Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 import torch
 import nltk
 import numpy as np
@@ -41,19 +41,20 @@ try:
 except FileNotFoundError:
     raise FileNotFoundError("Error: 'data.pickle' file not found. Ensure it exists and matches the model.")
 
-# Build the model structure
-net = tflearn.input_data(shape=[None, len(training[0])])
-net = tflearn.fully_connected(net, 8)
-net = tflearn.fully_connected(net, 8)
-net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
-net = tflearn.regression(net)
+# Build the model structure using Keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.losses import CategoricalCrossentropy
 
-# Load the trained model
-model = tflearn.DNN(net)
-try:
-    model.load("MentalHealthChatBotmodel.tflearn")
-except FileNotFoundError:
-    raise FileNotFoundError("Error: Trained model file 'MentalHealthChatBotmodel.tflearn' not found.")
+model = Sequential()
+model.add(Dense(8, input_shape=(len(training[0]),), activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(len(output[0]), activation='softmax'))
+model.compile(optimizer=Adam(), loss=CategoricalCrossentropy(), metrics=['accuracy'])
+
+# Load the trained model weights
+model.load_weights("MentalHealthChatBotmodel.tflearn")
 
 # Function to process user input into a bag-of-words format
 def bag_of_words(s, words):
@@ -73,7 +74,7 @@ def chat(message, history):
 
     try:
         # Predict the tag
-        results = model.predict([bag_of_words(message, words)])
+        results = model.predict(np.array([bag_of_words(message, words)]))
         results_index = np.argmax(results)
         tag = labels[results_index]
 
@@ -161,7 +162,7 @@ def scrape_website_for_contact_info(website):
         response = requests.get(website, timeout=5)
         soup = BeautifulSoup(response.content, 'html.parser')
 
-        phone_match = re.search(r'\(?\+?[0-9]*\)?[0-9_\- \(\)]*', soup.get_text())
+        phone_match = re.search(r'$$?\+?[0-9]*$$?[0-9_\- $$$$]*', soup.get_text())
         if phone_match:
             phone_number = phone_match.group()
 
@@ -242,7 +243,7 @@ def main():
 # Gradio UI setup
 with gr.Blocks() as demo:
     # Load pre-trained model and tokenizer
-    @gr.cache_resource
+    @gr.cache
     def load_model():
         tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
         model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
@@ -277,7 +278,7 @@ with gr.Blocks() as demo:
 
         try:
             # Predict the tag
-            results = model.predict([bag_of_words(message, words)])
+            results = model.predict(np.array([bag_of_words(message, words)]))
             results_index = np.argmax(results)
             tag = labels[results_index]
 
@@ -314,4 +315,4 @@ with gr.Blocks() as demo:
     fetch_button.click(fetch_data, inputs=None, outputs=data_output)
 
 # Launch Gradio interface
-demo.launch()
+demo.launch()