Update app.py

app.py

@@ -1,11 +1,41 @@
-from transformers import pipeline
+# Import necessary libraries
+import pandas as pd
+import numpy as np
+import folium
+from sklearn.cluster import KMeans
+import gradio as gr
 
-# Load GPT-3 model for text generation
-generator = pipeline("text-generation", model="gpt-3")
+# Load dataset
+def load_data(file):
+    data = pd.read_csv(file)
+    return data
 
-# Function for generating text
-def generate_text(prompt):
-    return generator(prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
+# Function to process the data and apply clustering or regression model
+def process_data(data):
+    X = data[['temperature', 'humidity', 'soil_type']]  # Example features
+    kmeans = KMeans(n_clusters=3)  # Example clustering model
+    data['moisture_category'] = kmeans.fit_predict(X)
+    return data
 
-# Example test
-print(generate_text("Once upon a time"))
+# Function to create an interactive map using folium
+def create_map(data):
+    m = folium.Map(location=[data['latitude'].mean(), data['longitude'].mean()], zoom_start=12)
+    for _, row in data.iterrows():
+        folium.CircleMarker(location=[row['latitude'], row['longitude']],
+                            radius=10,
+                            color='blue' if row['moisture_category'] == 0 else 'green' if row['moisture_category'] == 1 else 'red',
+                            fill=True).add_to(m)
+    return m
+
+# Gradio Interface
+def soil_moisture_mapping(file):
+    data = load_data(file)
+    processed_data = process_data(data)
+    map_result = create_map(processed_data)
+    return map_result
+
+# Gradio Interface setup
+iface = gr.Interface(fn=soil_moisture_mapping, inputs="file", outputs="html")
+
+# Launch Gradio app
+iface.launch()