Update app.py

app.py

@@ -1,37 +1,37 @@
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-import streamlit as st
-
-tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
-model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
-
-def llm_response(prompt):
-    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
-    outputs = model.generate(input_ids, max_length=300, do_sample=True, temperature=0.1)
-    return tokenizer.decode(outputs[0])[6:-4]
-
-def predict_review_sentiment(review):
-    sys_prompt = """
-        Categorize the sentiment of the customer review as positive, negative, or neutral.
-        Leverage your expertise in the aviation industry and deep understanding of industry trends to analyze the nuanced expressions and overall tone.
-        It is crucial to accurately identify neutral sentiments, which may indicate a balanced view or neutral stance towards Us Airways. Neutral expressions could involve factual statements without explicit positive or negative opinions.
-        Consider the importance of these neutral sentiments in gauging the public sentiment towards the airline company.
-        For instance, a positive sentiment might convey satisfaction with the airline's services, a negative sentiment could express dissatisfaction, while neutral sentiment may reflect an impartial observation or a neutral standpoint
-    """
-    pred_sent = llm_response(
-        """
-            {}
-            Review text: '{}'
-        """.format(sys_prompt, review)
-    )
-    return pred_sent
-
-st.title("Airline Review Sentiment Classifier")
-
-review = st.text_area("Paste a review:")
-
-if st.button("Analyse Sentiment"):
-    if review.strip():
-        result = predict_review_sentiment(review)
-        st.success(f"Predicted Sentiment: {result}")
-    else:
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import streamlit as st
+
+tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
+model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
+
+def llm_response(prompt):
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+    outputs = model.generate(input_ids, max_length=300, do_sample=True, temperature=0.1)
+    return tokenizer.decode(outputs[0])[6:-4]
+
+def predict_review_sentiment(review):
+    sys_prompt = """
+        Categorize the sentiment of the customer review as positive, negative, or neutral.
+        Leverage your expertise in the aviation industry and deep understanding of industry trends to analyze the nuanced expressions and overall tone.
+        It is crucial to accurately identify neutral sentiments, which may indicate a balanced view or neutral stance towards Us Airways. Neutral expressions could involve factual statements without explicit positive or negative opinions.
+        Consider the importance of these neutral sentiments in gauging the public sentiment towards the airline company.
+        For instance, a positive sentiment might convey satisfaction with the airline's services, a negative sentiment could express dissatisfaction, while neutral sentiment may reflect an impartial observation or a neutral standpoint
+    """
+    pred_sent = llm_response(
+        """
+            {}
+            Review text: '{}'
+        """.format(sys_prompt, review)
+    )
+    return pred_sent
+
+st.title("Airline Review Sentiment Classifier")
+
+review = st.text_area("Paste a review:")
+
+if st.button("Analyse Sentiment"):
+    if review.strip():
+        result = predict_review_sentiment(review)
+        st.success(f"Predicted Sentiment: {result}")
+    else:
         st.warning("Please enter some review text.")