Update app.py

app.py

@@ -16,10 +16,6 @@ classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnl
 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")
@@ -29,20 +25,16 @@ collection_name = "math_problems"
 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",
@@ -59,36 +51,25 @@ class MathAnswer(dspy.Signature):
 # === 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="")
+            return {"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="")
+            return {"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 ===
@@ -100,19 +81,14 @@ def store_feedback(question, answer, feedback, correct_answer):
         "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"]
 
-
 def submit_feedback(question, model_answer, feedback, correct_answer):
     store_feedback(question, model_answer, feedback, correct_answer)
     return "✅ Feedback received. Thank you!"
@@ -120,29 +96,27 @@ def submit_feedback(question, model_answer, feedback, correct_answer):
 # === 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)
     
-    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])
+    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)
+