Update app.py

app.py

@@ -8,67 +8,41 @@ import pickle
 import gradio as gr
 from nltk.stem.lancaster import LancasterStemmer
 
-# Ensure nltk downloads
 nltk.download('punkt')
 stemmer = LancasterStemmer()
 
-# Load intents and check for errors
-try:
-    with open("intents.json") as file:
-        data = json.load(file)
-except FileNotFoundError:
-    raise FileNotFoundError("The file 'intents.json' was not found.")
+# Load intents
+with open("intents.json") as file:
+    data = json.load(file)
 
-# Load or regenerate the data
+# Load or regenerate data.pickle
 try:
     with open("data.pickle", "rb") as f:
         words, labels, training, output = pickle.load(f)
 except FileNotFoundError:
-    # Regenerate the data.pickle if not found
-    words = []
-    labels = []
-    docs_x = []
-    docs_y = []
-
+    words, labels, docs_x, docs_y = [], [], [], []
     for intent in data["intents"]:
         for pattern in intent["patterns"]:
             wrds = nltk.word_tokenize(pattern)
             words.extend(wrds)
             docs_x.append(wrds)
             docs_y.append(intent["tag"])
-
         if intent["tag"] not in labels:
             labels.append(intent["tag"])
 
-    words = [stemmer.stem(w.lower()) for w in words if w not in ["?", "!", ".", ","]]
-    words = sorted(list(set(words)))
+    words = sorted(set(stemmer.stem(w.lower()) for w in words if w not in ["?", ".", ",", "!"]))
     labels = sorted(labels)
 
-    training = []
-    output = []
-
-    out_empty = [0 for _ in range(len(labels))]
-
+    training, output = [], []
+    out_empty = [0] * len(labels)
     for x, doc in enumerate(docs_x):
-        bag = []
-
-        wrds = [stemmer.stem(w.lower()) for w in doc]
-
-        for w in words:
-            if w in wrds:
-                bag.append(1)
-            else:
-                bag.append(0)
-
+        bag = [1 if stemmer.stem(w.lower()) in [stemmer.stem(word) for word in doc] else 0 for w in words]
         output_row = out_empty[:]
         output_row[labels.index(docs_y[x])] = 1
-
         training.append(bag)
         output.append(output_row)
 
-    training = np.array(training)
-    output = np.array(output)
-
+    training, output = np.array(training), np.array(output)
     with open("data.pickle", "wb") as f:
         pickle.dump((words, labels, training, output), f)
 
@@ -82,60 +56,44 @@ net = tflearn.regression(net)
 model = tflearn.DNN(net)
 try:
     model.load("MentalHealthChatBotmodel.tflearn")
-except Exception as e:
-    raise FileNotFoundError("Model file 'MentalHealthChatBotmodel.tflearn' could not be loaded.") from e
+except FileNotFoundError:
+    model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
+    model.save("MentalHealthChatBotmodel.tflearn")
 
-# Function to convert user input into a bag-of-words representation
+# Define chat function
 def bag_of_words(s, words):
-    bag = [0 for _ in range(len(words))]
-    s_words = nltk.word_tokenize(s)
-    s_words = [stemmer.stem(word.lower()) for word in s_words]
-
+    bag = [0] * len(words)
+    s_words = [stemmer.stem(w.lower()) for w in nltk.word_tokenize(s)]
     for se in s_words:
         for i, w in enumerate(words):
             if w == se:
                 bag[i] = 1
     return np.array(bag)
 
-# Chat function
 def chat(message, history=None):
     history = history or []
-    message = message.lower()
-
     try:
         bag = bag_of_words(message, words)
         results = model.predict([bag])
         results_index = np.argmax(results)
         tag = labels[results_index]
+        for tg in data["intents"]:
+            if tg['tag'] == tag:
+                response = random.choice(tg['responses'])
+                break
+        else:
+            response = "I'm sorry, I don't have a response for that."
     except Exception as e:
-        print(f"Error during processing: {e}")  # Debugging
         response = "I'm sorry, I couldn't understand your message."
-        history.append((message, response))
-        return history, history
-
-    for tg in data["intents"]:
-        if tg['tag'] == tag:
-            responses = tg['responses']
-            response = random.choice(responses)
-            break
-    else:
-        response = "I'm sorry, I don't have a response for that."
-
     history.append((message, response))
     return history, history
 
 # Gradio Interface
-css = """
-footer {display:none !important}
-div[data-testid="user"] {background-color: #253885 !important;}
-"""
 demo = gr.Interface(
     fn=chat,
-    inputs=[gr.Textbox(lines=1, label="Message"), gr.State([])],
+    inputs=[gr.Textbox(lines=1, label="Message"), gr.State()],
     outputs=[gr.Chatbot(label="Chat"), gr.State()],
-    allow_flagging="never",
-    title="Wellbeing Chatbot",
-    css=css
+    allow_flagging="never"
 )
 
 if __name__ == "__main__":