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app.py

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+import pandas as pd
+from geopy.geocoders import Nominatim
+from geopy.distance import geodesic
+import requests
+import gradio as gr
+import os
+
+# 🔐 Your OpenRouter API Key (keep this secret)
+OPENROUTER_API_KEY = os.getenv('OPENROUTER_API_KEY')
+
+# 📂 Load your accident dataset
+df = pd.read_csv("final_merged.csv")  # Replace with your file
+
+# 🌐 Get coordinates for user location
+def geocode_location(location_name):
+    geolocator = Nominatim(user_agent="accident-warning-system")
+    location = geolocator.geocode(location_name)
+    return (location.latitude, location.longitude) if location else None
+
+# 📍 Find nearest accident locations
+def find_nearest_accidents(user_latlon, df, top_n=10):
+    df['DISTANCE'] = df.apply(
+        lambda row: geodesic(user_latlon, (row['LATITUDE'], row['LONGITUDE'])).meters, axis=1)
+    return df.sort_values('DISTANCE').head(top_n)
+
+# 🧪 Filter by atmospheric and road condition
+def filter_by_conditions(df, atmosphere, road_condition):
+    return df[
+        df['ATMOSPH_COND_DESC'].str.contains(atmosphere, case=False, na=False) &
+        df['SURFACE_COND_DESC'].str.contains(road_condition, case=False, na=False)
+    ]
+
+# 📊 Summarize accident data
+def summarize_data(df):
+    return {
+        "total_accidents": len(df),
+        "severe_accidents": (df["SEVERITY"] == "Serious").sum(),
+        "fatalities": int(df["NO_PERSONS_KILLED"].sum()),
+        "common_types": df["ACCIDENT_TYPE_DESC"].value_counts().head(3).to_dict(),
+        "age_groups": df["AGE_GROUP"].value_counts().head(2).to_dict(),
+        "user_types": df["ROAD_USER_TYPE"].value_counts().head(2).to_dict()
+    }
+
+# 🧾 Format readable summary
+def format_summary(summary, location, atmosphere, road_condition):
+    return (
+        f"📍 Location: {location}\n"
+        f"🌦️ Atmosphere: {atmosphere}\n"
+        f"🛣️ Road Condition: {road_condition}\n\n"
+        f"🚧 Accidents Nearby: {summary['total_accidents']}\n"
+        f"❗ Severe Accidents: {summary['severe_accidents']}\n"
+        f"☠️ Fatalities: {summary['fatalities']}\n"
+        f"🔁 Common Accident Types: {', '.join(summary['common_types'].keys())}\n"
+        f"👥 Age Groups Involved: {', '.join(summary['age_groups'].keys())}\n"
+        f"🚶‍♂️ User Types Affected: {', '.join(summary['user_types'].keys())}\n"
+    )
+
+# 💬 Generate LLM warning
+def get_llm_warning(summary, location, atmosphere, road_condition):
+    prompt = f"""
+A driver is traveling to "{location}" during "{atmosphere}" weather and "{road_condition}" road conditions.
+
+Nearby accident statistics:
+- Total accidents: {summary['total_accidents']}
+- Severe: {summary['severe_accidents']}
+- Fatalities: {summary['fatalities']}
+- Common types: {summary['common_types']}
+- Age groups involved: {summary['age_groups']}
+- Road users involved: {summary['user_types']}
+
+Give a short safety warning with practical advice based on this data.
+"""
+    headers = {
+        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": "openai/gpt-3.5-turbo",
+        "messages": [{"role": "user", "content": prompt}]
+    }
+    try:
+        response = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload)
+        if response.status_code == 200:
+            return response.json()['choices'][0]['message']['content']
+        else:
+            return f"⚠️ LLM Error: {response.status_code} - {response.text}"
+    except Exception as e:
+        return f"⚠️ Request failed: {e}"
+
+# 🤖 Gradio function
+def advisory_bot(location, atmosphere, road_condition):
+    coords = geocode_location(location)
+    if not coords:
+        return "❌ Could not find the specified location.", ""
+
+    nearest_df = find_nearest_accidents(coords, df)
+    filtered_df = filter_by_conditions(nearest_df, atmosphere, road_condition)
+
+    if filtered_df.empty:
+        return f"⚠️ No recent accident data found near '{location}' for these conditions.", ""
+
+    summary = summarize_data(filtered_df)
+    summary_text = format_summary(summary, location, atmosphere, road_condition)
+    llm_warning = get_llm_warning(summary, location, atmosphere, road_condition)
+
+    return summary_text, llm_warning
+
+# 🎛️ Gradio UI
+gr.Interface(
+    fn=advisory_bot,
+    inputs=[
+        gr.Textbox(label="📍 Location (e.g., Plenty Road, Melbourne)"),
+        gr.Textbox(label="🌦️ Atmosphere (e.g., Rain, Fog, Clear)"),
+        gr.Textbox(label="🛣️ Road Condition (e.g., Wet, Dry)")
+    ],
+    outputs=[
+        gr.Textbox(label="📊 Accident Summary"),
+        gr.Textbox(label="🛑 Safety Warning")
+    ],
+    title="🚧 Accident Risk Advisory",
+    description="Input a location, weather, and road condition to get a data-based accident summary and safety warning."
+).launch()

requirements.txt

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+pandas
+geopy
+gradio
+requests