Update app.py

app.py

@@ -1,156 +1,98 @@
-import gradio as gr
+import os
+import json
 import random
+import subprocess
+import gradio as gr
 
-# -------------------------------
-# Demo Exam Data
-# -------------------------------
-QUESTIONS = {
-    "Bahasa Melayu Paper 1": [
-        {"text": "Tulis sebuah karangan tentang kepentingan membaca.", "answer": None},
-        {"text": "Huraikan peranan teknologi dalam kehidupan seharian.", "answer": None},
-    ],
-    "Bahasa Melayu Paper 2": [
-        {"text": "Apakah kata dasar bagi perkataan 'berjalan'?",
-         "choices": ["A. Ber", "B. Jalan", "C. An", "D. Jal"], "answer": "B"},
-        {"text": "Peribahasa 'harapkan pegar, pegar makan padi' bermaksud?",
-         "choices": ["A. Orang rajin", "B. Pengkhianatan oleh penjaga", "C. Hidup susah", "D. Orang baik hati"], "answer": "B"},
-    ],
-    "English Paper 1": [
-        {"text": "Write an essay about the importance of sports.", "answer": None},
-        {"text": "Describe a memorable day at school.", "answer": None},
-    ],
-    "English Paper 2": [
-        {"text": "Choose the correct synonym for 'happy'.",
-         "choices": ["A. Sad", "B. Joyful", "C. Angry", "D. Tired"], "answer": "B"},
-        {"text": "What is the plural of 'child'?",
-         "choices": ["A. Childs", "B. Childes", "C. Children", "D. Childer"], "answer": "C"},
-    ],
-}
-
-# -------------------------------
-# Load Exam Function
-# -------------------------------
-def load_exam(subject):
-    questions = QUESTIONS.get(subject, [])
-    outputs = []
-
-    for i, q in enumerate(questions, 1):
-        # Display question text
-        outputs.append(f"**Q{i}:** {q['text']}")
-
-        # Subjective Paper → Textbox
-        if "choices" not in q:
-            outputs.append("")  # empty string = textbox placeholder
-
-        # Objective Paper → Radio buttons
-        else:
-            outputs.append({"choices": q["choices"], "value": None})
-
-    return outputs, questions
-
-
-# -------------------------------
-# Submit Exam Function
-# -------------------------------
-def submit_exam(answers, questions):
-    score = 0
-    results = []
-
-    for ans, q in zip(answers, questions):
-        if "choices" in q:  # objective paper
-            correct = q["answer"]
-            if ans == correct:
-                score += 1
-                results.append(f"✅ {q['text']} → Your answer: {ans} (Correct)")
-            else:
-                results.append(f"❌ {q['text']} → Your answer: {ans}, Correct answer: {correct}")
-        else:  # subjective paper
-            results.append(f"📝 {q['text']} → Your response recorded.")
-
-    return f"Your Score: {score} / {len(questions)}", "\n\n".join(results)
-
-
-# -------------------------------
-# Build Gradio UI
-# -------------------------------
+from agents import AnalyzerAgent, CoachAgent, PredictiveAgent
+from ocr_agent import OcrAgent
+
+# Paths
+DATA_DIR = "data"
+QUESTIONS_FILE = "questions.json"
+os.makedirs(DATA_DIR, exist_ok=True)
+
+# Load merged question bank
+def load_question_bank():
+    if not os.path.exists(QUESTIONS_FILE) or os.path.getsize(QUESTIONS_FILE) == 0:
+        return []
+    try:
+        with open(QUESTIONS_FILE, "r", encoding="utf-8") as f:
+            return json.load(f)
+    except:
+        return []
+
+QUESTION_BANK = load_question_bank()
+
+# Initialize agents
+analyzer = AnalyzerAgent()
+coach = CoachAgent()
+predictor = PredictiveAgent()
+ocr_agent = OcrAgent()
+
+# Functions
+def upload_pdf(pdf_file, subject, year, paper):
+    if pdf_file is None:
+        return "⚠️ No file uploaded."
+    questions = ocr_agent.extract_questions(pdf_file, subject, year, paper)
+    subprocess.run(["python", "merge_questions.py"])
+    return f"✅ Extracted {len(questions)} questions from {subject.upper()} Paper {paper} ({year})"
+
+def run_exam(subject, num_questions, include_predicted):
+    pool = [q for q in QUESTION_BANK if subject.lower() in q["source"]]
+    if include_predicted:
+        predicted = predictor.predict(subject)
+        pool.append({"id": 9999, "text": predicted, "choices": [], "topics": [], "source": "predicted"})
+    if not pool:
+        return "⚠️ No questions available.", [], []
+    selected = random.sample(pool, min(num_questions, len(pool)))
+    q_texts = [q["text"] for q in selected]
+    q_ids = [q["id"] for q in selected]
+    return "\n\n".join(q_texts), q_ids, [q.get("choices", []) for q in selected]
+
+def submit_answers(q_ids, answers):
+    correct_answers = ["A"] * len(q_ids)  # Placeholder until scheme data ready
+    score, analysis = analyzer.analyze(answers, correct_answers)
+    return f"Score: {score}/{len(q_ids)}", "\n".join(analysis)
+
+def essay_feedback(essay_text):
+    return coach.coach(essay_text)
+
+# Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 📘 SPM Exam Simulator")
-
-    subject = gr.Dropdown(list(QUESTIONS.keys()), label="Select Subject")
-    load_btn = gr.Button("Load Exam")
-
-    question_boxes = []
-    answer_inputs = []
-
-    # Preallocate 10 slots (max 10 questions per subject for demo)
-    for i in range(10):
-        qtext = gr.Markdown(visible=False)
-        atext = gr.Textbox(visible=False, label="Your Answer")
-        aradio = gr.Radio(visible=False, label="Select Answer")
-        question_boxes.append((qtext, atext, aradio))
-        answer_inputs.append((atext, aradio))
-
-    submit_btn = gr.Button("Submit", visible=False)
-    score_output = gr.Markdown()
-    detail_output = gr.Markdown()
-    state_questions = gr.State()
-
-    def on_load(subject):
-        outputs, questions = load_exam(subject)
-
-        # Reset UI
-        updates = []
-        answers_state = []
-
-        for i, (qtext, atext, aradio) in enumerate(question_boxes):
-            if i < len(questions):
-                q = questions[i]
-                qtext.update(value=f"**Q{i+1}:** {q['text']}", visible=True)
-
-                if "choices" in q:  # objective
-                    atext.update(visible=False)
-                    aradio.update(choices=q["choices"], value=None, visible=True)
-                    answers_state.append(None)
-                else:  # subjective
-                    aradio.update(visible=False)
-                    atext.update(value="", visible=True)
-                    answers_state.append("")
-            else:
-                qtext.update(visible=False)
-                atext.update(visible=False)
-                aradio.update(visible=False)
-
-            updates.extend([qtext, atext, aradio])
-
-        return updates + [gr.Button.update(visible=True), questions]
-
-    def on_submit(*all_inputs):
-        # all_inputs includes answers + state_questions
-        *answers, questions = all_inputs
-
-        # Extract answers: pick from textbox or radio
-        processed = []
-        for (txt, rad), ans in zip(answer_inputs, answers):
-            if isinstance(ans, str):
-                processed.append(ans)
-            elif isinstance(ans, dict) and "value" in ans:
-                processed.append(ans["value"])
-            else:
-                processed.append(None)
-
-        return submit_exam(processed, questions)
-
-    load_btn.click(
-        fn=on_load,
-        inputs=[subject],
-        outputs=[comp for triple in question_boxes for comp in triple] + [submit_btn, state_questions],
-    )
-
-    submit_btn.click(
-        fn=on_submit,
-        inputs=[comp for pair in answer_inputs for comp in pair] + [state_questions],
-        outputs=[score_output, detail_output],
-    )
+    gr.Markdown("## 📝 SPM Exam Simulator (Form 5)")
+
+    with gr.Tab("Upload Past Papers"):
+        pdf_file = gr.File(label="Upload PDF", type="filepath")
+        subject = gr.Dropdown(["BM", "English", "Math", "History", "Science", "MoralStudies"], label="Subject")
+        year = gr.Number(label="Year", value=2018, precision=0)
+        paper = gr.Dropdown([1, 2], label="Paper")
+        upload_btn = gr.Button("Process PDF")
+        upload_out = gr.Textbox(label="Upload Status")
+        upload_btn.click(fn=upload_pdf, inputs=[pdf_file, subject, year, paper], outputs=upload_out)
+
+    with gr.Tab("Paper 2 Simulator"):
+        subject_sim = gr.Dropdown(["BM", "English", "Math", "History", "Science", "MoralStudies"], label="Subject")
+        num_q = gr.Slider(1, 50, step=1, value=10, label="Number of Questions")
+        include_pred = gr.Checkbox(label="Include AI-Predicted")
+        run_btn = gr.Button("Start Simulation")
+        q_out = gr.Textbox(label="Questions")
+        ids_out = gr.State()
+        choices_out = gr.State()
+        run_btn.click(fn=run_exam, inputs=[subject_sim, num_q, include_pred], outputs=[q_out, ids_out, choices_out])
+
+        ans_in = gr.Textbox(label="Your Answers (comma-separated, e.g. A,B,C)")
+        submit_btn = gr.Button("Submit")
+        score_out = gr.Textbox(label="Score")
+        analysis_out = gr.Textbox(label="Analysis")
+        submit_btn.click(fn=lambda ids, ans: submit_answers(ids, ans.split(",")),
+                         inputs=[ids_out, ans_in], outputs=[score_out, analysis_out])
+
+    with gr.Tab("Paper 1 (Essay Practice)"):
+        essay_in = gr.Textbox(label="Write your essay here", lines=10)
+        essay_btn = gr.Button("Get Feedback")
+        essay_out = gr.Textbox(label="Feedback")
+        essay_btn.click(fn=essay_feedback, inputs=essay_in, outputs=essay_out)
 
 demo.launch(server_name="0.0.0.0", server_port=7860)
 
@@ -174,5 +116,6 @@ demo.launch(server_name="0.0.0.0", server_port=7860)
 
 
 
+