Create app.py

app.py

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+import httpx
+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_core.messages import BaseMessage, HumanMessage
+from langgraph.graph import MessageGraph, END
+from typing import Sequence
+
+# streamlit app
+st.title("City Weather Information with AI Review")
+OPENWEATHER_API_KEY = st.sidebar.text_input("Enter Weather API Key", type="password")
+st.sidebar.write("Check out this [Weather API](https://home.openweathermap.org/api_keys) to generate API key")
+HF_TOKEN = st.sidebar.text_input("Enter Hugging Face API Key", type="password")
+st.sidebar.write("Check out this [Hugging Face Token](https://huggingface.co/settings/tokens) to generate token")
+city = st.text_input("Enter the name of a city:")
+
+# Initialize the HuggingFace inference endpoint
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    huggingfacehub_api_token=HF_TOKEN.strip(),
+    temperature=0.7,
+    max_new_tokens=100
+)
+
+# Define nodes
+def fetch_weather_node(state: Sequence[BaseMessage]) -> str:
+    city = state[0].content.strip()
+    url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&appid={OPENWEATHER_API_KEY}&units=metric"
+    
+    try:
+        response = httpx.get(url)
+        response.raise_for_status()
+        weather_data = response.json()
+        weather = weather_data['weather'][0]['main']
+        temperature = weather_data['main']['temp']
+        return f"The current weather in {city} is {weather} with a temperature of {temperature}°C."
+    except Exception as e:
+        return f"Error: {e}"
+
+def generate_review_node(state: Sequence[BaseMessage]) -> str:
+    input_text = state[0].content
+    response = llm(input_text)
+    return response
+
+# Define the prompt template for generating weather reviews
+review_prompt_template = """
+You are an expert weather analyst. Based on the provided weather information, generate a detailed and insightful review. 
+Weather Information: {weather_info[1]}
+Your review should include an analysis of the weather conditions. 
+Review:
+"""
+
+# Create and configure the graph
+builder = MessageGraph()
+
+# Add nodes
+builder.add_node("fetch_weather", fetch_weather_node)
+builder.add_node("generate_review", generate_review_node)
+builder.set_entry_point("fetch_weather")
+
+# Define transitions
+builder.add_edge("fetch_weather", "generate_review")
+builder.set_finish_point("generate_review")
+
+# Compile the graph
+graph = builder.compile()
+
+# Streamlit app
+if st.button("Get Weather Information and Review"):
+    if city:
+        with st.spinner("Processing..."):
+            try:
+                # Prepare the input for the graph
+                weather_info = graph.invoke(HumanMessage(content=city))
+                st.write(weather_info[1].content)
+                # Generate the review using the refined prompt
+                review_input = review_prompt_template.format(weather_info=weather_info)
+                review = graph.invoke(HumanMessage(content=review_input))
+                
+                st.subheader("AI Generated Weather Review")
+                st.write(review[2].content)
+                st.subheader("Mermaid Graph")
+                st.write("Check out this [mermaid link](https://mermaid.live/) to display a graph with following data")
+                #st.write(graph.get_graph().draw_mermaid())
+                mermaid_code = graph.get_graph().draw_mermaid()
+                st.markdown(f"```mermaid\n{mermaid_code}\n```", unsafe_allow_html=True)
+
+            except Exception as e:
+                st.error(f"Error generating weather review: {e}")
+    else:
+        st.warning("Please enter a city name.")