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import streamlit as st |
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import os, pickle, faiss, numpy as np |
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from groq import Groq |
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from sentence_transformers import SentenceTransformer |
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from langdetect import detect |
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import requests |
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from datetime import datetime |
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from rapidfuzz import process |
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import json |
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PAK_FLAG_URL = "https://flagcdn.com/w320/pk.png" |
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client = Groq(api_key=os.environ.get("GROQ_API_KEY")) |
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@st.cache_resource |
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def load_data(): |
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idx = faiss.read_index("resqbot_index.faiss") |
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with open("resqbot_chunks.pkl","rb") as f: |
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ch = pickle.load(f) |
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return idx, ch |
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@st.cache_resource |
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def load_model(): |
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return SentenceTransformer('all-MiniLM-L6-v2') |
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embed_model = load_model() |
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def detect_language_fallback(text): |
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try: |
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lang = detect(text) |
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if lang not in ["en", "ur"]: |
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if any("\u0600" <= c <= "\u06FF" for c in text): |
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return "ur" |
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else: |
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return "en" |
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return lang |
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except: |
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return "en" |
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st.title("🤖 ResQBot – Disaster QA (Urdu + English)") |
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with st.spinner("🛡️ Loading ResQBot..."): |
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index, chunks = load_data() |
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st.markdown(""" |
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<style> |
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.grid-section { |
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display: grid; |
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grid-template-columns: repeat(auto-fit, minmax(320px, 1fr)); |
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gap: 1rem; |
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} |
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</style> |
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""", unsafe_allow_html=True) |
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st.markdown(f"### 🌌 <img src='{PAK_FLAG_URL}' width='30' style='vertical-align:middle;'> Disaster Alerts", unsafe_allow_html=True) |
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st.markdown('<div class="grid-section">', unsafe_allow_html=True) |
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quakes = [ |
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{"mag": 5.4, "place": "Quetta, Balochistan", "time": "2025-07-27 03:45 AM"}, |
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{"mag": 4.8, "place": "Peshawar, KPK", "time": "2025-07-26 11:30 PM"}, |
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] |
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st.markdown("#### Earthquake Alerts") |
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if quakes: |
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for q in quakes: |
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st.warning(f"Magnitude {q['mag']} quake in {q['place']} at {q['time']}") |
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if len(quakes) > 1: |
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st.error("⚠️ Increased seismic activity detected.") |
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else: |
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st.success("✅ No notable earthquakes in Pakistan.") |
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floods = [ |
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("2025-07-28", 9200, "High"), |
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("2025-07-29", 7800, "Medium"), |
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("2025-07-30", 4000, "Low"), |
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] |
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st.markdown("#### Flood Forecast") |
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if floods: |
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high_risk_days = [f for f in floods if f[2] in ["High", "Medium"]] |
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if high_risk_days: |
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for d, v, risk in floods: |
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st.info(f"{d}: Discharge {v:.1f} m³/s – Risk level: {risk}") |
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else: |
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st.success("✅ No significant flood risk detected in Pakistan.") |
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else: |
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st.error("⚠️ Unable to fetch flood data at the moment.") |
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st.markdown('</div>', unsafe_allow_html=True) |
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st.markdown("---") |
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st.markdown("### 💬 Ask About Disaster Preparedness") |
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st.markdown("You can ask about earthquake, flood, shelter advice or precaution/preparations etc. in English or Urdu.") |
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query = st.text_input("❓ Your question (English یا اردو/or):") |
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if query: |
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with st.spinner("🤖 Thinking..."): |
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emb = embed_model.encode([query]) |
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D,I = index.search(np.array(emb), k=3) |
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context_chunks = [chunks[i] for i in I[0]] |
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context = "\n".join(context_chunks) |
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if len(context) > 4000: |
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context = context[:4000] + "..." |
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lang = detect_language_fallback(query) |
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if lang=="ur": |
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prompt = f"""اس سیاق و سباق کی بنیاد پر اردو میں کم از کم 3-4 لائنوں میں جواب دیں۔:\n\n{context}\n\nسوال: {query}""" |
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else: |
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prompt = f"""Answer in at least 3-4 lines and to the point in English based on this context:\n\n{context}\n\nQuestion: {query}""" |
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resp = client.chat.completions.create( |
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messages=[{"role":"user","content":prompt}], |
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model="llama-3.1-8b-instant" |
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) |
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st.markdown("### 💬 ResQBot Answer:") |
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st.write(resp.choices[0].message.content) |
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