app.py

import gradio as gr
import torch
import requests
import json
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
from datetime import datetime

# === Load Models ===
print("Loading zero-shot classifier...")
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

print("Loading embedding model...")
embedding_model = SentenceTransformer("intfloat/e5-large")

print("Loading step-by-step generator...")
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2")

# === Qdrant Setup ===
print("Connecting to Qdrant...")
qdrant_client = QdrantClient(path="qdrant_data")
collection_name = "math_problems"

# === Guard Function ===
def is_valid_math_question(text):
    candidate_labels = ["math", "not math"]
    result = classifier(text, candidate_labels)
    return result['labels'][0] == "math" and result['scores'][0] > 0.7

# === Retrieval from Qdrant ===
def retrieve_from_qdrant(query):
    query_vector = embedding_model.encode(query).tolist()
    hits = qdrant_client.query_points(
        collection_name=collection_name,
        query_vector=query_vector,
        limit=3
    )
    return [hit.payload for hit in hits] if hits else []

# === Web Search Fallback ===
def web_search_tavily(query):
    TAVILY_API_KEY = "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"
    response = requests.post(
        "https://api.tavily.com/search",
        json={"api_key": TAVILY_API_KEY, "query": query, "search_depth": "advanced"},
    )
    return response.json().get("answer", "No answer found from Tavily.")

# === Generator ===
def generate_step_by_step_answer(question, context):
    prompt = f"Answer the following math question step-by-step:\nQuestion: {question}\nContext: {context}\nAnswer:"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        top_p=0.95,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# === Router ===
def router(question):
    if not is_valid_math_question(question):
        return "❌ Only math questions are accepted. Please rephrase.", ""

    retrieved = retrieve_from_qdrant(question)
    context = "\n".join([item["solution"] for item in retrieved if "solution" in item])
    if context:
        answer = generate_step_by_step_answer(question, context)
        return answer, context
    else:
        fallback = web_search_tavily(question)
        return fallback, "Tavily Search"

# === Feedback Storage ===
def store_feedback(question, answer, correct_answer):
    entry = {
        "question": question,
        "model_answer": answer,
        "correct_answer": correct_answer,
        "timestamp": str(datetime.now())
    }
    with open("feedback.json", "a") as f:
        f.write(json.dumps(entry) + "\n")

# === Gradio Functions ===
def ask_question(question):
    answer, context = router(question)
    return answer, question, answer

def submit_feedback(question, model_answer, correct_answer):
    store_feedback(question, model_answer, correct_answer)
    return "✅ Feedback received. Thank you!"

# === Gradio UI ===
with gr.Blocks() as demo:
    gr.Markdown("## 🧮 Math Question Answering with Retrieval + Feedback")

    with gr.Row():
        question_input = gr.Textbox(label="Enter your math question", lines=2)
        submit_btn = gr.Button("Get Answer")

    answer_output = gr.Markdown(label="Answer")
    hidden_q = gr.Textbox(visible=False)
    hidden_a = gr.Textbox(visible=False)

    submit_btn.click(fn=ask_question, inputs=[question_input], outputs=[answer_output, hidden_q, hidden_a])

    gr.Markdown("### 📝 Submit Feedback")
    fb_correct = gr.Textbox(label="Correct Answer (optional)")
    fb_submit = gr.Button("Submit Feedback")
    fb_status = gr.Textbox(label="Status", interactive=False)

    fb_submit.click(
        fn=submit_feedback,
        inputs=[hidden_q, hidden_a, fb_correct],
        outputs=[fb_status]
    )

demo.launch(share=True)