Update app.py

app.py

@@ -288,6 +288,176 @@ def evaluate_question_topic_match(data):
     return parse_matching_json(generated)
 
 
+# ===============================================================================
+# STUDENT FEEDBACK ANALYSER MODEL
+# ===============================================================================
+
+FEEDBACK_MODEL_PATH   = "Qwen/Qwen2-1.5B-Instruct"
+FEEDBACK_ADAPTER_PATH = "Chamith2000/Video_Feedback"
+
+VALID_ISSUES     = {"audio", "confusion", "difficulty", "pacing", "positive"}
+VALID_SEVERITIES = {"high", "medium", "low"}
+
+print("Loading student feedback analyser model...")
+feedback_tokenizer = AutoTokenizer.from_pretrained(
+    FEEDBACK_MODEL_PATH,
+    trust_remote_code=True,
+    padding_side="right",
+)
+if feedback_tokenizer.pad_token is None:
+    feedback_tokenizer.pad_token = feedback_tokenizer.eos_token
+
+bnb_config_feedback = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_use_double_quant=True,
+)
+
+feedback_base = AutoModelForCausalLM.from_pretrained(
+    FEEDBACK_MODEL_PATH,
+    quantization_config=bnb_config_feedback,
+    device_map='auto',
+    trust_remote_code=True,
+    torch_dtype=torch.float16,
+)
+feedback_model = PeftModel.from_pretrained(feedback_base, FEEDBACK_ADAPTER_PATH)
+feedback_model.eval()
+print("✅ Student feedback analyser model loaded!")
+
+
+def build_feedback_prompt(text: str, grade: int, lesson: str, video_content: str) -> str:
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are an educational feedback analyser for a mathematics e-learning platform. "
+                "You analyse student feedback and return ONLY a valid JSON object — no explanation, "
+                "no markdown, no extra text. Just the raw JSON."
+            ),
+        },
+        {
+            "role": "user",
+            "content": (
+                f"Analyse the student feedback below and return a JSON object.\n\n"
+                f"Student details:\n"
+                f"  Grade        : {grade}\n"
+                f"  Lesson       : {lesson}\n"
+                f"  Video context: {video_content}\n\n"
+                f"Student feedback: \"{text}\"\n\n"
+                f"Return JSON with exactly these keys:\n"
+                f"  issue          : one of [audio, confusion, difficulty, pacing, positive]\n"
+                f"  severity       : one of [high, medium, low]\n"
+                f"  recommendation : a specific actionable instruction for the teacher\n"
+                f"  confidence     : your confidence as a float between 0.0 and 1.0\n\n"
+                f"JSON:"
+            ),
+        },
+    ]
+    return feedback_tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+
+
+def parse_feedback_json(text: str) -> dict:
+    """Parse and validate the model's JSON output for feedback analysis."""
+    clean = text.strip()
+
+    # Strip markdown code fences if present
+    if clean.startswith("```"):
+        lines = clean.split("\n")
+        clean = "\n".join(l for l in lines if not l.startswith("```"))
+
+    # Try direct parse
+    try:
+        parsed = json.loads(clean)
+    except json.JSONDecodeError:
+        # Fall back to brace extraction
+        start = clean.find('{')
+        end   = clean.rfind('}') + 1
+        if start != -1 and end > start:
+            try:
+                parsed = json.loads(clean[start:end])
+            except json.JSONDecodeError:
+                return {
+                    "error": "Failed to parse model output as JSON",
+                    "raw_output": text[:500],
+                }
+        else:
+            return {
+                "error": "No JSON object found in model output",
+                "raw_output": text[:500],
+            }
+
+    # Normalise and validate fields
+    issue     = str(parsed.get("issue", "")).lower().strip()
+    severity  = str(parsed.get("severity", "")).lower().strip()
+    rec       = parsed.get("recommendation", "")
+    raw_conf  = parsed.get("confidence", 0.0)
+
+    try:
+        confidence = float(raw_conf)
+        confidence = max(0.0, min(1.0, confidence))
+    except (TypeError, ValueError):
+        confidence = 0.0
+
+    if issue not in VALID_ISSUES:
+        issue = "confusion"   # safe default
+    if severity not in VALID_SEVERITIES:
+        severity = "medium"   # safe default
+
+    return {
+        "issue":          issue,
+        "severity":       severity,
+        "recommendation": rec,
+        "confidence":     confidence,
+    }
+
+
+@torch.no_grad()
+def infer_student_feedback(data: dict) -> dict:
+    """Run inference with the student feedback analyser model."""
+    text          = data.get("text", "").strip()
+    grade         = data.get("grade", 3)
+    lesson        = data.get("lesson", "")
+    video_content = data.get("video_content", "")
+
+    if not text:
+        return {"error": "Missing required field: 'text'"}
+    if not lesson:
+        return {"error": "Missing required field: 'lesson'"}
+    if not video_content:
+        return {"error": "Missing required field: 'video_content'"}
+
+    prompt = build_feedback_prompt(text, grade, lesson, video_content)
+
+    inputs = feedback_tokenizer(
+        prompt,
+        return_tensors="pt",
+        truncation=True,
+        max_length=512,
+    ).to(DEVICE)
+
+    outputs = feedback_model.generate(
+        **inputs,
+        max_new_tokens=150,
+        do_sample=False,
+        temperature=1.0,
+        repetition_penalty=1.1,
+        pad_token_id=feedback_tokenizer.eos_token_id,
+        eos_token_id=feedback_tokenizer.eos_token_id,
+    )
+
+    generated = feedback_tokenizer.decode(
+        outputs[0][inputs["input_ids"].shape[1]:],
+        skip_special_tokens=True,
+    ).strip()
+
+    return parse_feedback_json(generated)
+
+
 # ===============================================================================
 # FLASK APP
 # ===============================================================================
@@ -308,7 +478,10 @@ def generate():
     elif calling_type == 'analyze':
         return analyze_gen(data.get('teacher_data'))
 
-    return jsonify({"error": "Invalid model type. Use 'teacher', 'question', or 'analyze'"}), 400
+    elif calling_type == 'feedback':
+        return student_feedback(data.get('feedback_data'))
+
+    return jsonify({"error": "Invalid model type. Use 'teacher', 'question', 'analyze', or 'feedback'"}), 400
 
 
 def question_gen(data):
@@ -347,6 +520,15 @@ def teacher_guide(data):
     return jsonify({"output": prompt})
 
 
+def student_feedback(data):
+    """Analyse student video feedback and classify issue, severity, and recommendation."""
+    if not data:
+        return jsonify({"error": "Missing 'feedback_data'"}), 400
+
+    result = infer_student_feedback(data)
+    return jsonify({"output": result})
+
+
 if __name__ == "__main__":
     print("\n" + "="*70)
     print("SERVER READY!")
@@ -356,6 +538,7 @@ if __name__ == "__main__":
     print("    - model_type: 'question'  → math question generation")
     print("    - model_type: 'analyze'   → question-topic match scoring")
     print("    - model_type: 'teacher'   → teaching feedback analysis")
+    print("    - model_type: 'feedback'  → student video feedback analysis")
     print("="*70 + "\n")
 
     app.run(host="0.0.0.0", port=7860)