Update app.py

app.py

@@ -610,7 +610,6 @@ if uploaded_file:
         processing_time = round((time.time() - start_time) / 60, 2)
         st.info(f"⏱️ Response generated in **{processing_time} minutes**.")
 
-        st.session_state.generated_summary = rag_summary
         st.session_state.last_uploaded_hash = file_hash
         st.session_state.processed = True
         st.session_state.last_prompt_hash = None
@@ -703,43 +702,6 @@ if prompt:
 
 ######################################################################################################################
 
-# 📚 Imports
-import evaluate
-from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
-
-# 📌 Load Evaluators Once
-@st.cache_resource
-def load_evaluators():
-    rouge = evaluate.load("rouge")
-    bertscore = evaluate.load("bertscore")
-    return rouge, bertscore
-
-rouge, bertscore = load_evaluators()
-
-# 📌 Define Evaluation Functions
-def evaluate_summary(generated_summary, ground_truth_summary):
-    """Evaluate ROUGE and BERTScore."""
-    rouge_result = rouge.compute(predictions=[generated_summary], references=[ground_truth_summary])
-    bert_result = bertscore.compute(predictions=[generated_summary], references=[ground_truth_summary], lang="en")
-    return rouge_result, bert_result
-
-def compute_bleu(prediction, ground_truth):
-    reference = [ground_truth.strip().split()]
-    candidate = prediction.strip().split()
-    smoothie = SmoothingFunction().method4
-    return sentence_bleu(reference, candidate, smoothing_function=smoothie)
-
-def evaluate_metrics(prediction, ground_truth):
-    """Only compute ROUGE, BLEU, and BERTScore."""
-    rouge_result, bert_result = evaluate_summary(prediction, ground_truth)
-    bleu_score = compute_bleu(prediction, ground_truth)
-    return {
-        "ROUGE": rouge_result,
-        "BERTScore": bert_result,
-        "BLEU Score": bleu_score
-    }
-
-
 
 # Run this along with streamlit run app.py to evaluate the model's performance on a test set
 # Otherwise, comment the below code