import gradio as gr
import pandas as pd
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
import joblib

# Load assets
print("🔄 Loading data and models...")
df = pd.read_csv("clean_feedback.csv")
print("✅ CSV loaded with columns:", df.columns.tolist())

embeddings = np.load("embeddings.npy")
print("✅ Embeddings loaded with shape:", embeddings.shape)

index = faiss.read_index("feedback.index")
print("✅ FAISS index loaded")

clf = joblib.load("feedback_model.pkl")
print("✅ Sentiment model loaded")

model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2", device="cpu")
print("✅ SentenceTransformer ready")

def classify_feedback(text, top_k=5):
    print(f"\n🧠 New query: {text}")

    if not text.strip():
        return "⚠️ Please enter a feedback text."

    # Embed query
    query_emb = model.encode([text])
    print("Embedding shape:", query_emb.shape)

    # Search similar samples
    distances, indices = index.search(query_emb, top_k)
    print("Retrieved indices:", indices)

    retrieved = df.iloc[indices[0]]
    if "Sentence" not in df.columns:
        return "❌ Column 'Sentence' not found in CSV. Columns are: " + ", ".join(df.columns)

    # Predict sentiment
    try:
        sentiment = clf.predict(query_emb)[0]
    except Exception as e:
        return f"❌ Model prediction error: {str(e)}"

    examples = "\n".join([f"{i+1}. {s}" for i, s in enumerate(retrieved['Sentence'].tolist())])
    print("✅ Prediction done")

    return f"**Predicted Sentiment:** {sentiment}\n\n**Similar Feedbacks:**\n{examples}"

# Gradio UI
demo = gr.Interface(
    fn=classify_feedback,
    inputs=[gr.Textbox(label="Enter Student Feedback")],
    outputs=[gr.Markdown(label="Prediction & Explanation")],
    title="🎓 Student Feedback RAG System",
    description="Classifies Roman Urdu/English student feedback with context and reasoning."
)

demo.launch()