Create app.py

app.py

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+import gradio as gr
+import json
+import random
+import pickle
+import numpy as np
+import nltk
+from tensorflow.keras.models import  Sequential
+from tensorflow.keras.optimizers.legacy import SGD
+from tensorflow.keras.layers import Dense,Activation,Dropout
+from tensorflow.keras.models import load_model
+
+
+from nltk.stem import  WordNetLemmatizer
+lemmatizer = WordNetLemmatizer()
+
+import nltk
+nltk.download('punkt')
+nltk.download('wordnet')
+
+# Load intents and model
+new_intents = json.loads(open('intents.json').read())
+words = pickle.load(open('words.pkl', 'rb'))
+classes = pickle.load(open('classes.pkl', 'rb'))
+model = load_model("chatbot_final.h5")
+
+
+# Load intents and model
+new_intents = json.loads(open('intents.json').read())
+words = pickle.load(open('words.pkl', 'rb'))
+classes = pickle.load(open('classes.pkl', 'rb'))
+model = load_model("chatbot_final.h5")
+
+    # Function to clean up a sentence
+def clean_up_sentence(sentence):
+    sentence_words = nltk.word_tokenize(sentence)
+    sentence_words = [lemmatizer.lemmatize(word) for word in sentence_words]
+    return sentence_words
+
+# Function to convert a sentence to a bag of words
+def bag_of_words(sentence):
+    sentence_words = clean_up_sentence(sentence)
+    bag = [0] * len(words)
+    for w in sentence_words:
+        for i, word in enumerate(words):
+            if word == w:
+                bag[i] = 1
+    return np.array(bag)
+
+# Function to predict the intent of a sentence
+def predict_class(sentence):
+    bow = bag_of_words(sentence)
+    res = model.predict(np.array([bow]))[0]
+    ERROR_THRESHOLD = 0.25
+    results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
+
+    results.sort(key=lambda x: x[1], reverse=True)
+    return_list = []
+    for r in results:
+        return_list.append({'intent': classes[r[0]], 'probability': str(r[1])})
+
+    if not return_list:
+        # No intents detected, return a default response
+        return_list.append({'intent': 'default', 'probability': '1.0'})
+
+    return return_list# Function to get a response based on detected intent
+def get_response(intents_list, intents_json):
+    tag = intents_list[0]['intent']
+    list_of_intents = intents_json['intents']
+    for i in list_of_intents:
+        if i['tag'] == tag:
+            result = random.choice(i['responses'])
+            break
+    return result
+
+
+
+
+def greet(name):
+    ints = predict_class(name)
+    res = get_response(ints)
+    return res
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()