Create app.py

app.py

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+import gradio as gr
+import json
+import nltk
+from nltk.stem import WordNetLemmatizer
+
+# Load the intents from JSON file
+with open('intents.json', 'r') as file:
+    intents_data = json.load(file)
+
+lemmatizer = WordNetLemmatizer()
+
+words = []
+classes = []
+documents = []
+ignore_letters = ['?', '!', '.', ',']
+
+for intent in intents_data['intents']:
+    for pattern in intent['patterns']:
+        word_list = nltk.word_tokenize(pattern)
+        words.extend(word_list)
+        documents.append((word_list, intent['tag']))
+        if intent['tag'] not in classes:
+            classes.append(intent['tag'])
+
+words = [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
+words = sorted(set(words))
+
+# Initialize Gradio components
+output_text = gr.Textbox()
+input_text = gr.Textbox()
+
+# Define a Gradio function
+def process_input(input_text):
+    # Your existing code here
+
+    # For example, let's return a sample output for demonstration
+    output_result = "Sample output for: " + input_text
+    return output_result
+
+# Create the Gradio Interface
+iface = gr.Interface(fn=process_input, inputs=input_text, outputs=output_text, live=True, capture_session=True)
+
+# Launch the Gradio Interface
+iface.launch()