Spaces:
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"""
Root-level app.py for Hugging Face Spaces.
This wraps the main application from src/app.py
"""
import os
from pathlib import Path
import spaces
# Import the main app components
from src.app import load_vector_store, retrieve, build_prompt, create_pipeline, RAG_DEFAULT_K
import gradio as gr
from sentence_transformers import SentenceTransformer
# Configuration
MODEL_PATH = os.environ.get("MODEL_PATH", "ZamZeerak-Phi3-Pashto")
STORE_PATH = os.environ.get("STORE_PATH", "rag_store")
EMB_MODEL = os.environ.get("EMB_MODEL", "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
# Initialize components
print(f"Loading embedding model: {EMB_MODEL}")
emb_model = SentenceTransformer(EMB_MODEL)
print(f"Loading vector store from: {STORE_PATH}")
try:
index, texts = load_vector_store(STORE_PATH)
print(f"Loaded {len(texts)} text chunks")
except FileNotFoundError as e:
print(f"Warning: {e}")
print("RAG features will be disabled")
index, texts = None, []
print(f"Loading model: {MODEL_PATH}")
gen = create_pipeline(MODEL_PATH)
print("Model loaded successfully!")
def answer(question: str, k: int = RAG_DEFAULT_K):
"""Answer a question using RAG."""
if index is None or not texts:
return "❌ Vector store not initialized. Please contact the space owner."
try:
contexts = retrieve(emb_model, index, texts, question, int(k))
prompt = build_prompt(question, contexts)
out = gen(prompt)[0]['generated_text'][len(prompt):]
# Format response with sources
response = f"{out.strip()}\n\n---\n\n**د سرچينې (Sources):**\n\n"
for i, ctx in enumerate(contexts, 1):
response += f"{i}. {ctx[:200]}...\n\n"
return response
except Exception as e:
return f"❌ Error generating response: {str(e)}"
# Create Gradio interface
with gr.Blocks(title="Pashto Tutor (Phi-3 RAG)", theme=gr.themes.Soft()) as demo:
gr.HTML("""
<div style="text-align: center; padding: 20px;">
<h1>📚 Pashto Educational Tutor</h1>
<p>د پښتو ښوونيز ملګری - Powered by Phi-3 with RAG</p>
<p><em>Fine-tuned on Pashto educational content</em></p>
</div>
""")
with gr.Row():
with gr.Column():
question_input = gr.Textbox(
label="پوښتنه (Question)",
placeholder="خپله پوښتنه دلته وليکئ... (Enter your question here...)",
lines=3
)
k_slider = gr.Slider(
minimum=1,
maximum=10,
value=RAG_DEFAULT_K,
step=1,
label="Number of Context Passages"
)
submit_btn = gr.Button("ځواب ترلاسه کړئ (Get Answer)", variant="primary")
with gr.Column():
output = gr.Textbox(
label="ځواب + سرچينې (Answer + Sources)",
lines=15,
interactive=False
)
# Examples
gr.Examples(
examples=[
["د ریاضی په اړه راته ووایه", 4],
["د پښتو ګرامر څه دی؟", 4],
["د علوم اساسی مفاهیم تشریح کړه", 4],
],
inputs=[question_input, k_slider]
)
# Event handlers
submit_btn.click(
fn=answer,
inputs=[question_input, k_slider],
outputs=output
)
question_input.submit(
fn=answer,
inputs=[question_input, k_slider],
outputs=output
)
# Footer
gr.HTML("""
<div style="text-align: center; padding: 20px; margin-top: 20px; border-top: 1px solid #ddd;">
<p>⚠️ This is an educational tool. Verify information with official sources.</p>
<p>🔍 Uses retrieval-augmented generation (RAG) for accurate, context-aware responses.</p>
</div>
""")
if __name__ == "__main__":
demo.launch()
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