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")

print("Loading text generation model...")
# Use a lighter model for testing
#qa_pipeline = pipeline("text-generation", model="gpt2")

# === 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)
    print("Classifier result:", result)
    return result['labels'][0] == "math" and result['scores'][0] > 0.7

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

# === Web Search ===
def web_search_tavily(query):
    print("Calling Tavily...")
    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):
        print("Inside MathRetrievalQA...")
        context_items = retrieve_from_qdrant(question)
        context = "\n".join([item["solution"] for item in context_items if "solution" in item])
        print("Context for generation:", context)
        if not context:
            return dspy.Output(answer="", retrieved_context="")
        prompt = f"Question: {question}\nContext: {context}\nAnswer:"
        print("Generating answer...")
       # answer = qa_pipeline(prompt, max_new_tokens=100)[0]["generated_text"]
        print("Generated answer:", prompt)
        return {"answer": prompt, "retrieved_context": context}

       # return dspy.Output(answer=answer, retrieved_context=context)

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


class MathRouter(dspy.Program):
    def forward(self, question):
        print("Routing question:", question)
        if not is_valid_math_question(question):
            return dspy.Output(answer="❌ Only math questions are accepted. Please rephrase.", retrieved_context="")
        result = MathRetrievalQA().forward(question)
        #return result if result.answer else WebFallbackQA().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())
    }
    print("Storing feedback:", entry)
    with open("feedback.json", "a") as f:
        f.write(json.dumps(entry) + "\n")

# === Gradio Functions ===
def ask_question(question):
    print("ask_question() called with:", question)
    result = router.forward(question)
    print("Result:", result)
    #return result.answer, question, result.answer
    #return result["answer"], question, result["answer"]
    return 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.Tab("Ask a Math Question"):
        with gr.Row():
            question_input = gr.Textbox(label="Enter your math question", lines=2)
        gr.Markdown("### 🧠 Answer:")
        answer_output = gr.Markdown()

        #answer_output = gr.Markdown(label="Answer")
        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])

    with gr.Tab("Submit Feedback"):
        gr.Markdown("### Was the answer helpful?")
        fb_question = gr.Textbox(label="Original Question")
        fb_answer = gr.Textbox(label="Model's Answer")
        fb_like = gr.Radio(["👍", "👎"], label="Your Feedback")
        fb_correct = gr.Textbox(label="Correct Answer (optional)")
        fb_submit_btn = gr.Button("Submit Feedback")
        fb_status = gr.Textbox(label="Status", interactive=False)
        fb_submit_btn.click(fn=submit_feedback,
                            inputs=[fb_question, fb_answer, fb_like, fb_correct],
                            outputs=[fb_status])

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