Initial deployment

.gitattributes

@@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.meta filter=lfs diff=lfs merge=lfs -text
+*.index filter=lfs diff=lfs merge=lfs -text
+*.data-* filter=lfs diff=lfs merge=lfs -text
+*.tflearn* filter=lfs diff=lfs merge=lfs -text
+*.tflearn filter=lfs diff=lfs merge=lfs -text
+MingleModel.tflearn.index filter=lfs diff=lfs merge=lfs -text
+MingleModel.tflearn.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+MingleModel.tflearn.meta filter=lfs diff=lfs merge=lfs -text

MingleModel.tflearn.data-00000-of-00001

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MingleModel.tflearn.index

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MingleModel.tflearn.meta

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app.py

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+# app.py — Mingle Chatbot for Hugging Face Spaces
+
+import nltk
+from nltk.stem.lancaster import LancasterStemmer
+import numpy as np
+import tflearn
+import tensorflow as tf
+import random
+import json
+import pickle
+import gradio as gr
+import os
+
+# Ensure punkt tokenizer
+nltk.download('punkt')
+
+# Patch tflearn for TF 2.x
+import tensorflow.python.util.nest as nest
+if not hasattr(nest, "is_sequence"):
+    nest.is_sequence = lambda seq: isinstance(seq, (list, tuple))
+
+# --- Load Data ---
+stemmer = LancasterStemmer()
+
+with open("intents.json") as file:
+    data = json.load(file)
+
+with open("data.pickle", "rb") as f:
+    words, labels, training, output = pickle.load(f)
+
+# --- Build Model ---
+tf.compat.v1.reset_default_graph()
+
+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)
+
+model = tflearn.DNN(net)
+model.load("MingleModel.tflearn")
+
+# --- Helper: Bag of Words ---
+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]
+    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_response(message):
+    message = message.lower()
+    results = model.predict([bag_of_words(message, words)])
+    results_index = np.argmax(results)
+    tag = labels[results_index]
+
+    for tg in data["intents"]:
+        if tg['tag'] == tag:
+            responses = tg['responses']
+            return random.choice(responses)
+    return "I'm not sure I understand. Tell me more 🙂"
+
+# --- Gradio Interface ---
+with gr.Blocks(title="Mingle Chatbot") as demo:
+    gr.Markdown("## 💬 Mingle — Friendly Social Chatbot")
+    chatbot = gr.Chatbot(label="MingleBot")
+    msg = gr.Textbox(label="Type your message here...")
+
+    def respond(message, chat_history):
+        reply = chat_response(message)
+        chat_history.append((message, reply))
+        return "", chat_history
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+
+# --- Launch ---
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))

checkpoint

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+model_checkpoint_path: "/home/dickson/Documents/projects/php/Mingle/Generate/MingleModel.tflearn"
+all_model_checkpoint_paths: "/home/dickson/Documents/projects/php/Mingle/Generate/MingleModel.tflearn"

data.pickle

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+size 20013

intents.json

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+{
+    "intents": [
+        {
+            "tag": "greeting",
+            "patterns": [
+                "Hi",
+                "Hey",
+                "Hello",
+                "What's up",
+                "Hey there",
+                "Hi Mingle",
+                "Yo"
+            ],
+            "responses": [
+                "Hey there! How’s your day going?",
+                "Hi! Nice to see you here 😊",
+                "Hey! What brings you to Mingle today?",
+                "Hello! How are you feeling right now?"
+            ]
+        },
+        {
+            "tag": "flirting",
+            "patterns": [
+                "You look nice",
+                "You're cute",
+                "I like you",
+                "You're charming",
+                "You seem fun",
+                "I like your vibe"
+            ],
+            "responses": [
+                "Aww, that’s sweet of you 😄",
+                "Thanks, you know how to make someone smile!",
+                "You’ve got a great energy too!",
+                "Flattery will get you everywhere 😉"
+            ]
+        },
+        {
+            "tag": "getting_to_know",
+            "patterns": [
+                "What do you do for fun?",
+                "Tell me about yourself",
+                "What are your hobbies?",
+                "What kind of music do you like?",
+                "What do you like doing on weekends?"
+            ],
+            "responses": [
+                "I love deep chats and good vibes. What about you?",
+                "I’m into music, movies, and great conversations. You?",
+                "Mostly chilling, exploring new things, and meeting cool people like you!",
+                "I enjoy learning about others — tell me something fun about you!"
+            ]
+        },
+        {
+            "tag": "compliment",
+            "patterns": [
+                "You're funny",
+                "You're sweet",
+                "You're smart",
+                "You're kind"
+            ],
+            "responses": [
+                "You’re making me blush 😊",
+                "You’re pretty amazing yourself!",
+                "You’ve got great taste, clearly!",
+                "Thanks! That means a lot coming from you."
+            ]
+        },
+        {
+            "tag": "connection",
+            "patterns": [
+                "I like talking to you",
+                "You make me feel good",
+                "This is nice",
+                "I feel connected to you"
+            ],
+            "responses": [
+                "I’m really enjoying this too ❤️",
+                "That’s sweet — I feel the same!",
+                "It’s nice when a chat just *clicks*, you know?",
+                "Glad to hear that. You’ve got a nice vibe."
+            ]
+        },
+        {
+            "tag": "date_ideas",
+            "patterns": [
+                "What would be a fun date?",
+                "How about we hang out?",
+                "Where should we go on a date?",
+                "What do you like to do on dates?"
+            ],
+            "responses": [
+                "Something chill — maybe coffee, a walk, or a cozy dinner 😊",
+                "I like good food, better company, and endless laughter.",
+                "A sunset walk sounds perfect. What’s your ideal kind of date?"
+            ]
+        },
+        {
+            "tag": "goodbye",
+            "patterns": [
+                "Goodbye",
+                "See you later",
+                "Talk to you soon",
+                "Bye",
+                "Catch you later"
+            ],
+            "responses": [
+                "See you soon — I’ll be looking forward to it 😉",
+                "Had a lovely chat. Talk again soon!",
+                "Take care! Don’t be a stranger 💫",
+                "Goodbye for now — stay awesome!"
+            ]
+        }
+    ]
+}

requirements.txt

@@ -0,0 +1,5 @@
+tflearn
+tensorflow==2.15.0
+nltk
+numpy
+gradio