app.py

import gradio as gr
import torch
import requests
from transformers import pipeline
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
from datetime import datetime
import dspy
import json

# === 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")

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

# === Web Search ===
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.")

# === DSPy Signature ===
class MathAnswer(dspy.Signature):
    question = dspy.InputField()
    retrieved_context = dspy.InputField()
    answer = dspy.OutputField()

# === DSPy Programs ===
class MathRetrievalQA(dspy.Program):
    def forward(self, question):
        context_items = retrieve_from_qdrant(question)
        context = "\n".join([item["solution"] for item in context_items if "solution" in item])
        if not context:
            return {"answer": "", "retrieved_context": ""}
        prompt = f"Question: {question}\nContext: {context}\nAnswer:"
        return {"answer": prompt, "retrieved_context": context}

class WebFallbackQA(dspy.Program):
    def forward(self, question):
        answer = web_search_tavily(question)
        return {"answer": answer, "retrieved_context": "Tavily"}

class MathRouter(dspy.Program):
    def forward(self, question):
        if not is_valid_math_question(question):
            return {"answer": "❌ Only math questions are accepted. Please rephrase.", "retrieved_context": ""}
        result = MathRetrievalQA().forward(question)
        return result if result["answer"] else WebFallbackQA().forward(question)

router = MathRouter()

# === Feedback Storage ===
def store_feedback(question, answer, feedback, correct_answer):
    entry = {
        "question": question,
        "model_answer": answer,
        "feedback": feedback,
        "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):
    result = router.forward(question)
    return result["answer"], question, result["answer"]

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

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

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

    answer_output = gr.Markdown()
    hidden_q = gr.Textbox(visible=False)
    hidden_a = gr.Textbox(visible=False)
    
    submit_btn = gr.Button("Get Answer")
    submit_btn.click(fn=ask_question, inputs=[question_input], outputs=[answer_output, hidden_q, hidden_a])

    # Feedback section on same page
    gr.Markdown("### 💬 Give Feedback")
    fb_correct = gr.Textbox(label="Correct Answer (optional)")
    fb_like = gr.Radio(["👍", "👎"], label="Was the answer helpful?")
    fb_submit_btn = gr.Button("Submit Feedback")
    fb_status = gr.Markdown()

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

demo.launch(share=True, debug=True)