Update app.py

app.py

@@ -618,9 +618,17 @@ def weather_agent_tool(query: str) -> str:
         hour_str = closest_hour["time"].split(" ")[1]
 
         summary_prompt = f"""
-You are a weather analyst. Below is detailed weather data for a specific time and location.
+You are a helpful weather reasoning assistant.
 
-Your task: Based on the user's question, **reason step-by-step** which information is most relevant, then provide a concise, human-readable answer in British English.
+Based on the following weather data and the user's question, think step-by-step to extract the most relevant information, and give a natural, friendly, and cautious answer in British English.
+
+Avoid being overly confident — never say "Yes, it will..." or "Definitely." Instead, use expressions like:
+- "It is very likely that..."
+- "There is a high chance of..."
+- "Based on the current data, it seems that..."
+- "There may be..."
+
+Also, after answering the question, include a short weather summary and a useful suggestion (e.g., bring an umbrella, wear sunscreen, avoid outdoor activities).
 
 --- Weather Data ---
 Location: {location}
@@ -629,15 +637,16 @@ Condition: {condition}
 Temperature: {temp}°C (Feels like {feels}°C)
 Humidity: {humidity}%
 Chance of rain: {chance_rain}%
-Chance of snow: {closest_hour.get("chance_of_snow", 0)}%
-Wind speed: {closest_hour.get("wind_kph", 'N/A')} kph
-UV index: {closest_hour.get("uv", 'N/A')}
-Cloud cover: {closest_hour.get("cloud", 'N/A')}%
+Chance of snow: {closest_hour.get("chance_of_snow", "N/A")}%
+Wind speed: {closest_hour.get("wind_kph", "N/A")} kph
+UV index: {closest_hour.get("uv", "N/A")}
+Cloud cover: {closest_hour.get("cloud", "N/A")}%
+Visibility: {closest_hour.get("vis_km", "N/A")} km
 
---- User Query ---
-"{query}"
+--- User Question ---
+{query}
 
-Step-by-step reasoning and final answer:
+--- Final Answer ---
 """
         response = llm_gpt4.invoke(summary_prompt)
         return response.content.strip() if isinstance(response, AIMessage) else str(response)
@@ -726,9 +735,17 @@ def weather_tool(query: str) -> str:
         hour_str = closest_hour["time"].split(" ")[1]
 
         summary_prompt = f"""
-You are a weather analyst. Below is detailed weather data for a specific time and location.
+You are a helpful weather reasoning assistant.
+
+Based on the following weather data and the user's question, think step-by-step to extract the most relevant information, and give a natural, friendly, and cautious answer in British English.
+
+Avoid being overly confident — never say "Yes, it will..." or "Definitely." Instead, use expressions like:
+- "It is very likely that..."
+- "There is a high chance of..."
+- "Based on the current data, it seems that..."
+- "There may be..."
 
-Your task: Based on the user's question, **reason step-by-step** which information is most relevant, then provide a concise, human-readable answer in British English.
+Also, after answering the question, include a short weather summary and a useful suggestion (e.g., bring an umbrella, wear sunscreen, avoid outdoor activities).
 
 --- Weather Data ---
 Location: {location}
@@ -737,15 +754,16 @@ Condition: {condition}
 Temperature: {temp}°C (Feels like {feels}°C)
 Humidity: {humidity}%
 Chance of rain: {chance_rain}%
-Chance of snow: {closest_hour.get("chance_of_snow", 0)}%
-Wind speed: {closest_hour.get("wind_kph", 'N/A')} kph
-UV index: {closest_hour.get("uv", 'N/A')}
-Cloud cover: {closest_hour.get("cloud", 'N/A')}%
+Chance of snow: {closest_hour.get("chance_of_snow", "N/A")}%
+Wind speed: {closest_hour.get("wind_kph", "N/A")} kph
+UV index: {closest_hour.get("uv", "N/A")}
+Cloud cover: {closest_hour.get("cloud", "N/A")}%
+Visibility: {closest_hour.get("vis_km", "N/A")} km
 
---- User Query ---
-"{query}"
+--- User Question ---
+{query}
 
-Step-by-step reasoning and final answer:
+--- Final Answer ---
 """
         response = llm_gpt4.invoke(summary_prompt)
         return response.content.strip() if isinstance(response, AIMessage) else str(response)