Update app.py

app.py

@@ -1,42 +1,49 @@
-import gradio as gr
-import requests
 import os
-from groq import Groq  # Assuming Groq or a similar LLM can provide insights
+import requests
+import gradio as gr
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
 
-def get_weather_and_insight(city_name):
-    # Initialize the Groq client with your API key
-    groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))
-    
-    # Use Groq to get a short insight about the city
-    insight = groq_client.get_insight(city_name, max_words=10)  # Assumed method and parameter
-    
-    API_Key = os.getenv("OPENWEATHER_API_KEY")
-    if not API_Key:
-        return "API Key is not set. Please set the OPENWEATHER_API_KEY environment variable."
-        
-    url = f'https://api.openweathermap.org/data/2.5/weather?q={city_name}&appid={API_Key}&units=metric'
-    response = requests.get(url)
+def get_weather_and_insight(city):
+    # Fetch weather data
+    url = f'http://api.openweathermap.org/data/2.5/weather?q={city}&appid={os.getenv("OPENWEATHER_API_KEY")}&units=metric'
+    res = requests.get(url)
+    data = res.json()
+
+    if res.status_code != 200:
+        return "Failed to retrieve weather data. Please check the city name and try again."
+
+    humidity = data.get('main', {}).get('humidity')
+    temp = data.get('main', {}).get('temp')
+    description = data.get('weather', [{}])[0].get('description')
+
+    # Generate a creative weather summary using LangChain LLM
+    groq_api_key = os.getenv("GROQ_API_KEY")
+    system = f"You are a weather expert reporter of {city}. You must compile a short weather summary based on information like {temp} degree celsius is the current temperature of the city, {humidity} percent is the humidity percentage for the city. The weather of the city can be best described as {description}."
+    human = "{text}"
+    prompt = ChatPromptTemplate.from_messages(
+        [("system", system), ("human", human)]
+    )
+    chat = ChatGroq(api_key=groq_api_key, model_name="llama3-70b-8192")
+    chain = prompt | chat | StrOutputParser()
+    output = chain.invoke({"text": city})
+
+    # Add emojis for visual flair
+    weather_emoji = "☀️" if "clear" in description else "☁️" if "cloud" in description else "🌧️" if "rain" in description else "❄️" if "snow" in description else "🌫️"
     
-    if response.status_code == 200:
-        data = response.json()
-        weather = data['weather'][0]['description']
-        temp = data['main']['temp']
-        humidity = data['main']['humidity']
-
-        weather_emoji = "☀️" if "clear" in weather else "☁️" if "cloud" in weather else "🌧️" if "rain" in weather else "❄️" if "snow" in weather else "🌫️"
-        return (f"🔍 {insight} | In {city_name}, it's {weather} {weather_emoji}. "
-                f"Temperature: {temp}°C 🌡️, Humidity: {humidity}% 💧")
-    else:
-        return "Failed to retrieve data. Please check the city name and try again."
-
-# Define Gradio interface
+    # Create the final response string
+    final_response = f"🌍 {city.upper()} Weather Insight: {output} {weather_emoji}\n🌡️ Temp: {temp}°C, 💧 Humidity: {humidity}%."
+
+    return final_response
+
+# Gradio Interface
 iface = gr.Interface(
     fn=get_weather_and_insight,
-    inputs=gr.Textbox(label="Enter City Name", placeholder="Type here..."),
-    outputs=gr.Textbox(label="Weather Update and Insight"),
+    inputs=gr.Textbox(label="Enter City Name", placeholder="Type city here..."),
+    outputs=gr.Textbox(label="Weather Report and Insight"),
     title="WeatherAssistantApp",
-    description="Enter a city name to get an interesting insight and the current weather details."
+    description="Enter a city name to get a detailed weather report with an AI-generated insight."
 )
 
-# Launch the interface
 iface.launch()