Update app.py

app.py

@@ -14,6 +14,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipe
 from nltk.tokenize import word_tokenize
 from nltk.stem.lancaster import LancasterStemmer
 import os
+from functools import lru_cache
 
 # Ensure necessary NLTK resources are downloaded
 nltk.download('punkt')
@@ -22,32 +23,29 @@ nltk.download('punkt')
 stemmer = LancasterStemmer()
 
 # Load intents.json
-try:
-    with open("intents.json") as file:
-        data = json.load(file)
-except FileNotFoundError:
-    raise FileNotFoundError("Error: 'intents.json' file not found. Ensure it exists in the current directory.")
+def load_intents(file_path):
+    with open(file_path) as file:
+        return json.load(file)
 
 # Load preprocessed data from pickle
-try:
-    with open("data.pickle", "rb") as f:
-        words, labels, training, output = pickle.load(f)
-except FileNotFoundError:
-    raise FileNotFoundError("Error: 'data.pickle' file not found. Ensure it exists and matches the model.")
+def load_preprocessed_data(file_path):
+    with open(file_path, "rb") as f:
+        return pickle.load(f)
 
 # 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)
+def build_model(words, labels, training, output):
+    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)
+    return tflearn.DNN(net)
 
 # 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.")
+def load_model(model_path, net):
+    model = tflearn.DNN(net)
+    model.load(model_path)
+    return model
 
 # Function to process user input into a bag-of-words format
 def bag_of_words(s, words):
@@ -61,7 +59,7 @@ def bag_of_words(s, words):
     return np.array(bag)
 
 # Chat function
-def chat(message, history):
+def chat(message, history, words, labels, model):
     history = history or []
     message = message.lower()
     
@@ -86,7 +84,6 @@ def chat(message, history):
     history.append((message, response))
     return history, history
 
-
 # Sentiment analysis setup
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
@@ -148,26 +145,30 @@ def detect_emotion(user_input):
 # Google Geocoding API setup to convert city name to latitude/longitude
 geocode_url = "https://maps.googleapis.com/maps/api/geocode/json"
 
+@lru_cache(maxsize=128)
 def get_lat_lon(location, api_key):
     params = {
         "address": location,
         "key": api_key
     }
-    response = requests.get(geocode_url, params=params)
-    if response.status_code == 200:
+    try:
+        response = requests.get(geocode_url, params=params)
+        response.raise_for_status()
         result = response.json()
         if result['status'] == 'OK':
-            # Return the first result's latitude and longitude
             location = result['results'][0]['geometry']['location']
             return location['lat'], location['lng']
-    return None, None
+        else:
+            return None, None
+    except requests.RequestException as e:
+        print(f"Error fetching coordinates: {e}")
+        return None, None
 
 # Get wellness professionals
 def get_wellness_professionals(location, api_key):
     query = "therapist OR counselor OR mental health professional OR marriage and family therapist OR psychotherapist OR psychiatrist OR psychologist OR nutritionist OR wellness doctor OR holistic practitioner OR integrative medicine OR chiropractor OR naturopath"
     radius = 50000  # 50 km radius
 
-    # Get the latitude and longitude from the location input
     lat, lon = get_lat_lon(location, api_key)
     
     if lat is None or lon is None:
@@ -210,8 +211,8 @@ def generate_map(wellness_data):
     return map_file
 
 # Gradio interface setup for user interaction
-def user_interface(message, location, history, api_key):
-    history, history = chat(message, history)
+def user_interface(message, location, history, api_key, words, labels, model):
+    history, history = chat(message, history, words, labels, model)
     
     # Sentiment analysis
     inputs = tokenizer_sentiment(message, return_tensors="pt")
@@ -231,7 +232,24 @@ def user_interface(message, location, history, api_key):
     suggestions_df = pd.DataFrame(resources, columns=["Subject", "Article URL"])
     suggestions_df["Video URL"] = video_link  # Add video URL column
     
-    return history, history, sentiment, emotion, resources, video_link, map_file, suggestions_df.to_html(escape=False)
+    return history, history, sentiment, emotion, suggestions_df.to_html(escape=False), map_file
+
+# Load data and model
+try:
+    data = load_intents("intents.json")
+except FileNotFoundError:
+    raise FileNotFoundError("Error: 'intents.json' file not found. Ensure it exists in the current directory.")
+
+try:
+    words, labels, training, output = load_preprocessed_data("data.pickle")
+except FileNotFoundError:
+    raise FileNotFoundError("Error: 'data.pickle' file not found. Ensure it exists and matches the model.")
+
+net = build_model(words, labels, training, output)
+try:
+    model = load_model("MentalHealthChatBotmodel.tflearn", net)
+except FileNotFoundError:
+    raise FileNotFoundError("Error: Trained model file 'MentalHealthChatBotmodel.tflearn' not found.")
 
 # Gradio chatbot interface
 chatbot = gr.Chatbot(label="Mental Health Chatbot")
@@ -239,13 +257,25 @@ location_input = gr.Textbox(label="Enter your location (latitude,longitude)", pl
 
 # Gradio interface definition
 demo = gr.Interface(
-    user_interface,
-    [gr.Textbox(label="Message"), location_input, "state", "text"],
-    [chatbot, "state", "text", "text", "json", "text", "html", "html"],  # Added additional output for the map
+    fn=lambda message, location, history, api_key: user_interface(message, location, history, api_key, words, labels, model),
+    inputs=[
+        gr.Textbox(label="Message"),
+        location_input,
+        "state",
+        "text"  # API Key input
+    ],
+    outputs=[
+        chatbot, 
+        "state", 
+        gr.Textbox(label="Sentiment"), 
+        gr.Textbox(label="Emotion"), 
+        gr.HTML(label="Resources"), 
+        gr.HTML(label="Map")
+    ],
     allow_flagging="never",
     title="Mental Health & Well-being Assistant"
 )
 
 # Launch Gradio interface
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()