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app (1).py

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+import os
+import httpx
+import streamlit as st
+from dotenv import load_dotenv
+from langchain.prompts import PromptTemplate
+from langchain.agents import create_react_agent, AgentExecutor
+from langchain.tools import Tool
+from langchain_huggingface import HuggingFaceEndpoint
+import urllib.parse
+
+# Get API keys from the user
+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:")
+
+
+# Define a tool to fetch weather information and generate a review
+def fetch_and_review_weather(city: str) -> str:
+    """Fetch the weather information for a given city and generate a review."""
+    city = city.strip()  # Ensure no leading/trailing spaces
+    
+    # Encode the city name for the URL
+    encoded_city = urllib.parse.quote(city)
+    url = f"https://api.openweathermap.org/data/2.5/weather?q={encoded_city}&appid={OPENWEATHER_API_KEY}&units=metric"
+    
+    try:
+        response = httpx.get(url)
+        response.raise_for_status()
+        weather_data = response.json()
+        
+        # Extract weather details
+        weather = weather_data['weather'][0]['main']
+        temperature = weather_data['main']['temp']
+        
+        # Generate the review
+        input_text = f"The current weather in {city} is {weather} with a temperature of {temperature}°C. As an expert in weather forecast analysis, please provide an appropriate weather review."
+        input_text = ''.join(c for c in input_text if c.isprintable())
+        
+        # Generate the review using the language model
+        review = llm(input_text)
+        return review
+    except Exception as e:
+        return f"Error: {e}"
+
+# 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=200
+)
+
+# Define the tool for the agent
+tools_for_agent = [
+    Tool(
+        name="fetch_and_review_weather",
+        func=fetch_and_review_weather,
+        description="Fetch the weather information for a given city and generate a review"
+    )
+]
+
+# Define a prompt template for the agent
+template = """
+Answer the following questions as best you can. You have access to the following tools:
+
+{tools}
+
+Use the following format:
+
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: the action to take, should be one of [{tool_names}]
+Action Input: the input to the action
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can repeat N times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin!
+
+Question: {input}
+Thought:
+{agent_scratchpad}
+"""
+
+prompt = PromptTemplate.from_template(template=template).partial(
+    tools="\n".join([f"{t.name}: {t.description}" for t in tools_for_agent]),
+    tool_names=", ".join([t.name for t in tools_for_agent]),
+)
+
+# Initialize the agent
+react_prompt = prompt
+agent = create_react_agent(
+    llm=llm,
+    tools=tools_for_agent,
+    prompt=react_prompt
+)
+agent_executor = AgentExecutor(agent=agent, tools=tools_for_agent, verbose=True)
+
+# Streamlit UI for fetching and reviewing weather
+
+if st.button("Get Weather Information and Review"):
+    with st.spinner("Processing..."):
+        try:
+            # Directly call the fetch_and_review_weather function through the agent
+            review = agent_executor.invoke({
+                'input': f"Generate weather review for {city}",
+                'agent_scratchpad': ''
+            }, handle_parsing_errors=True)
+            
+            st.subheader("AI Generated Weather Review")
+            st.write(review['output'])
+        except Exception as e:
+            st.error(f"Error generating weather review: {e}")

requiements.txt

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+langchain
+httpx
+streamlit
+python-dotenv
+langchain-huggingface