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import streamlit as st
import ast
import base64
import streamlit.components.v1 as components
from transformers import pipeline
from gtts import gTTS
st.set_page_config(page_title="AR/VR Code Visualizer", layout="wide")
st.title("👓 AR/VR Code Visualizer with Call Relationships + Narration")
@st.cache_resource
def load_model():
return pipeline("summarization", model="philschmid/bart-large-cnn-samsum")
summarizer = load_model()
uploaded_file = st.file_uploader("📁 Upload a Python file", type=["py"])
if uploaded_file:
code = uploaded_file.read().decode("utf-8")
st.code(code, language="python")
# Parse AST for functions and call relationships
tree = ast.parse(code)
class FunctionCallVisitor(ast.NodeVisitor):
def __init__(self):
self.calls = {}
def visit_FunctionDef(self, node):
caller = node.name
self.calls[caller] = []
for child in ast.walk(node):
if isinstance(child, ast.Call) and isinstance(child.func, ast.Name):
self.calls[caller].append(child.func.id)
self.generic_visit(node)
visitor = FunctionCallVisitor()
visitor.visit(tree)
call_graph = visitor.calls
all_functions = list(call_graph.keys())
# Show call relationships
st.subheader("📊 Call Relationships")
for fn, callees in call_graph.items():
st.write(f"🔹 `{fn}` calls: {', '.join(callees) if callees else 'None'}")
# Hugging Face summarization
prompt = f"Explain the structure and purpose of the following functions and how they call each other: {call_graph}"
summary = summarizer(prompt, max_length=60, min_length=15, do_sample=False)
summary_text = summary[0]['summary_text']
st.success(summary_text)
# Voice narration
st.subheader("🔊 Voice Narration of Code Summary")
tts = gTTS(text=summary_text)
tts.save("summary.mp3")
audio_file = open("summary.mp3", "rb")
st.audio(audio_file.read(), format="audio/mp3")
# Generate A-Frame scene
def generate_aframe(call_graph):
spacing = 3
boxes = []
lines = []
positions = {}
for i, fn in enumerate(call_graph):
x = i * spacing
positions[fn] = (x, 1, -3)
boxes.append(f'''
<a-box position="{x} 1 -3" depth="0.5" height="0.5" width="2" color="#FFC65D"></a-box>
<a-text value="{fn}" position="{x} 1.8 -3" align="center" color="#000"></a-text>
''')
for caller, callees in call_graph.items():
for callee in callees:
if callee in positions:
x1, y1, z1 = positions[caller]
x2, y2, z2 = positions[callee]
lines.append(f'''
<a-entity line="start: {x1} {y1} {z1}; end: {x2} {y2} {z2}; color: red"></a-entity>
''')
html = f'''
<!DOCTYPE html>
<html>
<head>
<script src="https://aframe.io/releases/1.3.0/aframe.min.js"></script>
</head>
<body>
<a-scene background="color: #ECECEC">
<a-entity position="0 1.6 3">
<a-camera wasd-controls-enabled="true" look-controls-enabled="true"></a-camera>
</a-entity>
<a-light type="ambient" color="#FFF"></a-light>
<a-plane rotation="-90 0 0" width="40" height="40" color="#7BC8A4"></a-plane>
{''.join(boxes)}
{''.join(lines)}
</a-scene>
</body>
</html>
'''
return html
aframe_html = generate_aframe(call_graph)
b64 = base64.b64encode(aframe_html.encode()).decode()
data_url = f"data:text/html;base64,{b64}"
st.subheader("🌐 3D Call Relationship Visualization")
components.iframe(data_url, height=500)
else:
st.info("Upload a Python file to visualize its structure, relationships, and get narrated summary.")
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