -114,7 +114,7 @@ class Speech():
     def shorten_paragraph(self, sentence):
         #TODO find a better way, we would like to have the TTS not be annoying, speak only useful informations
         """
-        Find long paragraph like **explaination**: <long text> by keeping only the first sentence.
+        Find long paragraph like **explanation**: <long text> by keeping only the first sentence.
         Args:
             sentence (str): The sentence to shorten
         Returns:

sources/tools/mcpFinder.py

@@ -76,7 +76,7 @@ class MCP_finder(Tools):
             try:
                 matching_mcp_infos = self.find_mcp_servers(block_clean)
             except requests.exceptions.RequestException as e:
-                output += "Connection failed. Is the API key in environement?\n"
+                output += "Connection failed. Is the API key in environment?\n"
                 continue
             except Exception as e:
                 output += f"Error: {str(e)}\n"

start_services.cmd

@@ -3,26 +3,24 @@
 if "%1"=="full" (
     echo Starting full deployment...
 ) else (
-    echo Starting partial deployment... (backend run on host), use "full" to run all services in containers
+    set "msg=Starting partial deployment... (backend run on host), use "full" to run all services in containers"
+    echo !msg!
 )
 
+
 where openssl >nul 2>&1
 if %ERRORLEVEL% == 0 (
     for /f %%i in ('openssl rand -hex 32') do set SEARXNG_SECRET_KEY=%%i
 ) else (
-    where python3 >nul 2>&1
+    where python >nul 2>&1
     if %ERRORLEVEL% == 0 (
-        for /f %%i in ('python3 -c "import secrets; print(secrets.token_hex(32))"') do set SEARXNG_SECRET_KEY=%%i
+        for /f %%i in ('python -c "import secrets; print(secrets.token_hex(32))"') do set SEARXNG_SECRET_KEY=%%i
     ) else (
         echo Error: Neither openssl nor python is available to generate a secret key.
-        exit /b 1
+        exit /b 2
     )
 )
 
-REM Stop all containers
-echo Stopping containers...
-docker stop $(docker ps -aq) >nul 2>&1
-
 REM Generate secret key
 for /f %%i in ('powershell -command "[System.Web.Security.Membership]::GeneratePassword(64,0)"') do set SEARXNG_SECRET_KEY=%%i
 

start_services.sh

@@ -105,7 +105,7 @@ fi
 
 if [ "$1" = "full" ]; then
     # First start backend and wait for it to be healthy
-    echo "Full docker deployement. Starting backend service..."
+    echo "Full docker deployment. Starting backend service..."
     if ! $COMPOSE_CMD up -d backend; then
         echo "Error: Failed to start backend container."
         exit 1