Update app.py

app.py

@@ -3,9 +3,8 @@ import gradio as gr
 from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
 
 # -----------------------------
-# 1. Load the Models
-#    a) For question generation (using T5-base QG model with slow tokenizer)
-#    b) For passage generation (using GPT-2 for dynamic passage creation)
+# 1. Load the Model & Slow Tokenizer
+#    Using the "valhalla/t5-base-qg-hl" model for improved question quality.
 # -----------------------------
 tokenizer = T5Tokenizer.from_pretrained("valhalla/t5-base-qg-hl", use_fast=False)
 model = T5ForConditionalGeneration.from_pretrained("valhalla/t5-base-qg-hl")
@@ -15,54 +14,47 @@ qg_pipeline = pipeline(
     tokenizer=tokenizer
 )
 
-# Pipeline for text generation (passage generation)
-tg_pipeline = pipeline(
-    "text-generation",
-    model="gpt2",
-    max_length=100,
-    do_sample=True
-)
-
 # -----------------------------
-# 2. Dynamically Generate a Passage Based on Difficulty
-#    The generated passage should include <hl> tokens to mark the key fact (answer).
+# 2. Define Fixed Passages by Difficulty
 # -----------------------------
-def generate_passage(difficulty):
-    if difficulty == "easy":
-        prompt = (
-            "Write a simple, short informative passage about a common fact. "
-            "Enclose one key fact with <hl> and <hl> to highlight it. "
-            "Keep it easy to understand."
-        )
-    elif difficulty == "medium":
-        prompt = (
-            "Write a medium-difficulty informative passage on a historical or scientific topic. "
-            "Enclose one key fact with <hl> and <hl> to highlight it. "
-            "The passage should be moderately challenging."
-        )
-    elif difficulty == "hard":
-        prompt = (
-            "Write a challenging, detailed informative passage on a complex topic. "
-            "Enclose one key fact with <hl> and <hl> to highlight it. "
-            "The passage should be thought-provoking and detailed."
-        )
-    else:
-        prompt = (
-            "Write an informative passage and enclose one key fact with <hl> and <hl> to highlight it."
-        )
-    
-    # Generate a passage using the text-generation pipeline.
-    generated = tg_pipeline(prompt, num_return_sequences=1)[0]["generated_text"]
-    
-    # If the generated passage does not include the <hl> tokens, insert them manually.
-    if "<hl>" not in generated:
-        words = generated.split()
-        if len(words) > 10:
-            insert_pos = len(words) // 2
-            words.insert(insert_pos, "<hl>")
-            words.insert(insert_pos + 2, "<hl>")
-            generated = " ".join(words)
-    return generated
+passages = {
+    "easy": [
+        "What is the capital of France? <hl>Paris<hl>",
+        "What type of animal is a cat? <hl>Feline<hl>",
+        "What liquid is essential for all known forms of life? <hl>Water<hl>",
+        "In which direction does the sun rise? <hl>East<hl>",
+        "What quality are dogs best known for? <hl>Loyalty<hl>",
+        "Which planet do humans live on? <hl>Earth<hl>",
+        "Which fruit is commonly red and grows on trees? <hl>Apple<hl>",
+        "What device is used to tell time? <hl>Clock<hl>",
+        "Which object in the sky shines at night? <hl>Moon<hl>",
+        "What appears in the sky after rain? <hl>Rainbow<hl>"
+    ],
+    "medium": [
+        "In which country was Albert Einstein born? <hl>Germany<hl>",
+        "What is the process by which plants convert sunlight into energy? <hl>Photosynthesis<hl>",
+        "Which explorer sailed across the Atlantic Ocean in 1492? <hl>Columbus<hl>",
+        "Which rainforest is the largest in the world? <hl>Amazon<hl>",
+        "Which ocean is the largest and deepest on Earth? <hl>Pacific<hl>",
+        "Which gas is essential for human breathing? <hl>Oxygen<hl>",
+        "Which galaxy contains our solar system? <hl>Milky<hl>",
+        "What structures in Egypt were built as tombs for pharaohs? <hl>Pyramids<hl>",
+        "Which revolution transformed manufacturing in the 18th and 19th centuries? <hl>Industrial<hl>",
+        "What system of government allows citizens to elect their leaders? <hl>Democracy<hl>"
+    ],
+    "hard": [
+        "Which historical period was a European cultural and intellectual revival? <hl>Renaissance<hl>",
+        "Which branch of physics describes nature at the smallest scales? <hl>Quantum<hl>",
+        "What document established the framework of the U.S. government? <hl>Constitution<hl>",
+        "Which empire was the continuation of the Roman Empire in the East? <hl>Byzantine<hl>",
+        "Which fundamental particle gives mass to others? <hl>Higgs<hl>",
+        "Which principle states that position and momentum cannot both be precisely known? <hl>Heisenberg<hl>",
+        "What was the period of political tension between the U.S. and the Soviet Union called? <hl>Cold<hl>",
+        "Which collection of essays supported the ratification of the U.S. Constitution? <hl>Federalist<hl>",
+        "Which theoretical framework aims to unify quantum mechanics and relativity? <hl>String<hl>",
+        "Which field of computer science focuses on creating intelligent machines? <hl>Artificial<hl>"
+    ]
+}
 
 # -----------------------------
 # 3. Session State Initialization
@@ -84,34 +76,35 @@ def adjust_difficulty(state):
     idx = diff_order.index(state["difficulty"])
     if state["score"] >= 2 and idx < len(diff_order) - 1:
         state["difficulty"] = diff_order[idx + 1]
-        state["score"] = 0  # reset score when leveling up
+        state["score"] = 0  # Reset score when leveling up
         return "Level up! Difficulty increased to: " + state["difficulty"]
     elif state["score"] <= -2 and idx > 0:
         state["difficulty"] = diff_order[idx - 1]
-        state["score"] = 0  # reset score when leveling down
+        state["score"] = 0  # Reset score when leveling down
         return "Don't worry! Difficulty decreased to: " + state["difficulty"]
     else:
         return f"Keep going! Current level: {state['difficulty']} (Score: {state['score']})"
 
 # -----------------------------
-# 5. Generate a Question from a Dynamically Generated Passage
+# 5. Generate a Question from a Fixed Passage
 # -----------------------------
 def generate_question(state):
-    # Dynamically generate a passage based on current difficulty.
-    passage = generate_passage(state["difficulty"])
-    state["passage"] = passage
+    # Select a random passage from the fixed list based on the current difficulty.
+    passage = random.choice(passages[state["difficulty"]])
     
-    # Extract the answer from the passage (text between <hl> tokens).
+    # Extract the answer from text between <hl> tokens.
     parts = passage.split("<hl>")
     if len(parts) >= 3:
         answer = parts[1].strip()
     else:
         answer = "N/A"
     
-    # Use the question generation pipeline on the generated passage.
+    # Use the QG pipeline to generate a question from the fixed passage.
     result = qg_pipeline(passage, max_length=64)
     question_text = result[0]["generated_text"]
     
+    # Update state with the chosen passage, generated question, and correct answer.
+    state["passage"] = passage
     state["question"] = question_text
     state["answer"] = answer
     return question_text