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.gitattributes

@@ -33,3 +33,5 @@ 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
+bg_music.mp3 filter=lfs diff=lfs merge=lfs -text
+campus_bg.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,6 @@
 ---
-title: Attachment Style Game
-emoji: 🐨
-colorFrom: green
-colorTo: indigo
+title: attachment-style-game
+app_file: app.py
 sdk: gradio
 sdk_version: 5.45.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,136 @@
+import re
+import gradio as gr
+from datasets import load_dataset
+from huggingface_hub import InferenceClient
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+
+# 1. Connect to Hugging Face Inference API (free hosted models)
+# You MUST add your Hugging Face token in the Space "Secrets" panel as HF_TOKEN
+client = InferenceClient("microsoft/phi-2", token=None)  # if hosted on your HF account, no token needed
+
+def generate_text(prompt, max_tokens=150):
+    """Call Hugging Face Inference API to generate text."""
+    response = client.text_generation(prompt, max_new_tokens=max_tokens)
+    return response
+
+# 2. Load dataset for RAG
+data = load_dataset("Amod/mental_health_counseling_conversations")
+docs = [f"Q: {entry['Context']}\nA: {entry['Response']}" for entry in data['train']]
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+vectordb = Chroma.from_texts(texts=docs, embedding=embedding_model)
+
+# 3. Traits dictionary
+traits = {"Secure": 0, "Anxious": 0, "Avoidant": 0}
+MAX_SCENES = 5
+
+# Narrative instructions for LLM
+narrator_instructions = (
+    "You are a supportive narrative AI creating an interactive story for the player.\n"
+    "Write in second person (\"you\" as Alex) and describe feelings and events vividly.\n"
+    "The story should feel emotionally deep and realistic, suitable for an adult audience (age 25-35).\n"
+    "Focus on how attachment styles influence Alex's feelings and decisions in a romantic relationship.\n"
+    "In each scene, after narrating the situation, offer the player choices labeled A), B), (and possibly C)).\n"
+    "Each choice should represent a different attachment response (secure, avoidant, anxious).\n"
+    "Do NOT reveal the outcome of the choices yet; just present the options.\n"
+)
+
+# --- STORY FUNCTIONS ---
+def parse_choices(text):
+    """Split LLM output into narrative and choices."""
+    choice_start = text.find("A)")
+    if choice_start == -1:
+        return text, []
+    narrative = text[:choice_start].strip()
+    choice_lines = text[choice_start:].splitlines()
+    choices = [line.strip() for line in choice_lines if re.match(r"^[A-C]\)", line.strip())]
+    return narrative, choices
+
+def generate_initial_scene():
+    intro = (
+        "Alex is a 26-year-old graduate student who recently moved to the UK. "
+        "They are in a romantic relationship, but lately Alex feels insecure. "
+        "One morning, Alex wakes up with a knot in their stomach. "
+        "Their partner didn’t reply to a message from last night. "
+        "Thoughts race through Alex’s mind..."
+    )
+    text = generate_text(narrator_instructions + intro + "\nWhat does Alex do?")
+    return parse_choices(text)
+
+def next_step(selected_choice, story_so_far, scene_index, traits_dict):
+    if not selected_choice:
+        return story_so_far, [], scene_index, traits_dict
+
+    choice_label = selected_choice[0]
+    choice_text = selected_choice[3:].strip()
+    scene_index += 1
+
+    # Update traits
+    text_lower = choice_text.lower()
+    if any(word in text_lower for word in ["talk", "share", "open", "honest", "calm"]):
+        traits_dict["Secure"] += 1
+    if any(word in text_lower for word in ["ignore", "avoid", "distance", "later", "alone"]):
+        traits_dict["Avoidant"] += 1
+    if any(word in text_lower for word in ["worry", "jealous", "clingy", "panic", "angry", "upset"]):
+        traits_dict["Anxious"] += 1
+
+    # Retrieve advice
+    results = vectordb.similarity_search(choice_text + " relationship anxiety", k=1)
+    advice = results[0].page_content.split("A:")[1].strip() if results else ""
+
+    # Prompt for next scene
+    last_line = story_so_far.splitlines()[-1] if story_so_far else ""
+    prompt = narrator_instructions
+    prompt += f"Previously: {last_line}\n"
+    prompt += f"The player chose option {choice_label}: {choice_text}.\n"
+    if advice:
+        prompt += f"[Helpful thought: {advice}]\n"
+    prompt += "Continue the story and provide new choices.\n"
+
+    text = generate_text(prompt)
+    return *parse_choices(text), scene_index, traits_dict
+
+def summarize_story(story, traits_dict):
+    prompt = (
+        f"Alex's story is ending. Traits: Secure={traits_dict['Secure']}, "
+        f"Anxious={traits_dict['Anxious']}, Avoidant={traits_dict['Avoidant']}.\n"
+        "Write a reflective ending about what Alex learned about relationships."
+    )
+    return generate_text(prompt, max_tokens=120)
+
+# --- GRADIO UI ---
+with gr.Blocks(css=".gradio-container {background-color:#f5f7fa;} #story_box {padding:15px; background:#fff; border-radius:10px;}") as demo:
+    story_state = gr.State("")
+    scene_state = gr.State(0)
+    traits_state = gr.State(traits.copy())
+
+    gr.HTML("<h1>💙 Attachment Style Interactive Story</h1><p>Explore how attachment styles shape relationships.</p>")
+
+    # Background music
+    gr.HTML("""
+    <audio src='file=bg_music.mp3' autoplay loop hidden></audio>
+    """)
+
+    story_box = gr.Markdown("*(Loading story...)*", elem_id="story_box")
+    choices_radio = gr.Radio(label="Choose:", choices=[], visible=False)
+    next_btn = gr.Button("Next")
+
+    def start_game():
+        narrative, choices = generate_initial_scene()
+        return narrative, choices, 1, {"Secure":0,"Anxious":0,"Avoidant":0}
+
+    def play_step(choice, story, scene, traits_dict):
+        if scene >= MAX_SCENES:
+            return story + "\n\n" + summarize_story(story, traits_dict), [], scene, traits_dict
+        narrative, choices, new_scene, traits_dict = next_step(choice, story, scene, traits_dict)
+        story += "\n\n" + narrative
+        if new_scene >= MAX_SCENES:
+            story += "\n\n" + summarize_story(story, traits_dict)
+            return story, [], new_scene, traits_dict
+        return story, choices, new_scene, traits_dict
+
+    demo.load(start_game, inputs=None, outputs=[story_box, choices_radio, scene_state, traits_state])
+    next_btn.click(play_step, inputs=[choices_radio, story_state, scene_state, traits_state],
+                   outputs=[story_box, choices_radio, scene_state, traits_state])
+
+demo.launch()

bg_music.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a3eb4a7dfec8d62ed6bc9dde06a87ca4e8b225ccfa4031343a1febb85d2f47f1
+size 4289097

requirements.txt

@@ -0,0 +1,6 @@
+transformers
+datasets
+gradio
+langchain
+langchain-community
+sentence-transformers