Merge pull request #1 from viole4244/main

LOML

Author: duckling69

__pycache__/streamlit.cpython-310.pyc

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+import json
+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import pickle
+
+# Load the model, tokenizer, and label encoder
+model = load_model('chatbot_model.h5')
+
+with open('tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+with open('label_encoder.pickle', 'rb') as handle:
+    label_encoder = pickle.load(handle)
+
+# Function to predict intents based on user input
+def predict_intent(user_input):
+    user_input_seq = tokenizer.texts_to_sequences([user_input])
+    user_input_pad = pad_sequences(user_input_seq, maxlen=model.input_shape[1], padding='post')
+    prediction = model.predict(user_input_pad)
+    intent = label_encoder.inverse_transform([np.argmax(prediction)])
+    return intent[0]
+
+# Function to generate responses based on predicted intents
+def generate_response(intent):
+    with open('therapy_data.json', 'r') as f:
+        data = json.load(f)
+    responses = [i['responses'] for i in data['intents'] if i['tag'] == intent]
+    response = np.random.choice(responses[0])
+    return response
+
+# Streamlit UI
+st.title("AI Therapy Chatbot")
+
+user_input = st.text_input("You:", "")
+
+if user_input:
+    intent = predict_intent(user_input)
+    response = generate_response(intent)
+    st.write(f"Chatbot: {response}")

app.py

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+flask
+google-cloud-vertex-ai
+pandas
+transformers