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.gitignore

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+venv/
+.env 

Predictions.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import pandas as pd\n",
+    "\n",
+    "model_path = r\"C:\\Users\\saipr\\Crop_Recommendation\\saved_models\\RF_Model.pkl\"\n",
+    "\n",
+    "with open(model_path, 'rb') as f:\n",
+    "    rf_model = pickle.load(f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted Crops: ['papaya' 'rice' 'rice']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example input data \n",
+    "data = [\n",
+    "    {\"N\": 56, \"P\": 48, \"K\": 28, \"temperature\": 28.5, \"humidity\": 89.0, \"ph\": 6.9, \"rainfall\": 220.0},\n",
+    "    {\"N\": 64, \"P\": 55, \"K\": 33, \"temperature\": 22.0, \"humidity\": 78.0, \"ph\": 7.3, \"rainfall\": 200.0},\n",
+    "    {\"N\": 98, \"P\": 47, \"K\": 49, \"temperature\": 22.8, \"humidity\": 89.0, \"ph\": 6.1, \"rainfall\": 202.9},\n",
+    "]\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "rf_predictions = rf_model.predict(df)\n",
+    "\n",
+    "print(\"Predicted Crops:\", rf_predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## with SVC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import pandas as pd\n",
+    "\n",
+    "model_path = r\"C:\\Users\\saipr\\Crop_Recommendation\\saved_models\\svc_model.pkl\"\n",
+    "\n",
+    "with open(model_path, 'rb') as f:\n",
+    "    svc_model = pickle.load(f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted Crops: ['coffee' 'rice' 'rice']\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = [\n",
+    "    {\"N\": 98, \"P\": 48, \"K\": 35, \"temperature\": 28.5, \"humidity\": 65.0, \"ph\": 6.9, \"rainfall\": 220.0},\n",
+    "    {\"N\": 64, \"P\": 55, \"K\": 33, \"temperature\": 22.0, \"humidity\": 78.0, \"ph\": 7.3, \"rainfall\": 200.0},\n",
+    "    {\"N\": 98, \"P\": 47, \"K\": 49, \"temperature\": 22.8, \"humidity\": 89.0, \"ph\": 6.1, \"rainfall\": 202.9},\n",
+    "]\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "svc_predictions = svc_model.predict(df)\n",
+    "\n",
+    "print(\"Predicted Crops:\", svc_predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gradient Boosting Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import pandas as pd\n",
+    "\n",
+    "model_path = r\"C:\\Users\\saipr\\Crop_Recommendation\\saved_models\\gb_model.pkl\"\n",
+    "\n",
+    "with open(model_path, 'rb') as f:\n",
+    "    gb_model = pickle.load(f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted Crops: ['maize' 'rice' 'rice']\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = [\n",
+    "    {\"N\": 98, \"P\": 48, \"K\": 35, \"temperature\": 28.5, \"humidity\": 65.0, \"ph\": 6.9, \"rainfall\": 100.0},\n",
+    "    {\"N\": 64, \"P\": 55, \"K\": 33, \"temperature\": 22.0, \"humidity\": 78.0, \"ph\": 7.3, \"rainfall\": 200.0},\n",
+    "    {\"N\": 98, \"P\": 47, \"K\": 49, \"temperature\": 22.8, \"humidity\": 89.0, \"ph\": 6.1, \"rainfall\": 202.9},\n",
+    "]\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "gb_predictions = gb_model.predict(df)\n",
+    "\n",
+    "print(\"Predicted Crops:\", gb_predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

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+import streamlit as st
+import numpy as np
+import pickle
+import os
+from langchain.schema import HumanMessage, SystemMessage, AIMessage
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_groq import ChatGroq
+from dotenv import load_dotenv
+
+# Set Streamlit Page Config
+st.set_page_config(
+    page_title="Agricultural AI Assistant 🌱",
+    layout="wide"
+)
+
+load_dotenv()
+os.environ['GROQ_API_KEY'] = os.getenv("GROQ_API_KEY")
+groq_api_key = os.getenv("GROQ_API_KEY")
+chat = ChatGroq(groq_api_key=groq_api_key, model_name="llama-3.3-70b-versatile")
+
+model_path = r"C:\Users\saipr\Crop_Recommendation\saved_models\RF_Model.pkl"
+model = pickle.load(open(model_path, 'rb'))
+
+st.markdown("""
+    <style>
+        .title { text-align: center; color: mediumseagreen; }
+        .warning { color: red; font-weight: bold; text-align: center; }
+        .container {
+            background: #edf2f7; font-weight: bold;
+            padding: 20px; border-radius: 15px; margin-top: 20px;
+        }
+        .stButton>button {
+            background-color: #007bff; color: white;
+            font-size: 16px; font-weight: bold; border: none;
+            border-radius: 5px; padding: 10px 20px;
+        }
+        .stTextInput>div>input {
+            border-radius: 5px; border: 1px solid #007bff; padding: 10px;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+if 'flow_messages' not in st.session_state:
+    st.session_state['flow_messages'] = [
+        SystemMessage(content="You are a highly intelligent and friendly agricultural assistant. Provide accurate and relevant answers about crops, farming, and agricultural practices.")
+    ]
+
+def get_response(question):
+    st.session_state['flow_messages'].append(HumanMessage(content=question))
+    answer = chat(st.session_state['flow_messages'])
+    st.session_state['flow_messages'].append(AIMessage(content=answer.content))
+    return answer.content
+
+st.markdown('<h1 class="title">🌾 Agricultural AI Assistant</h1>', unsafe_allow_html=True)
+st.sidebar.header("🔹 Features")
+features = st.sidebar.radio("Choose a feature:", ("Crop Recommendation", "Crop Disease Diagnosis", "Conversational Q&A"))
+
+if features == "Crop Recommendation":
+    st.write("### 📊 Provide the necessary agricultural parameters:")
+
+    N = st.number_input('Nitrogen', min_value=0, max_value=150, step=1)
+    P = st.number_input('Phosphorus', min_value=0, max_value=100, step=1)
+    K = st.number_input('Potassium', min_value=0, max_value=100, step=1)
+    temp = st.number_input('Temperature (°C)', min_value=-10.0, max_value=60.0, step=0.1)
+    humidity = st.number_input('Humidity (%)', min_value=0.0, max_value=100.0, step=0.1)
+    ph = st.number_input('pH', min_value=0.0, max_value=14.0, step=0.1)
+    rainfall = st.number_input('Rainfall (mm)', min_value=0.0, max_value=1000.0, step=1.0)
+
+    if st.button('🌱 Get Recommendation'):
+        feature_list = [N, P, K, temp, humidity, ph, rainfall]
+        single_pred = np.array(feature_list).reshape(1, -1)
+        
+        prediction = model.predict(single_pred)[0]  
+
+        crop = str(prediction).strip().title()
+
+        st.success(f"🌾 **{crop}** is the best crop for the provided data!")
+
+elif features == "Crop Disease Diagnosis":
+    st.write("### 🦠 Diagnose Crop Diseases")
+
+    symptoms = st.text_input("🔍 Enter Symptoms (e.g., yellow leaves, wilting):")
+    crop = st.text_input("🌱 Enter Crop Name (e.g., Tomato, Wheat):")
+    location = st.text_input("📍 Enter Location (e.g., Punjab, India):")
+    season = st.selectbox("🗓 Select Season:", ["Summer", "Winter", "Rainy", "Spring", "Autumn"])
+
+    disease_prompt = PromptTemplate(
+        input_variables=["symptoms", "crop", "location", "season"],
+        template=(
+            "You are an expert plant pathologist assisting farmers in diagnosing crop diseases.\n\n"
+            "📌 **Symptoms:** {symptoms}\n"
+            "🌱 **Crop:** {crop}\n"
+            "📍 **Location:** {location}\n"
+            "🗓 **Season:** {season}\n\n"
+            "### 🦠 Possible Disease(s) and Causes:\n"
+            "- Analyze symptoms and list possible diseases.\n"
+            "- Mention environmental and pest-related causes.\n\n"
+            "### 💊 Treatment & Remedies:\n"
+            "- Suggest **organic** and **chemical** treatments.\n"
+            "- Recommend suitable pesticides or fungicides (if needed).\n\n"
+            "### 🛡 Preventive Measures:\n"
+            "- Guide the farmer on crop rotation, irrigation, and soil treatment.\n"
+            "- Suggest resistant crop varieties if available."
+        )
+    )
+
+    if st.button("🩺 Diagnose"):
+        chain = LLMChain(llm=chat, prompt=disease_prompt)
+        response = chain.run(symptoms=symptoms, crop=crop, location=location, season=season)
+        st.write(response)
+
+elif features == "Conversational Q&A":
+    st.write("### 💬 Ask an Agriculture-related Question")
+    user_input = st.text_input("Your Question:")
+    if st.button("🤖 Ask AI"):
+        if user_input.strip():
+            response = get_response(user_input)
+            st.subheader("AI Response:")
+            st.write(response)
+        else:
+            st.warning("⚠️ Please enter a question!")
+

model_selection.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np \n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(\"C:\\\\Users\\\\saipr\\\\Crop_Recommendation\\\\data\\\\Crop_recommendation.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2195</th>\n",
+       "      <td>107</td>\n",
+       "      <td>34</td>\n",
+       "      <td>32</td>\n",
+       "      <td>26.774637</td>\n",
+       "      <td>66.413269</td>\n",
+       "      <td>6.780064</td>\n",
+       "      <td>177.774507</td>\n",
+       "      <td>coffee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2196</th>\n",
+       "      <td>99</td>\n",
+       "      <td>15</td>\n",
+       "      <td>27</td>\n",
+       "      <td>27.417112</td>\n",
+       "      <td>56.636362</td>\n",
+       "      <td>6.086922</td>\n",
+       "      <td>127.924610</td>\n",
+       "      <td>coffee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2197</th>\n",
+       "      <td>118</td>\n",
+       "      <td>33</td>\n",
+       "      <td>30</td>\n",
+       "      <td>24.131797</td>\n",
+       "      <td>67.225123</td>\n",
+       "      <td>6.362608</td>\n",
+       "      <td>173.322839</td>\n",
+       "      <td>coffee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2198</th>\n",
+       "      <td>117</td>\n",
+       "      <td>32</td>\n",
+       "      <td>34</td>\n",
+       "      <td>26.272418</td>\n",
+       "      <td>52.127394</td>\n",
+       "      <td>6.758793</td>\n",
+       "      <td>127.175293</td>\n",
+       "      <td>coffee</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199</th>\n",
+       "      <td>104</td>\n",
+       "      <td>18</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23.603016</td>\n",
+       "      <td>60.396475</td>\n",
+       "      <td>6.779833</td>\n",
+       "      <td>140.937041</td>\n",
+       "      <td>coffee</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2200 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        N   P   K  temperature   humidity        ph    rainfall   label\n",
+       "0      90  42  43    20.879744  82.002744  6.502985  202.935536    rice\n",
+       "1      85  58  41    21.770462  80.319644  7.038096  226.655537    rice\n",
+       "2      60  55  44    23.004459  82.320763  7.840207  263.964248    rice\n",
+       "3      74  35  40    26.491096  80.158363  6.980401  242.864034    rice\n",
+       "4      78  42  42    20.130175  81.604873  7.628473  262.717340    rice\n",
+       "...   ...  ..  ..          ...        ...       ...         ...     ...\n",
+       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee\n",
+       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610  coffee\n",
+       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839  coffee\n",
+       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293  coffee\n",
+       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041  coffee\n",
+       "\n",
+       "[2200 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "label\n",
+       "rice           100\n",
+       "maize          100\n",
+       "jute           100\n",
+       "cotton         100\n",
+       "coconut        100\n",
+       "papaya         100\n",
+       "orange         100\n",
+       "apple          100\n",
+       "muskmelon      100\n",
+       "watermelon     100\n",
+       "grapes         100\n",
+       "mango          100\n",
+       "banana         100\n",
+       "pomegranate    100\n",
+       "lentil         100\n",
+       "blackgram      100\n",
+       "mungbean       100\n",
+       "mothbeans      100\n",
+       "pigeonpeas     100\n",
+       "kidneybeans    100\n",
+       "chickpea       100\n",
+       "coffee         100\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['label'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Splitting the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X=df.drop('label',axis=1)\n",
+    "y=df['label']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Assuming X is your feature set and y is the corresponding label\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025/02/16 01:16:03 INFO mlflow.tracking.fluent: Experiment with name 'RandomForest_GridSearch' does not exist. Creating a new experiment.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025/02/16 01:18:02 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Parameters: {'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
+      "Best Accuracy: 0.9960227272727271\n",
+      "🏃 View run illustrious-cub-960 at: http://127.0.0.1:5000/#/experiments/809510633914373352/runs/7be5a439659d4274a47adfc717813c77\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/809510633914373352\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mlflow\n",
+    "import mlflow.sklearn\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n",
+    "\n",
+    "mlflow.set_experiment(\"RandomForest_GridSearch\")\n",
+    "\n",
+    "with mlflow.start_run():\n",
+    "    rf = RandomForestClassifier(random_state=42)\n",
+    "\n",
+    "    params = {\n",
+    "        'n_estimators': [50, 100, 200],\n",
+    "        'max_depth': [None, 10, 20, 30], \n",
+    "        'min_samples_split': [2, 5, 10],  \n",
+    "        'min_samples_leaf': [1, 2, 4],  \n",
+    "        'criterion': ['gini', 'entropy']  \n",
+    "    }\n",
+    "\n",
+    "    grid_search_rf = GridSearchCV(estimator=rf, param_grid=params, \n",
+    "                               cv=5, scoring='accuracy', n_jobs=-1, verbose=2)\n",
+    "\n",
+    "    grid_search_rf.fit(X_train, y_train)\n",
+    "\n",
+    "    best_params = grid_search_rf.best_params_\n",
+    "    best_score = grid_search_rf.best_score_\n",
+    "\n",
+    "    mlflow.log_params(best_params)\n",
+    "    mlflow.log_metric(\"best_accuracy\", best_score)\n",
+    "\n",
+    "    mlflow.sklearn.log_model(grid_search_rf.best_estimator_, \"best_random_forest_model\")\n",
+    "\n",
+    "    print(\"Best Parameters:\", best_params)\n",
+    "    print(\"Best Accuracy:\", best_score)\n",
+    "\n",
+    "    mlflow.end_run()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_rfc=grid_search_rf.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'criterion': 'gini',\n",
+       " 'max_depth': 10,\n",
+       " 'min_samples_leaf': 1,\n",
+       " 'min_samples_split': 5,\n",
+       " 'n_estimators': 100}"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search_rf.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9960227272727271"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search_rf.best_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9954545454545455"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
+    "y_pred=model_rfc.predict(X_test)\n",
+    "accuracy_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 19  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0 19  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(confusion_matrix(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trying Out Support Vector Machines Algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025/02/16 01:21:04 INFO mlflow.tracking.fluent: Experiment with name 'SVC_GridSearch' does not exist. Creating a new experiment.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025/02/16 01:21:10 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Parameters: {'C': 0.1, 'kernel': 'linear'}\n",
+      "Best Accuracy: 0.9857954545454545\n",
+      "🏃 View run agreeable-goose-248 at: http://127.0.0.1:5000/#/experiments/308727505154579858/runs/b513565508424ee1883922873cb0fa35\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/308727505154579858\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mlflow\n",
+    "import mlflow.sklearn\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n",
+    "\n",
+    "mlflow.set_experiment(\"SVC_GridSearch\")\n",
+    "\n",
+    "with mlflow.start_run():\n",
+    "    svc = SVC()\n",
+    "\n",
+    "    params = {\n",
+    "        'C': [0.1, 1, 10, 100],  \n",
+    "        'kernel': ['linear', 'rbf', 'poly', 'sigmoid']\n",
+    "    }\n",
+    "\n",
+    "    grid_search_svc = GridSearchCV(estimator=svc, param_grid=params, \n",
+    "                                   cv=5, scoring='accuracy', n_jobs=-1, verbose=2)\n",
+    "\n",
+    "    grid_search_svc.fit(X_train, y_train)\n",
+    "\n",
+    "    best_params = grid_search_svc.best_params_\n",
+    "    best_score = grid_search_svc.best_score_\n",
+    "\n",
+    "    mlflow.log_params(best_params)\n",
+    "    mlflow.log_metric(\"best_accuracy\", best_score)\n",
+    "\n",
+    "    mlflow.sklearn.log_model(grid_search_svc.best_estimator_, \"best_svc_model\")\n",
+    "\n",
+    "    print(\"Best Parameters:\", best_params)\n",
+    "    print(\"Best Accuracy:\", best_score)\n",
+    "\n",
+    "    mlflow.end_run()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_svc=grid_search_svc.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred1=model_svc.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9931818181818182"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test,y_pred1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  1  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  1  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0 19  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(confusion_matrix(y_test,y_pred1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gradient Boosting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025/02/16 01:24:17 INFO mlflow.tracking.fluent: Experiment with name 'GradientBoosting_GridSearch' does not exist. Creating a new experiment.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025/02/16 01:25:36 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Parameters: {'learning_rate': 0.1, 'n_estimators': 100}\n",
+      "Best Accuracy: 0.9875\n",
+      "🏃 View run rare-auk-421 at: http://127.0.0.1:5000/#/experiments/148959594547896690/runs/417ba78c49f845bd9ff43a5f9a381fb2\n",
+      "🧪 View experiment at: http://127.0.0.1:5000/#/experiments/148959594547896690\n"
+     ]
+    }
+   ],
+   "source": [
+    "import mlflow\n",
+    "import mlflow.sklearn\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n",
+    "\n",
+    "mlflow.set_experiment(\"GradientBoosting_GridSearch\")\n",
+    "\n",
+    "with mlflow.start_run():\n",
+    "    gb = GradientBoostingClassifier(random_state=42)\n",
+    "\n",
+    "    params = {\n",
+    "        'n_estimators': [50, 100, 150],  \n",
+    "        'learning_rate': [0.01, 0.05, 0.1],  \n",
+    "    }\n",
+    "\n",
+    "    grid_search_gb = GridSearchCV(estimator=gb, param_grid=params, \n",
+    "                                  cv=5, scoring='accuracy', n_jobs=-1, verbose=2)\n",
+    "\n",
+    "    grid_search_gb.fit(X_train, y_train)\n",
+    "\n",
+    "    best_params = grid_search_gb.best_params_\n",
+    "    best_score = grid_search_gb.best_score_\n",
+    "\n",
+    "    mlflow.log_params(best_params)\n",
+    "    mlflow.log_metric(\"best_accuracy\", best_score)\n",
+    "\n",
+    "    mlflow.sklearn.log_model(grid_search_gb.best_estimator_, \"best_gradient_boosting_model\")\n",
+    "\n",
+    "    print(\"Best Parameters:\", best_params)\n",
+    "    print(\"Best Accuracy:\", best_score)\n",
+    "\n",
+    "    mlflow.end_run()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_gb=grid_search_gb.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred2=model_gb.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9886363636363636"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test,y_pred2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 19  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 19  0  0  1  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0 19  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  2  0  0  0  0  0  0  0  0  0  0  0 18  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(confusion_matrix(y_test,y_pred2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1,16 @@
+pandas 
+numpy 
+seaborn 
+matplotlib
+scikit-learn
+langchain
+langchain_groq
+python-dotenv
+xgboost
+mlflow
+pypdf
+langchain_community
+sentence-transformers 
+faiss-cpu
+langchain_hugginface
+rank_bm25

testing_prompts.ipynb

@@ -0,0 +1,758 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_groq import ChatGroq\n",
+    "import os\n",
+    "GROQ_API_KEY=os.getenv(\"GROQ_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm=ChatGroq(model_name=\"gemma2-9b-it\",api_key=GROQ_API_KEY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response=llm.invoke(\"What is Crop Optimization\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Crop optimization is a multi-faceted approach aiming to maximize the yield and quality of agricultural produce while minimizing resource use and environmental impact. \n",
+      "\n",
+      "Here's a breakdown:\n",
+      "\n",
+      "**Goals of Crop Optimization:**\n",
+      "\n",
+      "* **Increased Yield:** Producing more crops per unit of land, leading to higher economic returns for farmers.\n",
+      "* **Improved Quality:** Enhancing the size, shape, color, taste, and nutritional value of crops.\n",
+      "* **Resource Efficiency:** Optimizing the use of water, fertilizers, pesticides, and other inputs, reducing costs and environmental pollution.\n",
+      "* **Sustainability:** Promoting environmentally friendly practices that conserve natural resources and protect biodiversity.\n",
+      "\n",
+      "**Methods Used in Crop Optimization:**\n",
+      "\n",
+      "Crop optimization leverages a variety of techniques, including:\n",
+      "\n",
+      "* **Precision Agriculture:** Using technologies like GPS, sensors, and drones to collect data on soil conditions, crop health, and weather patterns. This data is then analyzed to make site-specific decisions about irrigation, fertilization, and pest control.\n",
+      "* **Data Analytics:** Analyzing historical yield data, weather patterns, and market trends to identify optimal planting times, crop varieties, and management practices.\n",
+      "* **Crop Modeling:** Using mathematical models to simulate crop growth and development under different conditions. These models can help predict yield potential and identify potential risks.\n",
+      "* **Genetic Engineering:** Developing crops with improved traits such as resistance to pests, diseases, and drought.\n",
+      "* **Integrated Pest Management (IPM):** Implementing a holistic approach to pest control that emphasizes prevention, monitoring, and targeted interventions.\n",
+      "* **Conservation Agriculture:** Promoting practices like no-till farming, crop rotation, and cover cropping to improve soil health and reduce erosion.\n",
+      "\n",
+      "**Benefits of Crop Optimization:**\n",
+      "\n",
+      "* **Increased profitability for farmers:** Higher yields and reduced input costs can lead to significant economic gains.\n",
+      "* **Enhanced food security:** Optimizing crop production can help meet the growing demand for food in a sustainable way.\n",
+      "* **Environmental protection:** Reducing resource use and minimizing pollution can contribute to a healthier planet.\n",
+      "\n",
+      "**Challenges of Crop Optimization:**\n",
+      "\n",
+      "* **Cost of technology:** Implementing precision agriculture and other advanced technologies can be expensive.\n",
+      "* **Data management:** Collecting, storing, and analyzing large amounts of data can be challenging.\n",
+      "* **Knowledge gap:** Farmers may need training and support to effectively use new technologies and practices.\n",
+      "* **Regulatory hurdles:** Genetic engineering and other innovations may face regulatory challenges.\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Overall, crop optimization is a promising approach to improving agricultural productivity and sustainability. By combining innovative technologies with sound farming practices, we can create a more efficient, resilient, and environmentally friendly food system.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing Basic Prompts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "template = \"\"\"You are an AI farming assistant designed to provide accurate, practical, and easy-to-understand agricultural advice. Your goal is to assist farmers in crop management, pest control, soil health, irrigation techniques, weather forecasting, livestock care, and sustainable farming practices. Always provide region-specific and season-specific recommendations. If a farmer asks about something outside agriculture, politely redirect them back to farming topics.\n",
+    "Use simple language and practical solutions tailored for small and large-scale farmers. \n",
+    "Maintain a helpful, supportive, and problem-solving tone.\n",
+    "    \n",
+    "Question: {question}\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=['question'], \n",
+    "    template=template  \n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\saipr\\AppData\\Local\\Temp\\ipykernel_23300\\3966671106.py:2: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 1.0. Use :meth:`~RunnableSequence, e.g., `prompt | llm`` instead.\n",
+      "  llm_chain=LLMChain(llm=llm,prompt=prompt)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "llm_chain=LLMChain(llm=llm,prompt=prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response=llm.invoke(\"What is Crop Optimization\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Crop optimization refers to a set of practices and technologies aimed at **maximizing the yield and quality of crops while minimizing the environmental impact and resource consumption**. \n",
+      "\n",
+      "It's a multi-faceted approach that combines various techniques, including:\n",
+      "\n",
+      "**1. Precision Agriculture:**\n",
+      "\n",
+      "* **Data-driven decision making:** Utilizing sensor data, satellite imagery, and weather forecasts to understand specific field conditions and tailor management practices accordingly.\n",
+      "* **Variable rate technology:** Applying inputs like fertilizers, pesticides, and water at varying rates across a field based on precise needs identified through data analysis.\n",
+      "\n",
+      "**2. Crop Management Practices:**\n",
+      "\n",
+      "* **Optimal planting density:** Determining the ideal number of plants per unit area to maximize sunlight capture and resource utilization.\n",
+      "* **Integrated Pest Management (IPM):** Employing a combination of biological, cultural, and chemical methods to control pests and diseases, minimizing reliance on pesticides.\n",
+      "* **Nutrient Management:** Utilizing soil tests and crop requirements to optimize fertilizer application, reducing nutrient runoff and promoting soil health.\n",
+      "\n",
+      "**3. Technological Advancements:**\n",
+      "\n",
+      "* **Drones and Robotics:** Utilizing drones for aerial imagery, crop monitoring, and targeted spraying, while robots can automate tasks like planting, weeding, and harvesting.\n",
+      "* **Artificial Intelligence (AI):** Applying machine learning algorithms to analyze vast amounts of data and predict crop yields, identify disease outbreaks, and optimize irrigation schedules.\n",
+      "\n",
+      "**4. Sustainable Practices:**\n",
+      "\n",
+      "* **Conservation tillage:** Minimizing soil disturbance to preserve soil structure, reduce erosion, and enhance water infiltration.\n",
+      "* **Crop rotation:** Alternating different crops in a field to break pest cycles, improve soil fertility, and reduce reliance on chemical inputs.\n",
+      "\n",
+      "**Benefits of Crop Optimization:**\n",
+      "\n",
+      "* **Increased yield and profitability:** By maximizing resource utilization and minimizing losses, farmers can achieve higher crop yields and increase their income.\n",
+      "* **Reduced environmental impact:**  Sustainable practices like precision irrigation and reduced pesticide use minimize water and chemical pollution.\n",
+      "* **Enhanced resource efficiency:** Optimizing nutrient and water application reduces waste and promotes responsible resource management.\n",
+      "* **Improved food security:** By increasing crop production, crop optimization contributes to global food security and supports a growing population.\n",
+      "\n",
+      "\n",
+      "Overall, crop optimization is a crucial strategy for ensuring sustainable and efficient agricultural practices in the face of growing global food demands and environmental challenges.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With a well-designed prompt, the LLM:\n",
+    "\n",
+    "✔ Produces structured, detailed, and farmer-focused answers.\n",
+    "\n",
+    "✔ Provides practical advice instead of just a theoretical explanation.\n",
+    "\n",
+    "✔ Makes technology more accessible and actionable.\n",
+    "\n",
+    "✔ Uses clear, engaging, and farmer-friendly language.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📝 AI Response: content=\"That's a great question!  Choosing the best irrigation method for rice farming depends on a few things, like the size of your farm, your soil type, and the amount of water available to you. \\n\\nHere are some popular options:\\n\\n**1. Flood Irrigation:** This is the traditional method, where the entire field is flooded with water. \\n\\n* **Pros:** Relatively simple and inexpensive to set up. \\n* **Cons:** Can waste a lot of water, increase the risk of weed growth, and may not be suitable for all soil types.\\n\\n**2. Alternate Wetting and Drying (AWD):** This method involves flooding the field for a period of time, then allowing it to dry out partially before reflooding.\\n\\n* **Pros:** Saves water compared to flood irrigation, reduces weed growth, and can improve soil health.\\n* **Cons:** Requires more careful monitoring and management.\\n\\n**3. System of Rice Intensification (SRI):** This method uses a combination of techniques, including reduced water use, wider spacing between plants, and the use of seedlings that are already a few weeks old.\\n\\n* **Pros:** Significantly reduces water use, increases yields, and can improve soil fertility.\\n* **Cons:** Requires more labor and time, and may not be suitable for all rice varieties.\\n\\n**4. Sprinkler Irrigation:** This method uses sprinklers to deliver water directly to the rice plants.\\n\\n* **Pros:** More efficient than flood irrigation, can be used on sloped land, and can be automated.\\n* **Cons:** More expensive to set up than flood irrigation, and can be affected by wind.\\n\\n**5. Drip Irrigation:** This method delivers water directly to the roots of the rice plants through a network of tubes.\\n\\n* **Pros:** Most efficient irrigation method, saves water, and can improve yields.\\n* **Cons:** Most expensive to set up, and requires careful maintenance.\\n\\n**To get the best advice for your specific situation, I recommend talking to your local agricultural extension office. They can help you assess your needs and recommend the most suitable irrigation method for your farm.**\\n\\nGood luck with your rice farming! \\n\" additional_kwargs={} response_metadata={'token_usage': {'completion_tokens': 451, 'prompt_tokens': 119, 'total_tokens': 570, 'completion_time': 0.82, 'prompt_time': 0.003625076, 'queue_time': 0.056550666, 'total_time': 0.823625076}, 'model_name': 'gemma2-9b-it', 'system_fingerprint': 'fp_10c08bf97d', 'finish_reason': 'stop', 'logprobs': None} id='run-ce8e13b3-fb33-4602-afd0-c44d5a81501c-0' usage_metadata={'input_tokens': 119, 'output_tokens': 451, 'total_tokens': 570}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from langchain_groq import ChatGroq\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "load_dotenv()\n",
+    "GROQ_API_KEY = os.getenv(\"GROQ_API_KEY\")\n",
+    "\n",
+    "llm = ChatGroq(model_name=\"gemma2-9b-it\", api_key=GROQ_API_KEY)\n",
+    "\n",
+    "template = \"\"\"You are an AI farming assistant designed to provide accurate, practical, and easy-to-understand agricultural advice. \n",
+    "Your goal is to assist farmers in crop management, pest control, soil health, irrigation techniques, weather forecasting, \n",
+    "livestock care, and sustainable farming practices. Always provide region-specific and season-specific recommendations. \n",
+    "\n",
+    "Use simple language and practical solutions tailored for small and large-scale farmers. Maintain a helpful, supportive, and problem-solving tone.\n",
+    "\n",
+    "Question: {question}\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(input_variables=['question'], template=template)\n",
+    "\n",
+    "def agriculture_rag(query):\n",
+    "    formatted_prompt = prompt.format(question=query)\n",
+    "\n",
+    "    response = llm.invoke(formatted_prompt)\n",
+    "\n",
+    "    return response\n",
+    "\n",
+    "query = \"What is the best irrigation method for rice farming?\"\n",
+    "response = agriculture_rag(query)\n",
+    "print(\"📝 AI Response:\", response)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "agriculture_prompt = PromptTemplate(\n",
+    "    input_variables=[\"question\", \"location\", \"crop\", \"season\"],\n",
+    "    template=(\n",
+    "        \"You are an expert agriculture assistant helping farmers with their queries. \"\n",
+    "        \"Provide a detailed yet simple answer for the given question.\\n\\n\"\n",
+    "        \"Farmer's Question: {question}\\n\"\n",
+    "        \"Location: {location}\\n\"\n",
+    "        \"Crop Type: {crop}\\n\"\n",
+    "        \"Current Season: {season}\\n\\n\"\n",
+    "        \"Answer the question in a way that a farmer with basic knowledge can understand. \"\n",
+    "        \"Use practical advice, avoiding overly technical terms unless necessary.\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain=LLMChain(llm=llm,prompt=agriculture_prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mangu, also known as citrus canker, is a serious disease that can harm your orange trees in Nalgonda's summer heat. Here's what you can do to prevent it:\n",
+      "\n",
+      "**1. Choose Resistant Varieties:**\n",
+      "\n",
+      "* Talk to your local agricultural officer or nursery owners about orange varieties that are less susceptible to mangu in your area. \n",
+      "\n",
+      "**2. Healthy Planting Material:**\n",
+      "\n",
+      "* **Never** use diseased saplings or cuttings. Buy your plants from reliable nurseries that follow good practices and offer disease-free stock.\n",
+      "\n",
+      "**3. Keep Trees Clean:**\n",
+      "\n",
+      "* Regularly remove fallen leaves and fruit from around the base of your trees. This helps prevent the disease from spreading.\n",
+      "\n",
+      "**4. Avoid Overcrowding:**\n",
+      "\n",
+      "* Give your orange trees enough space to grow.  Crowded trees have poor air circulation, which makes them more vulnerable to diseases.\n",
+      "\n",
+      "**5. Proper Watering:**\n",
+      "\n",
+      "* Water your trees deeply but infrequently. Avoid overhead watering, as water droplets can spread the disease.\n",
+      "\n",
+      "**6. Avoid Injury:**\n",
+      "\n",
+      "* Handle your trees carefully to avoid damaging the bark. Cuts and wounds can provide entry points for the disease.\n",
+      "\n",
+      "**7. Copper-Based Sprays:**\n",
+      "\n",
+      "*  Ask your local agricultural officer about copper-based fungicides. These can be applied as a preventive measure, especially during the summer months when the disease is more active. **Always follow instructions carefully** and use protective gear.\n",
+      "\n",
+      "**8. Early Detection:**\n",
+      "\n",
+      "*  Learn to recognize the symptoms of mangu: small, dark spots on leaves, fruits, and twigs. If you see any signs, isolate the affected trees and consult your agricultural officer immediately.\n",
+      "\n",
+      "Remember, prevention is key to managing mangu. By following these practices, you can help keep your orange trees healthy and productive. \n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = chain.run(\n",
+    "    question=\"How can I prevent mangu \",\n",
+    "    location=\"Nalgonda, India\",\n",
+    "    crop=\"orange\",\n",
+    "    season=\"Summer\"\n",
+    ")\n",
+    "\n",
+    "print(response)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You are an expert agriculture assistant helping farmers with their queries. Provide a detailed yet simple answer for the given question.\n",
+      "\n",
+      "📌 **Farmer's Question:** How can I prevent pests in my tomato crop?\n",
+      "📍 **Location:** Maharashtra, India\n",
+      "🌾 **Crop Type:** Tomato\n",
+      "🗓 **Current Season:** Summer\n",
+      "\n",
+      "### 🌱 Problem Analysis\n",
+      "1️⃣ **Possible Reasons:**\n",
+      "- Identify the key reasons causing this issue.\n",
+      "- Mention environmental, soil, or pest-related causes if relevant.\n",
+      "\n",
+      "2️⃣ **Solution Approach:**\n",
+      "- Provide practical and actionable steps to resolve the issue.\n",
+      "- Include organic and chemical solutions if applicable.\n",
+      "\n",
+      "3️⃣ **Preventive Measures:**\n",
+      "- Suggest best farming practices to avoid this issue in the future.\n",
+      "- Mention crop rotation, irrigation techniques, or natural remedies.\n",
+      "\n",
+      "4️⃣ **Expert Tips:**\n",
+      "- Provide any additional insights from agricultural experts.\n",
+      "- Mention tools, fertilizers, or techniques that could be useful.\n",
+      "\n",
+      "📢 Provide your response in **simple and easy-to-understand** language so that farmers can easily apply the solution.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "agriculture_prompt = PromptTemplate(\n",
+    "    input_variables=[\"question\", \"location\", \"crop\", \"season\"],\n",
+    "    template=(\n",
+    "        \"You are an expert agriculture assistant helping farmers with their queries. \"\n",
+    "        \"Provide a detailed yet simple answer for the given question.\\n\\n\"\n",
+    "        \"📌 **Farmer's Question:** {question}\\n\"\n",
+    "        \"📍 **Location:** {location}\\n\"\n",
+    "        \"🌾 **Crop Type:** {crop}\\n\"\n",
+    "        \"🗓 **Current Season:** {season}\\n\\n\"\n",
+    "        \"### 🌱 Problem Analysis\\n\"\n",
+    "        \"1️⃣ **Possible Reasons:**\\n\"\n",
+    "        \"- Identify the key reasons causing this issue.\\n\"\n",
+    "        \"- Mention environmental, soil, or pest-related causes if relevant.\\n\\n\"\n",
+    "        \"2️⃣ **Solution Approach:**\\n\"\n",
+    "        \"- Provide practical and actionable steps to resolve the issue.\\n\"\n",
+    "        \"- Include organic and chemical solutions if applicable.\\n\\n\"\n",
+    "        \"3️⃣ **Preventive Measures:**\\n\"\n",
+    "        \"- Suggest best farming practices to avoid this issue in the future.\\n\"\n",
+    "        \"- Mention crop rotation, irrigation techniques, or natural remedies.\\n\\n\"\n",
+    "        \"4️⃣ **Expert Tips:**\\n\"\n",
+    "        \"- Provide any additional insights from agricultural experts.\\n\"\n",
+    "        \"- Mention tools, fertilizers, or techniques that could be useful.\\n\\n\"\n",
+    "        \"📢 Provide your response in **simple and easy-to-understand** language \"\n",
+    "        \"so that farmers can easily apply the solution.\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Example usage:\n",
+    "query = agriculture_prompt.format(\n",
+    "    question=\"How can I prevent pests in my tomato crop?\",\n",
+    "    location=\"Maharashtra, India\",\n",
+    "    crop=\"Tomato\",\n",
+    "    season=\"Summer\"\n",
+    ")\n",
+    "\n",
+    "print(query)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain=LLMChain(llm=llm,prompt=agriculture_prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "##  Keeping Pests Away from Your Tomatoes in Maharashtra's Summer\n",
+      "\n",
+      "Hello!  Summer in Maharashtra can be tough on tomatoes, especially with all the pests that come along.  Don't worry, here's how to protect your crop:\n",
+      "\n",
+      "**1.  What's Bugging Your Tomatoes?**\n",
+      "\n",
+      "* **Aphids:** Tiny green or black bugs sucking sap from leaves. They make your plants weak.\n",
+      "* **Whiteflies:** Tiny, white, flying insects that also suck sap. They can make leaves yellow and drop.\n",
+      "* **Fruitworms:** Caterpillars that eat into your tomatoes. \n",
+      "* **Cutworms:** These fat, grey caterpillars cut off young tomato plants at the base.\n",
+      "\n",
+      "**2.  Fighting Back!**\n",
+      "\n",
+      "* **For Aphids & Whiteflies:**\n",
+      "\n",
+      "    * **Neem Oil:** Mix neem oil with water and spray on your plants. It's a natural insecticide that keeps these pests away.\n",
+      "    * **Soap Spray:** Mix a little soap with water and spray it on your plants. This can also help control these small pests.\n",
+      "* **For Fruitworms:**\n",
+      "\n",
+      "    * **Traps:**  Set up sticky traps near your tomato plants to catch adult moths.\n",
+      "    * **Handpicking:**  Check your plants daily and remove any caterpillars you find.\n",
+      "* **For Cutworms:**\n",
+      "\n",
+      "    * **Protect Young Plants:** Wrap the base of young plants with cardboard collars to prevent cutworms from reaching them.\n",
+      "\n",
+      "**3.  Prevention is Key!**\n",
+      "\n",
+      "* **Healthy Soil:** Use compost to improve your soil. Healthy soil makes strong plants that are less likely to be attacked by pests.\n",
+      "* **Crop Rotation:** Don't plant tomatoes in the same place year after year. Rotate with other crops like beans or corn.\n",
+      "* **Companion Planting:** Plant basil, marigolds, or onions near your tomatoes. They can help repel pests naturally.\n",
+      "* **Proper Watering:** Don't overwater your tomatoes, as this can attract pests. Water deeply but infrequently.\n",
+      "* **Monitor Regularly:**  Check your plants often for signs of pests. Early detection is key to stopping an infestation!\n",
+      "\n",
+      "**4.  Expert Tips:**\n",
+      "\n",
+      "* **Contact your local agricultural extension office:** They can provide specific advice for your region.\n",
+      "* **Learn about Integrated Pest Management (IPM):** This approach combines different pest control methods, including natural ones, to minimize harm to the environment.\n",
+      "\n",
+      "\n",
+      "Remember, protecting your tomato crop takes a little effort, but with these tips, you can enjoy a bountiful harvest!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run the chain with inputs\n",
+    "response = chain.run(\n",
+    "    question=\"How can I prevent pests in my tomato crop?\",\n",
+    "    location=\"Maharashtra, India\",\n",
+    "    crop=\"Tomato\",\n",
+    "    season=\"Summer\"\n",
+    ")\n",
+    "\n",
+    "print(response)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "crop_disease_prompt = PromptTemplate(\n",
+    "    input_variables=[\"symptoms\", \"crop\", \"location\", \"season\"],\n",
+    "    template=(\n",
+    "        \"You are an expert plant pathologist assisting farmers in diagnosing crop diseases.\\n\\n\"\n",
+    "        \"📌 **Farmer's Observation:** {symptoms}\\n\"\n",
+    "        \"🌱 **Crop:** {crop}\\n\"\n",
+    "        \"📍 **Location:** {location}\\n\"\n",
+    "        \"🗓 **Current Season:** {season}\\n\\n\"\n",
+    "        \"### 🦠 Possible Disease(s) and Causes:\\n\"\n",
+    "        \"- Analyze the symptoms and identify possible diseases.\\n\"\n",
+    "        \"- Mention environmental and pest-related causes.\\n\\n\"\n",
+    "        \"### 💊 Treatment & Remedies:\\n\"\n",
+    "        \"- Suggest **organic** and **chemical** treatments.\\n\"\n",
+    "        \"- Recommend suitable pesticides or fungicides (if needed).\\n\\n\"\n",
+    "        \"### 🛡 Preventive Measures:\\n\"\n",
+    "        \"- Guide the farmer on crop rotation, irrigation, and soil treatment.\\n\"\n",
+    "        \"- Suggest resistant crop varieties if available.\\n\\n\"\n",
+    "        \"Provide clear, easy-to-follow instructions that farmers can apply practically.\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain=LLMChain(llm=llm,prompt=crop_disease_prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "##  Yellow Spots on Tomato Leaves: A Guide for Punjab Farmers\n",
+      "\n",
+      "**Possible Diseases:**\n",
+      "\n",
+      "Based on your observation of yellow spots on tomato leaves and stunted growth during summer in Punjab, here are some possible diseases:\n",
+      "\n",
+      "* **Early Blight (Alternaria solani):** This fungal disease is very common in tomato during hot and humid weather. It causes small, brown, target-like lesions with yellow halos on the lower leaves, eventually spreading upwards. \n",
+      "* **Septoria Leaf Spot (Septoria lycopersici):** Another fungal disease, Septoria leaf spot appears as small, circular, dark brown to black spots with yellow halos on the leaves. \n",
+      "* **Tomato Mosaic Virus (ToMV):** This viral disease can cause mosaic patterns (yellowing and greening) on leaves, stunted growth, and fruit deformities.\n",
+      "\n",
+      "**Environmental & Pest-related Causes:**\n",
+      "\n",
+      "* **High humidity and temperatures:** Both early blight and septoria leaf spot thrive in warm, humid conditions common in Punjab summers.\n",
+      "* **Overwatering:** Excessive watering can create ideal conditions for fungal diseases.\n",
+      "* **Poor air circulation:** Dense planting or lack of pruning can hinder air flow, promoting fungal growth.\n",
+      "\n",
+      "**Treatment & Remedies:**\n",
+      "\n",
+      "**Organic Options:**\n",
+      "\n",
+      "* **Neem oil:** Mix 2-3 teaspoons of neem oil with 1 liter of water and spray on affected plants. Neem oil has antifungal and insecticidal properties.\n",
+      "* **Copper fungicide:** Apply a copper-based fungicide according to label instructions.\n",
+      "\n",
+      "**Chemical Options:**\n",
+      "\n",
+      "* **Mancozeb:** This fungicide is effective against early blight and septoria leaf spot.\n",
+      "* **Chlorothalonil:** Another broad-spectrum fungicide that can be used.\n",
+      "\n",
+      "**Always follow label instructions for application rates and safety precautions.**\n",
+      "\n",
+      "**Preventive Measures:**\n",
+      "\n",
+      "* **Crop rotation:** Avoid planting tomatoes in the same field year after year. \n",
+      "* **Proper irrigation:** Water deeply but infrequently, ensuring good drainage. \n",
+      "* **Ensure good air circulation:** Space plants adequately and prune suckers to improve airflow.\n",
+      "* **Resistant varieties:** Choose tomato varieties resistant to early blight and septoria leaf spot. Ask your local agricultural extension office for recommendations.\n",
+      "* **Healthy soil:**  Conduct soil tests and amend as needed.\n",
+      "\n",
+      "**Additional Tips:**\n",
+      "\n",
+      "* **Monitor your plants regularly.** Early detection and intervention are crucial for managing diseases.\n",
+      "* **Remove infected leaves and dispose of them properly.** This helps prevent the spread of disease.\n",
+      "* **Consider consulting a plant pathologist for a definitive diagnosis and tailored advice.**\n",
+      "\n",
+      "\n",
+      "Remember, a healthy crop starts with a healthy soil and proper management practices. \n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = chain.run(\n",
+    "    symptoms=\"Yellow spots on leaves, stunted growth.\",\n",
+    "    crop=\"Tomato\",\n",
+    "    location=\"Punjab, India\",\n",
+    "    season=\"Summer\"\n",
+    ")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "soil_health_prompt = PromptTemplate(\n",
+    "    input_variables=[\"soil_type\", \"pH\", \"nutrient_levels\", \"crop\"],\n",
+    "    template=(\n",
+    "        \"You are a soil scientist helping farmers analyze soil health and recommend fertilizers.\\n\\n\"\n",
+    "        \"🧪 **Soil Type:** {soil_type}\\n\"\n",
+    "        \"📉 **pH Level:** {pH}\\n\"\n",
+    "        \"🌱 **Nutrient Levels (NPK & others):** {nutrient_levels}\\n\"\n",
+    "        \"🌾 **Crop Being Grown:** {crop}\\n\\n\"\n",
+    "        \"### 🏜 Soil Health Analysis:\\n\"\n",
+    "        \"- Explain the current condition of the soil.\\n\"\n",
+    "        \"- Identify deficiencies or imbalances.\\n\\n\"\n",
+    "        \"### 💊 Fertilizer & Soil Treatment Recommendations:\\n\"\n",
+    "        \"- Suggest suitable **organic** and **chemical** fertilizers.\\n\"\n",
+    "        \"- Mention appropriate dosages and application frequency.\\n\\n\"\n",
+    "        \"### 🌿 Long-Term Soil Improvement:\\n\"\n",
+    "        \"- Recommend crop rotation strategies.\\n\"\n",
+    "        \"- Suggest composting or natural soil enrichment techniques.\\n\"\n",
+    "        \"Provide advice in an **easy-to-follow** format for farmers.\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain=LLMChain(llm=llm,prompt=soil_health_prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "## Your Tomato Soil Check-Up:\n",
+      "\n",
+      "**Current Condition:**\n",
+      "\n",
+      "Your loamy soil is a great foundation for tomatoes! It's well-draining and holds moisture nicely. The pH of 6.2 is also ideal for tomato growth. \n",
+      "\n",
+      "**Nutrient Needs:**\n",
+      "\n",
+      "Your soil is a bit low on nitrogen, which is essential for leafy growth and overall plant vigor. Phosphorus levels are moderate, good for root development and flowering.\n",
+      "\n",
+      "**Fertilizer & Treatment Recommendations:**\n",
+      "\n",
+      "**Organic Options:**\n",
+      "\n",
+      "* **Compost:** This is a powerhouse! Mix in 2-3 inches of well-rotted compost before planting to boost all nutrients, improve soil structure, and feed beneficial microbes.\n",
+      "* **Blood Meal:** High in nitrogen, apply 1-2 tablespoons per tomato plant every 4-6 weeks.\n",
+      "* **Bone Meal:** Adds phosphorus and calcium, apply 1-2 tablespoons per plant at planting time and again in mid-season.\n",
+      "\n",
+      "**Chemical Options:**\n",
+      "\n",
+      "* **Granular NPK Fertilizer:** Look for an NPK ratio like 12-6-6. Apply 1-2 tablespoons per plant at planting time and again every 4-6 weeks.\n",
+      "* **Liquid Nitrogen Fertilizer:**  Apply every 2-3 weeks during the growing season, following instructions on the label.\n",
+      "\n",
+      "**Important Note:**  Always apply fertilizers according to package instructions and avoid over-fertilizing, which can harm your plants. \n",
+      "\n",
+      "**Long-Term Soil Improvement:**\n",
+      "\n",
+      "* **Crop Rotation:**  Avoid planting tomatoes in the same spot year after year. Rotate with crops like beans, squash, or corn to replenish nutrients and break pest cycles.\n",
+      "* **Cover Crops:** Plant clover or rye in the off-season to prevent erosion, suppress weeds, and fix nitrogen in the soil.\n",
+      "\n",
+      "By following these tips, you can keep your soil healthy and your tomatoes thriving!\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = chain.run(\n",
+    "    soil_type=\"Loamy\",\n",
+    "    pH=\"6.2\",\n",
+    "    nutrient_levels=\"Low nitrogen, moderate phosphorus\",\n",
+    "    crop=\"Tomato\"\n",
+    ")\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}