Upload 2 files

agent_api.py

@@ -0,0 +1,164 @@
+# -*- coding: utf-8 -*-
+# This script demonstrates using LangChain, OpenRouter, and weather/news tools in Python.
+# Purpose: Fetch news, search weather, and interact with LLM agents using LangChain.
+
+# Import necessary libraries for LangChain, OpenAI, RSS parsing, HTML cleaning, embeddings, and HTTP requests
+from langchain.agents import initialize_agent
+from langchain.tools import Tool
+from langchain_openai import ChatOpenAI # Import ChatOpenAI from langchain_openai
+import feedparser # For parsing RSS feeds
+from bs4 import BeautifulSoup # For cleaning HTML
+from langchain.schema import Document # For document schema
+from langchain_community.vectorstores import FAISS # For vector storage
+from requests import get # For HTTP requests
+from langchain.agents import AgentType # For agent type
+from langchain.memory import ConversationBufferWindowMemory
+from langchain_community.embeddings import CohereEmbeddings
+from dotenv import load_dotenv
+import os
+
+load_dotenv("./settings.env") 
+
+os.environ['LANGSMITH_TRACING_V2'] = os.getenv('LANGSMITH_TRACING_V2')
+os.environ['LANGSMITH_API_KEY'] = os.getenv('LANGSMITH_API_KEY')
+os.environ['LANGSMITH_ENDPOINT'] = os.getenv('LANGSMITH_ENDPOINT')
+os.environ['LANGSMITH_PROJECT'] = os.getenv('LANGSMITH_PROJECT')
+API_KEY_OPENAI =os.getenv('OPENROUTER_API')
+API_KEY_COHERE = os.getenv('COHERE_API')
+API_KEY_WEATHER = os.getenv('WEATHER_API')
+
+# Initialize the ChatOpenAI model with OpenRouter API key and endpoint
+llm = ChatOpenAI(model_name='gpt-4o-mini',api_key=API_KEY_OPENAI,base_url="https://openrouter.ai/api/v1")
+
+
+
+# Function to fetch news from an RSS feed URL
+def rss_getNews(feed_url):
+    feed = feedparser.parse(feed_url)
+    return [entry['title'] + "\n" + entry['summary'] for entry in feed.entries]
+
+
+# Function to clean HTML content and extract text
+def clean_html(raw_html):
+    soup = BeautifulSoup(raw_html, "html.parser")
+    return soup.get_text(separator="\n")
+
+
+# List of RSS feed URLs to fetch news from
+urls = [
+    'https://www.hespress.com/feed',
+    'https://alyaoum24.com/feed',
+    'https://al3omk.com/feed'
+]
+# Parse feeds and create Document objects for each news entry
+documents = []
+for url in urls:
+    feed = feedparser.parse(url)
+    for entry in feed.entries[:30]:  # Limit to 10 entries per feed
+        tags = [tag.term for tag in entry.tags] if hasattr(entry, 'tags') else []
+        content = f"{entry.title}\n{entry.published}\n{entry.summary}"
+        metadata = {"tags": tags}
+        documents.append(Document(page_content=clean_html(content), metadata=metadata))
+
+
+
+# Initialize Cohere embeddings
+embedding = CohereEmbeddings(cohere_api_key=API_KEY_COHERE,model="embed-multilingual-v3.0",user_agent="langchain")
+# Create FAISS vector store from documents and save locally
+db = FAISS.from_documents(documents, embedding)
+db.save_local('data_news')
+
+
+
+# Create a retriever from the FAISS vector store
+retriever = db.as_retriever()
+# Define a tool for filtered news search using the retriever
+def filtered_retriever_tool():
+    def search_func(query):
+        results = retriever.get_relevant_documents(query)
+        return results
+    return Tool(
+        name='News Search',
+        func=search_func,
+        description="Search news articles by topic and tag"
+    )
+# Initialize the filtered news search tool
+filtered_tool = filtered_retriever_tool()
+
+
+
+
+
+# Format weather data with emojis and print nicely
+def format_weather_with_emoji(weather_data, city_name="Casablanca"):
+    weather_desc = weather_data["weather"][0]["description"]
+    temp = weather_data["main"]["temp"]
+    feels_like = weather_data["main"]["feels_like"]
+    humidity = weather_data["main"]["humidity"]
+    wind_speed = weather_data["wind"]["speed"]
+
+    # Map weather descriptions to emojis
+    emoji_map = {
+        "clear sky": "☀️",
+        "few clouds": "🌤️",
+        "scattered clouds": "⛅",
+        "broken clouds": "☁️",
+        "shower rain": "🌧️",
+        "rain": "🌦️",
+        "thunderstorm": "⛈️",
+        "snow": "❄️",
+        "mist": "🌫️"
+    }
+    emoji = emoji_map.get(weather_desc.lower(), "🌈")  # Default emoji
+    result = (
+        f"🌆 Weather in {city_name}:\n"
+        f" - Condition: {weather_desc} {emoji}\n"
+        f" - Temperature: {temp}°C (feels like {feels_like}°C)\n"
+        f" - Humidity: {humidity}%\n"
+        f" - Wind speed: {wind_speed} m/s\n"
+        f"Have a great day! 😊"
+    )
+
+   
+    return result
+
+
+
+# Function to get coordinates (latitude, longitude) for a city using OpenWeatherMap
+def get_coordinates(city: str):
+    """Automatically fetch coordinates from city name"""
+    url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&APPID={API_KEY_WEATHER}"
+    response = get(url)
+    if response.status_code == 200 and response.json():
+        data = response.json()['coord']
+        return data['lat'],data['lon']
+    else:
+        return response.json()
+# Function to search weather for a city and format the result
+def weather_search_tool(city, part: str = ""):
+    lat,lon =get_coordinates(city)
+    url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&APPID={API_KEY_WEATHER}"
+    response = get(url)
+    if response.status_code == 200:
+        return format_weather_with_emoji(response.json(),city)
+    else:
+        return {"error": response.status_code, "message": response.text}
+
+
+# Create a Tool for weather search with emoji formatting
+weather_search = Tool(
+    name='weather search',
+    func=weather_search_tool,
+    description="🔍 Search weather conditions by location 🌍. The tool fetches current weather data using latitude and longitude coordinates 📍 and explains results with fun emojis ☀️🌧️🌬️. The output is formatted neatly in multiple lines for better readability."
+)
+
+
+# Initialize conversation memory for the agent
+memory = ConversationBufferWindowMemory(memory_key='chat_history',return_messages=True,k=8)
+# Initialize the conversational agent with tools and memory
+agent =  initialize_agent(tools=[filtered_tool,weather_search],llm=llm,memory=memory,agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION)
+
+def sendPromptToAgent(prompt:str):
+# Invoke the agent with a sample query and print the output
+  out=  agent.invoke(prompt)
+  return str(out) 

api.py

@@ -0,0 +1,33 @@
+# app.py
+import gradio as gr
+from agent_api import sendPromptToAgent
+from dotenv import load_dotenv
+import os
+
+# تحميل المتغيرات من .env
+load_dotenv("./settings.env")
+
+# دالة لمعالجة الإدخال من Gradio
+def handle_prompt(prompt):
+    if not prompt.strip():
+        return "❌ الرجاء إدخال نص."
+    
+    try:
+        result = sendPromptToAgent(prompt)
+        return result
+    except Exception as e:
+        return f"❌ حدث خطأ: {e}"
+
+# تصميم واجهة Gradio
+iface = gr.Interface(
+    fn=handle_prompt,
+    inputs=gr.Textbox(lines=3, placeholder="اكتب سؤالك هنا...", label="📨 إدخال النص"),
+    outputs=gr.Textbox(label="💡 الرد من الوكيل"),
+    title="📰 وكيل الأخبار",
+    description="أدخل سؤالك ليقوم الوكيل بالإجابة عليه باستخدام LangChain.",
+    theme="soft"
+)
+
+# تشغيل التطبيق
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)