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app.py

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+import streamlit as st
+import numpy as np
+import pickle
+from langchain.schema import HumanMessage, SystemMessage, AIMessage
+from langchain_groq import ChatGroq
+from dotenv import load_dotenv
+import os
+
+# Set page configuration
+st.set_page_config(
+    page_title="Agricultural AI Assistant and Crop Recommendation",
+    layout="wide"
+)
+
+# Load environment variables
+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")
+
+# Load the model and scaler
+model = pickle.load(open('model.pkl', 'rb'))
+ms = pickle.load(open('minmaxscaler.pkl', 'rb'))
+
+# Custom CSS for styling
+st.markdown("""
+<style>
+    body {
+        background: #BCBBB8;
+    }
+    .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)
+
+# Initialize session state for chatbot messages
+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.")
+    ]
+
+# Define the chatbot response function
+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
+
+# App features
+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", "Conversational Q&A"))
+
+if features == "Crop Recommendation":
+    st.write("""
+    ### Provide the necessary agricultural parameters:
+    """)
+
+    # Input fields for the 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)
+
+    # Button to trigger prediction
+    if st.button('Get Recommendation'):
+        # Feature list and transformation
+        feature_list = [N, P, K, temp, humidity, ph, rainfall]
+        single_pred = np.array(feature_list).reshape(1, -1)
+
+        # Apply scaling
+        scaled_features = ms.transform(single_pred)
+
+        # Make prediction
+        prediction = model.predict(scaled_features)
+
+        # Dictionary to map predictions to crop names
+        crop_dict = {
+            1: "Rice", 2: "Maize", 3: "Jute", 4: "Cotton", 5: "Coconut", 6: "Papaya", 7: "Orange",
+            8: "Apple", 9: "Muskmelon", 10: "Watermelon", 11: "Grapes", 12: "Mango", 13: "Banana",
+            14: "Pomegranate", 15: "Lentil", 16: "Blackgram", 17: "Mungbean", 18: "Mothbeans",
+            19: "Pigeonpeas", 20: "Kidneybeans", 21: "Chickpea", 22: "Coffee"
+        }
+
+        # Display the result
+        if prediction[0] in crop_dict:
+            crop = crop_dict[prediction[0]]
+            result = f"**{crop}** is the best crop to be cultivated with the provided data."
+            st.success(result)
+        else:
+            result = "Sorry, we could not determine the best crop to be cultivated with the provided data."
+            st.error(result)
+
+elif features == "Conversational Q&A":
+    st.write("""
+    ### Ask any question about crops, farming, and agriculture:
+    """)
+    user_input = st.text_input("Your Question:")
+    if st.button("Ask Question"):
+        if user_input.strip():
+            response = get_response(user_input)
+            st.subheader("The Response is:")
+            st.write(response)
+        else:
+            st.warning("Please enter a question!")

minmaxscaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b075f12ce9474dc2d430a8eac974f4624f0878557846acb2e33bc2bb9209c345
+size 760

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d1b84260229f84bff1f35dfbbf64eb3a84c6db1ab8090878c5a478305c0409d1
+size 3524942

requirements.txt

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+streamlit
+numpy
+scikit-learn
+langchain
+langchain-groq
+python-dotenv