Create app.py

app.py

@@ -0,0 +1,118 @@
+import gradio as gr
+import dspy
+import torch
+import requests
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+from qdrant_client import QdrantClient
+from datetime import datetime
+import json
+
+# === Load Models ===
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+embedding_model = SentenceTransformer("intfloat/e5-large")
+qa_pipeline = pipeline("text-generation", model="WizardLM/WizardMath-7B-V1.0", device_map="auto", torch_dtype=torch.float16)
+
+# === Qdrant Setup ===
+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 = "your_tavily_api_key"
+    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 dspy.Output(answer="", retrieved_context="")
+        prompt = f"Question: {question}\nContext: {context}\nAnswer:"
+        answer = qa_pipeline(prompt, max_new_tokens=512)[0]["generated_text"]
+        return dspy.Output(answer=answer, retrieved_context=context)
+
+class WebFallbackQA(dspy.Program):
+    def forward(self, question):
+        answer = web_search_tavily(question)
+        return dspy.Output(answer=answer, retrieved_context="Tavily")
+
+class MathRouter(dspy.Program):
+    def forward(self, 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)
+
+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.Tab("Ask a Math Question"):
+        with gr.Row():
+            question_input = gr.Textbox(label="Enter your math question", lines=2)
+        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()