Update app.py (#72)

- Update app.py (0479d8c9776babb9de3de6780ef38c31b5bcacb5)

app.py

@@ -142,56 +142,83 @@ questions_db = {
 
 # ----------------- JSON helpers ---------------
 
-def generate_ai_questions(topic, num_questions=5):
-    """
-    Generate AI-based questions for a given topic using Groq API.
-    Falls back to `questions_db` if API fails.
-    """
-    try:
-        GROQ_API_KEY ="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"
-        url = "https://api.groq.com/openai/v1/responses"  # Groq OpenAI-compatible endpoint
+client = Groq(api_key=st.secrets["GROQ_API_KEY"].strip())
 
-        headers = {
-            "Authorization": f"Bearer {GROQ_API_KEY}",
-            "Content-Type": "application/json"
-        }
+MODEL_NAME = "openai/gpt-oss-20b"
 
-        prompt = f"Generate {num_questions} multiple-choice questions on the topic '{topic}'. " \
-                 f"Return each question in this format:\nQuestion|Option1,Option2,Option3,Option4|Answer"
-
-        payload = {
-            "model": "openai/gpt-oss-20b",
-            "input": prompt
-        }
-
-        response = requests.post(url, headers=headers, json=payload, timeout=20)
-        response.raise_for_status()
-        data = response.json()
+def generate_ai_questions(topic, num_questions=5, gid=None, questions_db=None):
+    """
+    Generate MCQs using Groq AI for a topic.
+    Fallback to questions_db if AI fails or no data returned.
+    Returns list of dicts: {"question":..., "options": [...], "answer":...}
+    """
 
-        raw_text = data.get("output_text") or data.get("result") or ""
-        questions = []
+    # Ensure fallback exists
+    if questions_db is None:
+        questions_db = {}
+
+    prompt = f"""
+    Generate {num_questions} high-quality MCQ questions for the topic "{topic}".
+
+    STRICT FORMAT:
+    [
+      {{
+        "topic": "{topic}",
+        "question": "Question text",
+        "options": ["Option 1", "Option 2", "Option 3", "Option 4"],
+        "answer": "Correct option text"
+      }}
+    ]
 
-        for line in raw_text.strip().split("\n"):
-            parts = line.strip().split("|")
-            if len(parts) == 3:
-                q_text = parts[0].strip()
-                opts = [o.strip() for o in parts[1].split(",")]
-                ans = parts[2].strip()
-                questions.append({"question": q_text, "options": opts, "answer": ans})
-            if len(questions) >= num_questions:
-                break
+    RULES:
+    - ONLY valid JSON array.
+    - No extra text.
+    - No markdown.
+    - No explanations.
+    """
 
-        if not questions:
-            st.warning(f"No AI questions generated for topic '{topic}'. Using static questions if any.")
-            return questions_db.get(topic.lower(), questions_db.get("default", []))[:num_questions]
+    try:
+        st.info(f"Generating AI questions for topic '{topic}'...")
+        response = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.2,
+            max_tokens=800,
+        )
 
-        return questions
+        raw = response.choices[0].message.content.strip()
+
+        # Remove code blocks if AI adds them
+        if raw.startswith("```"):
+            raw = raw.split("```")[1].replace("json", "").strip()
+
+        data = json.loads(raw)
+
+        # Optional: save Excel if gid provided
+        if gid:
+            os.makedirs("ai_questions_excel", exist_ok=True)
+            rows = []
+            for q in data:
+                rows.append({
+                    "topic": q.get("topic", topic),
+                    "question": q.get("question", ""),
+                    "option_1": q.get("options", [""]*4)[0],
+                    "option_2": q.get("options", [""]*4)[1],
+                    "option_3": q.get("options", [""]*4)[2],
+                    "option_4": q.get("options", [""]*4)[3],
+                    "answer": q.get("answer", "")
+                })
+            df = pd.DataFrame(rows)
+            df.to_excel(f"ai_questions_excel/{gid}_questions.xlsx", index=False)
+
+        return data[:num_questions]
 
     except Exception as e:
-        st.error(f"Groq API request failed: {e}")
-        st.warning(f"No AI questions generated for topic '{topic}'. Using static questions if any.")
-        return questions_db.get(topic.lower(), questions_db.get("default", []))[:num_questions]
-
+        st.warning(f"AI generation failed for topic '{topic}': {e}")
+        st.info("Using fallback questions from questions_db if available.")
+        # Fallback to static questions_db
+        fallback = questions_db.get(topic.lower(), questions_db.get("default", []))
+        return fallback[:num_questions]
 
 
 def load_json(path, default):