Update app.py

app.py

@@ -1,113 +1,3 @@
-# import os
-# import json
-# import re
-# import torch
-# import gradio as gr
-# import google.generativeai as genai
-# from sentence_transformers import SentenceTransformer, util
-
-# # ============================================================
-# # CONFIG
-# # ============================================================
-# GEMINI_API_KEY = "AIzaSyBrbLGXkSdXReb0lUucYqcNCNBkvS-RBFw" 
-# genai.configure(api_key=GEMINI_API_KEY)
-
-# # UPDATED: Use a supported 2026 model
-# MODEL = genai.GenerativeModel("gemini-2.5-flash") 
-
-# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-# EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
-# SIM_THRESHOLD = 0.55
-
-# print("Loading embedding model...")
-# embedder = SentenceTransformer(EMBED_MODEL, device=DEVICE)
-# print("✅ Ready")
-
-# # ============================================================
-# # LOGIC
-# # ============================================================
-# def get_evaluation_data(kb, question):
-#     """Gets both intent and rubric in one single API request."""
-#     prompt = f"""
-#     Acting as an examiner, analyze the Knowledge Base (KB) and Question.
-#     1. Determine the intent (FACTUAL, EXPLANATORY, PROCESS, or COMPARISON).
-#     2. Create a rubric of 3-5 atomic grading criteria based ONLY on the KB.
-    
-#     KB: {kb}
-#     Question: {question}
-
-#     OUTPUT JSON ONLY:
-#     {{
-#       "intent": "LABEL",
-#       "criteria": ["criterion 1", "criterion 2"]
-#     }}
-#     """
-#     try:
-#         response = MODEL.generate_content(prompt)
-#         # Handle cases where model might wrap JSON in backticks
-#         clean_text = re.sub(r'```json|```', '', response.text).strip()
-#         return json.loads(clean_text)
-#     except Exception as e:
-#         print(f"API Error: {e}")
-#         return {"intent": "ERROR", "criteria": []}
-
-# def evaluate(answer, question, kb):
-#     # API Call
-#     data = get_evaluation_data(kb, question)
-#     rubric = data.get("criteria", [])
-    
-#     if not rubric:
-#         return {"error": "Could not generate rubric. Check API status."}
-
-#     # Semantic Matching (Local)
-#     sents = [s.strip() for s in re.split(r'(?<=[.!?])\s+', answer) if len(s.strip()) > 5]
-#     if not sents:
-#         return {"error": "Answer is too short to evaluate."}
-
-#     ans_emb = embedder.encode(sents, convert_to_tensor=True)
-#     results = []
-#     for crit in rubric:
-#         crit_emb = embedder.encode(crit, convert_to_tensor=True)
-#         sims = util.cos_sim(crit_emb, ans_emb)[0]
-#         score = float(torch.max(sims)) if sims.numel() else 0.0
-#         results.append({"criterion": crit, "satisfied": score >= SIM_THRESHOLD})
-
-#     # Verdict
-#     hits = sum(r["satisfied"] for r in results)
-#     verdict = "✅ CORRECT" if hits == len(results) else "⚠️ PARTIAL" if hits > 0 else "❌ INCORRECT"
-
-#     return {
-#         "intent": data.get("intent"),
-#         "rubric_results": results,
-#         "final_verdict": verdict
-#     }
-
-# # UI
-# with gr.Blocks() as demo:
-#     gr.Markdown("## 🧠 Gemini 2.5 Answer Grader")
-#     kb_input = gr.Textbox(label="Knowledge Base", lines=5)
-#     q_input = gr.Textbox(label="Question")
-#     a_input = gr.Textbox(label="Student Answer", lines=4)
-#     out = gr.JSON(label="Evaluation Result")
-#     gr.Button("Evaluate").click(evaluate, [a_input, q_input, kb_input], out)
-
-# demo.launch()
-
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 import os
@@ -156,6 +46,12 @@ def get_advanced_evaluation(kb, question, answer):
     Question: {question}
     Student Answer: {answer}
 
+
+    but if Knowledge Base is empty then literally just evaluate the answer with respect the question and apply the same ideas we discussed before by generating a KB with respect the queston yourself.
+
+
+
+
     STRICT JSON OUTPUT ONLY:
     {{
       "intent": "...",
@@ -163,7 +59,7 @@ def get_advanced_evaluation(kb, question, answer):
         {{"criterion": "...", "satisfied": true, "confidence": 95}}
       ],
       "irrelevant_snippets": ["...", "..."],
-      "contradictions": ["...", "..."],
+      "contradictions": [{{"snippet": "...", "start": <the character where that snippet starts in user's answer> , "end": <the character where that snippet ends in user's answer>, "reason": "....."}, ....}] ,
       "suggested_mark": 85,
       "feedback": "..."
     }}