Create app

app

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+# -*- coding: utf-8 -*-
+"""
+tree_age_iks_300_tamil.py
+AI மர வகை மற்றும் வயது கணிப்பு (300 இனியோட்டப் பெயர்கள்) + IKS (தமிழ்) இணைப்பு
+Author: Generated for user
+"""
+
+import streamlit as st
+import os
+import sys
+import json
+import math
+import numpy as np
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+
+# ----------------------------
+# 1) 300 species list (primary list)
+# ----------------------------
+species_names = [
+    "mango","neem","banyan","peepal","teak","sal","sandalwood","rosewood","mahogany","acacia",
+    "babool","ashoka","gulmohar","rain tree","eucalyptus","jamun","guava","jackfruit","tamarind","coconut",
+    "areca palm","rubber","casuarina","bamboo","fig","amla","drumstick","kadamba","pongamia","arjuna",
+    "bael","custard apple","flame tree","indian almond","bottlebrush","silk cotton","indian coral","kadam","siris","subabul",
+    "albizia","karanja","tulip tree","silver oak","pine","deodar","oak","maple","ash","cedar",
+    "cypress","willow","poplar","birch","cashew","papaya","banana","mulberry","tendu","indian cherry",
+    "sapota","mangosteen","clove","nutmeg","coffee","tea","black pepper","cinnamon","neer maruthu","palmyra",
+    "date palm","cork tree","white teak","pungam","champa","plumeria","mahua","red cedar","apple","pear",
+    "peach","cherry","almond","walnut","plum","apricot","persimmon","betel nut","wild jack","neer fig",
+    "siris tree","arjun variant","custard variant","amla variant","rosewood variant","rain tree variant",
+    "ashoka variant","banyan variant","guava variant","neem variant",
+    "hibiscus","bougainvillea","jasmine","marigold","tulsi","mint","basil","lemongrass","oregano","thyme",
+    "rosemary","sage","aloevera","ginger","turmeric","galangal","cardamom","fennel","coriander","cumin",
+    "fenugreek","castor","sunflower","sesame","mustard","linseed","hemp","cotton","okra","brinjal",
+    "tomato","chili","potato","onion","garlic","spinach","amaranthus","cauliflower","cabbage","broccoli",
+    "pumpkin","ridge gourd","bottle gourd","bitter gourd","snake gourd","cucumber","watermelon","muskmelon","melon","lettuce",
+    "beetroot","carrot","radish","turnip","yam","sweet potato","colocasia","cassava","arrowroot","peppermint",
+    "sagebrush","lavender","thyme variant","oregano variant","mint variant","bamboo palm","traveller’s palm","fan palm","sago palm","royal palm",
+    "silver date palm","foxtail palm","betel leaf","vanilla","kokum","kokum tree","soursop","star fruit","breadfruit","durian",
+    "custard pear","sugar apple","dragon fruit","kiwi","pomegranate","blueberry","strawberry","blackberry","raspberry","cranberry",
+    "gooseberry","tamarillo","lychee","longan","rambutan","fig variant","mulberry variant","lemon","lime","orange",
+    "grapefruit","pomelo","citron","mandarin","tangerine","lemondrop mangosteen","miracle fruit","noni","baobab","acacia nilotica",
+    "prosopis juliflora","dalbergia latifolia","terminalia bellirica","terminalia chebula","emblica officinalis","cassia fistula","delonix regia","bauhinia purpurea","bauhinia variegata","peltophorum pterocarpum",
+    "lagerstroemia speciosa","millingtonia hortensis","polyalthia longifolia","ficus benghalensis","ficus religiosa","ficus racemosa","ficus elastica","artocarpus heterophyllus","artocarpus altilis","azadirachta indica",
+    "swietenia mahagoni","mimusops elengi","syzygium cumini","eucalyptus globulus","grevillea robusta","santalum album","pterospermum acerifolium","butea monosperma","madhuca longifolia","pithecellobium dulce",
+    "callistemon citrinus","cassia siamea","cassia javanica","albizia lebbeck","albizia saman","alstonia scholaris","antidesma acidum","barringtonia acutangula","careya arborea","cochlospermum religiosum",
+    "cordia dichotoma","croton bonplandianum","crataeva magna","dillenia indica","diospyros melanoxylon","erythrina variegata","feronia limonia","grewia asiatica","holarrhena pubescens","manilkara zapota",
+    "morinda tinctoria","polyalthia longifolia pendula","putranjiva roxburghii","sapindus trifoliatus","semecarpus anacardium","sterculia urens","syzygium aromaticum","tamarindus indica","terminalia arjuna","trichilia emetica",
+    "wrightia tinctoria","ziziphus mauritiana","ziziphus jujuba","adansonia digitata","hevea brasiliensis","catharanthus roseus","melia dubia","moringa oleifera","melia azedarach","saraca asoca",
+    "michelia champaca","magnolia grandiflora","tithonia diversifolia","melastoma malabathricum","thespesia populnea","vetiver","lemongrass variant","camphor tree","guaiacum officinale","annona reticulata",
+    "artemisia annua","tulsi krishna","neem hybrid","mahogany hybrid","sandalwood hybrid","rosewood hybrid","amla hybrid","eucalyptus hybrid","gulmohar hybrid","casuarina hybrid"
+]
+
+# ensure exactly 300 (if earlier list shorter, extend with generated names)
+if len(species_names) < 300:
+    idx = len(species_names) + 1
+    while len(species_names) < 300:
+        species_names.append(f"species_{idx}")
+        idx += 1
+elif len(species_names) > 300:
+    species_names = species_names[:300]
+
+# ----------------------------
+# 2) Create minimal synthetic attributes for classifier
+#    (kept simple: leaf_shape, bark_texture, habitat, fruit_presence, avg height, leaf size, growth_factor)
+# ----------------------------
+np.random.seed(42)
+species_data = {
+    "species": species_names,
+    "leaf_shape": np.random.choice(["broad","oval","needle","compound","heart","lanceolate"], len(species_names)),
+    "bark_texture": np.random.choice(["smooth","rough","flaky","fibrous","grooved"], len(species_names)),
+    "habitat": np.random.choice(["tropical","dry","coastal","hill","plain","rainforest"], len(species_names)),
+    "fruit_presence": np.random.choice(["yes","no"], len(species_names)),
+    "average_height_m": np.round(np.random.uniform(2, 60, len(species_names)), 2),
+    "leaf_size_cm": np.round(np.random.uniform(2, 45, len(species_names)), 2),
+    # growth_factor used to compute age = (circumference/pi) * growth_factor
+    "growth_factor": np.round(np.random.uniform(1.4, 5.0, len(species_names)), 2)
+}
+df_species = pd.DataFrame(species_data)
+
+# ----------------------------
+# 3) Train a Decision Tree on this lightweight synthetic table
+# ----------------------------
+X = pd.get_dummies(df_species[["leaf_shape","bark_texture","habitat","fruit_presence"]])
+X["average_height_m"] = df_species["average_height_m"]
+X["leaf_size_cm"] = df_species["leaf_size_cm"]
+y = df_species["species"]
+
+clf = DecisionTreeClassifier(random_state=42, max_depth=14)
+clf.fit(X, y)
+
+# ----------------------------
+# 4) IKS Tamil knowledge base (prepopulated entries for many common species)
+#    File path for persistence
+# ----------------------------
+IKS_DB_PATH = "iks_tamil_300_db.json"
+
+# Prepopulate Tamil IKS entries for commonly known species.
+# For many of the 300 species we provide a default placeholder.
+prepopulated = {
+    # Common examples (Tamil name, uses, notes) - you can expand/edit later
+    "mango": {
+        "tamil_name": "மாமரம்",
+        "english_name": "Mango",
+        "uses_tamil": "மாமரப் பழம் உணவாக பயன்படும்; இலைகள், கூழ்கள் பல மருந்து பயன்பாடுகள்.",
+        "notes_tamil": "பாரம்பரியமாக இந்தியாவில் மதிப்பிடப்பட்ட மரம்; பல்வேறு பழவகைகள் உண்டு."
+    },
+    "neem": {
+        "tamil_name": "வேம்பு",
+        "english_name": "Neem",
+        "uses_tamil": "வழக்கமாக கிருமிநாசினியாகவும், தோல் மருத்துவமாகவும் பயன்படும்; பல் பராமரிப்பிலும் பயன்பாடு.",
+        "notes_tamil": "பாரம்பரிய மருத்துவத்தில் முக்கியத்துவம் அதிகம்."
+    },
+    "banyan": {
+        "tamil_name": "ஆலமரம்",
+        "english_name": "Banyan",
+        "uses_tamil": "பரப்பாக்க மகத்தான நிழல்; வழிபாட்டு மற்றும் சமூக சந்த்பிரதிபலனில் முக்கியம்.",
+        "notes_tamil": "பழைய மரங்களின் வாழ்நாள் நீண்டது."
+    },
+    "peepal": {
+        "tamil_name": "பீப்பல்",
+        "english_name": "Peepal",
+        "uses_tamil": "மத வழிபாடு மற்றும் மரபுத்தனத்தின் அடையாளம்; சிலர் மருத்துவபயன்பாட்டு குறிப்புகளை மேற்கோள் செய்கிறார்கள்.",
+        "notes_tamil": "வளர்ச்சி மேலாண்மைக்கு பரவலாக பயனுள்ளது."
+    },
+    "teak": {
+        "tamil_name": "தேக்கு",
+        "english_name": "Teak",
+        "uses_tamil": "முதன்மையாக கட்டிடக்கலை மற்றும் கப்பல் பணிக்குப் பயன்படும் வலுவான மரம்.",
+        "notes_tamil": "ஊரக மற்றும் வணிக மர வனம்."
+    },
+    "coconut": {
+        "tamil_name": "தென்னை",
+        "english_name": "Coconut",
+        "uses_tamil": "பழம், எண்ணெய் மற்றும் பல பாவனைகளில் பயன்படும்; கடற்கரையில் பயன்பாடு அதிகம்.",
+        "notes_tamil": "வாழ்க்கை சார்ந்த மரம்; பலமருந்து பயன்பாடுகள்."
+    },
+    "amla": {
+        "tamil_name": "ஆமலா",
+        "english_name": "Amla",
+        "uses_tamil": "ஆயுர்வேதத்தில் முக்கியம்; C வைட்டமின் ஆதாரம்.",
+        "notes_tamil": "மருந்து மற்றும் உணவு பயன்பாடுகள்."
+    },
+    "drumstick": {
+        "tamil_name": "முருங்கை",
+        "english_name": "Drumstick (Moringa)",
+        "uses_tamil": "முருங்கை இலைகள், காய் மருத்துவ மற்றும் ஊட்டச்சத்து மூலமாக பயன்படும்.",
+        "notes_tamil": "குறைந்த நிலங்களில் வளர்ச்சி சிறந்தது."
+    },
+    # Add a few more explicit entries
+    "sandalwood": {
+        "tamil_name": "சந்தனம்",
+        "english_name": "Sandalwood",
+        "uses_tamil": "அருகம்பா வாசனை, மருந்து மற்றும் ஆன்மீகப் பயன்பாடுகள்.",
+        "notes_tamil": "மதிப்புமிக்க வெள்ளிமரப்பலு."
+    },
+    "jackfruit": {
+        "tamil_name": "பலாப்பழம்",
+        "english_name": "Jackfruit",
+        "uses_tamil": "காயும் பழமும் இரண்டுமே உணவாகவும், வணிகவாய்ப்புகளாகவும் இருக்கும்.",
+        "notes_tamil": "உணவு மற்றும் விவசாய பயன்பாடுகள் அதிகம்."
+    },
+    "guava": {
+        "tamil_name": "பேழை",
+        "english_name": "Guava",
+        "uses_tamil": "பழம், மருத்துவ பயன்பாடுகள் (விநோத தொற்று எதிர்ப்பு).",
+        "notes_tamil": "குடியரசு தோல் மற்றும் நன்மைகள்."
+    },
+    # default fallback entry
+    "default": {
+        "tamil_name": "",
+        "english_name": "",
+        "uses_tamil": "இந்த மரத்திற்கான பாரம்பரிய தகவல் தரவுத்தளத்தில் இல்லை. புதிய தகவலைச் சேர்க்கலாம்.",
+        "notes_tamil": "பயனாளர் IKS பதிவுகளை சேமிக்கலாம்."
+    }
+}
+
+# Load or create persistent IKS DB
+if os.path.exists(IKS_DB_PATH):
+    try:
+        with open(IKS_DB_PATH, "r", encoding="utf-8") as f:
+            iks_db = json.load(f)
+    except Exception:
+        iks_db = prepopulated.copy()
+else:
+    iks_db = prepopulated.copy()
+    try:
+        with open(IKS_DB_PATH, "w", encoding="utf-8") as f:
+            json.dump(iks_db, f, ensure_ascii=False, indent=2)
+    except Exception:
+        pass  # continue even if file write not permitted
+
+# ----------------------------
+# Helper functions
+# ----------------------------
+def save_iks_entry(species_key, tamil_name, english_name, uses_tamil, notes_tamil):
+    key = species_key.lower()
+    iks_db[key] = {
+        "tamil_name": tamil_name,
+        "english_name": english_name,
+        "uses_tamil": uses_tamil,
+        "notes_tamil": notes_tamil
+    }
+    try:
+        with open(IKS_DB_PATH, "w", encoding="utf-8") as f:
+            json.dump(iks_db, f, ensure_ascii=False, indent=2)
+        return True
+    except Exception:
+        return False
+
+def get_iks_for_species(species_key):
+    key = species_key.lower()
+    if key in iks_db:
+        return iks_db[key]
+    # try normalized matching
+    key2 = key.replace(" ", "").replace("’", "").replace("'", "").lower()
+    for k in iks_db:
+        nk = k.replace(" ", "").replace("’", "").replace("'", "").lower()
+        if nk == key2:
+            return iks_db[k]
+    return iks_db.get("default")
+
+def pretty_tamil_output(species, iks_entry, circumference_cm, diameter_cm, age_years):
+    tamil_lines = []
+    tamil_name = iks_entry.get("tamil_name") or species.capitalize()
+    eng_name = iks_entry.get("english_name") or ""
+    tamil_lines.append(f"🌳 மரம்: {tamil_name}  ({eng_name} — {species})")
+    tamil_lines.append(f"📏 சுற்றளவு: {circumference_cm} cm")
+    tamil_lines.append(f"📐 விட்டம் (அளவு): {diameter_cm:.2f} cm")
+    tamil_lines.append(f"🕰️ கணிக்கப்பட்ட வயது: {age_years:.1f} ஆண்டு(கள்)")
+    tamil_lines.append("")
+    tamil_lines.append("🌿 பாரம்பரிய பயன்பாடுகள்:")
+    tamil_lines.append(iks_entry.get("uses_tamil", "தகவல் இல்லை"))
+    tamil_lines.append("")
+    tamil_lines.append("📝 குறிப்புகள்:")
+    tamil_lines.append(iks_entry.get("notes_tamil", "தகவல் இல்லை"))
+    return "\n".join(tamil_lines)
+
+# ----------------------------
+# 5) Main interactive loop (Tamil I/O friendly)
+# ----------------------------
+def main():
+    print("\n🌳 AI மர வகை மற்றும் வயது கணிப்பு (300 மரங்கள்) - IKS (தமிழ்) இணைப்பு\n")
+    print("கால்வெளி: இங்கே நீங்கள் கீழ்கண்ட விவரங்களை தமிழில் உள்ளீடு செய்யலாம்.")
+    print("இலை வடிவம்: broad/oval/needle/compound/heart/lanceolate")
+    print("தோல் அமைப்பு: smooth/rough/flaky/fibrous/grooved")
+    print("வாழ்விடம்: tropical/dry/coastal/hill/plain/rainforest")
+    print("பழம்: yes/no")
+    print("உதாரணமாக: ஒவல் -> 'oval', கடுமையான தோல் -> 'rough'\n")
+
+    # collect inputs (allow Tamil words mapped to English tokens)
+    def map_tamil_to_token(value):
+        v = value.strip().lower()
+        mapping = {
+            "பரந்த": "broad", "பரந்தது": "broad", "ஒவல்": "oval", "முள்": "needle",
+            "முழுகு": "needle", "சேர்க்கை": "compound", "இதயம்": "heart", "ஊசி": "lanceolate",
+            "அழுக்கு": "rough", "மென்மை": "smooth", "காசு": "flaky", "நார்": "fibrous", "துளை": "grooved",
+            "வெப்பமண்டல": "tropical", "வெப்ப": "tropical", "உலர்": "dry", "கடற்கரை": "coastal",
+            "மலை": "hill", "புலம்": "plain", "மழைக் காட்டில்": "rainforest", "மழைக்காடு": "rainforest",
+            "ஆம்": "yes", "இல்லை": "no", "உள்ளது": "yes", "இல்லாது": "no"
+        }
+        return mapping.get(v, v)
+
+    leaf_shape_in = input("இலை வடிவம் (தமிழில்/ஆங்கிலத்தில்): ")
+    leaf_shape = map_tamil_to_token(leaf_shape_in)
+
+    bark_texture_in = input("தோல் அமைப்பு (தமிழில்/ஆங்கிலத்தில்): ")
+    bark_texture = map_tamil_to_token(bark_texture_in)
+
+    habitat_in = input("வாழ்விடம் (தமிழில்/ஆங்கிலத்தில்): ")
+    habitat = map_tamil_to_token(habitat_in)
+
+    fruit_in = input("பழம் உள்ளதா? (ஆம்/இல்லை அல்லது yes/no): ")
+    fruit_presence = map_tamil_to_token(fruit_in)
+
+    try:
+        avg_height = float(input("சுமார் உயரம் (மீட்டர்): ").strip())
+    except Exception:
+        avg_height = float(np.mean(df_species["average_height_m"]))
+
+    try:
+        leaf_size = float(input("இலை அளவு (செ.மீ): ").strip())
+    except Exception:
+        leaf_size = float(np.mean(df_species["leaf_size_cm"]))
+
+    try:
+        circumference = float(input("மர சுற்றளவு (செ.மீ): ").strip())
+    except Exception:
+        print("தவறு: சுற்றளவை (cm) சரியாக உள்ளிடவும்.")
+        return
+
+    # prepare input for classifier
+    input_df = pd.DataFrame([{
+        "leaf_shape": leaf_shape,
+        "bark_texture": bark_texture,
+        "habitat": habitat,
+        "fruit_presence": fruit_presence
+    }])
+    input_encoded = pd.get_dummies(input_df)
+    input_encoded = input_encoded.reindex(columns=X.columns, fill_value=0)
+    input_encoded["average_height_m"] = avg_height
+    input_encoded["leaf_size_cm"] = leaf_size
+
+    # predict species
+    try:
+        predicted_species = clf.predict(input_encoded)[0]
+    except Exception as e:
+        print("வகைப்படுத்தலில் பிழை:", e)
+        predicted_species = species_names[0]
+
+    # lookup growth factor
+    gf_row = df_species.loc[df_species["species"] == predicted_species, "growth_factor"]
+    growth_factor = float(gf_row.values[0]) if len(gf_row) > 0 else float(np.mean(df_species["growth_factor"]))
+
+    # compute diameter and age
+    diameter_cm = circumference / math.pi
+    age_years = diameter_cm * growth_factor
+
+    # fetch IKS entry
+    iks_entry = get_iks_for_species(predicted_species)
+
+    # display results in Tamil
+    print("\n" + "-"*60 + "\n")
+    print(pretty_tamil_output(predicted_species, iks_entry, circumference, diameter_cm, age_years))
+    print("\n" + "-"*60 + "\n")
+
+    # Ask user if they want to add/edit IKS info for predicted species
+    add = input("இந்த மரத்திற்கான IKS தமிழ் தகவலை சேரிக்கவா/தொகு (y/n)? ").strip().lower()
+    if add == "y" or add == "ஆம்":
+        tamil_name = input("தமிழ் பெயர் (உதா: மாமரம்): ").strip()
+        eng_name = input("ஆங்கிலப் பெயர் (optional): ").strip()
+        uses = input("பாரம்பரிய / மருத்துவ பயன்பாடுகள் (தமிழில்): ").strip()
+        notes = input("குறிப்புகள் (தமிழில்): ").strip()
+        ok = save_iks_entry(predicted_species, tamil_name, eng_name, uses, notes)
+        if ok:
+            print("✅ IKS தகவல் வெற்றிகரமாக சேமிக்கப்பட்டது:", IKS_DB_PATH)
+        else:
+            print("⚠️ IKS தகவலை சேமிக்க முடியவில்லை (அனுமதி சோதிக்கவும்).")
+
+    # Continue loop?
+    again = input("\nமீண்டும் ஒரு மரத்தை மதிப்பிட வேண்டுமா? (y/n): ").strip().lower()
+    if again in ("y","yes","ஆம்"):
+        main()
+    else:
+        print("\nநன்றி! வாழ்த்துகள் 🌿")
+        return
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\nநீங்கள் செயலியை நிறுத்தினீர்கள். வணக்கம்!")
+        sys.exit(0)