Update app.py

app.py

@@ -5,19 +5,26 @@ import gradio as gr
 
 from transformers import pipeline
 
-pipeline = pipeline("text-generation", model="nitinbhayana/Llama-2-7b-chat-hf-review-phrases-sentiments-v2")
+#pipeline = pipeline("text-generation", model="nitinbhayana/Llama-2-7b-chat-hf-review-phrases-sentiments-v2")
 
-def predict(review):
+
+pipeline = pipeline("text-generation", model="nitinbhayana/TinyLlama-1.1B-Chat-v1.0-title-suggestion-v1.0")
+
+def predict(title):
     
-    prompt=f"""[INST] <<SYS>>
-You are a helpful assistant that provides accurate and concise responses. Do not hallucinate.
+    prompt=f"""<s>[INST] <<SYS>>
+You are a helpful, respectful, and honest assistant for product feature mapping.
 <</SYS>>
-Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-### Instruction:
-Analyze the following printer product review on amazon to extract any relevant phrases from the review that are related to each of the specified topics: ['connectivity', 'customer_support', 'delivery_and_packaging', 'durability', 'features', 'ink_toner_cost_efficiency', 'noise', 'print_quality', 'setup_and_installation', 'speed', 'user_interface', 'value_for_money', 'portability', 'easy_to_use', 'mobile_printing_functionality','authenticity','product_quality'], and indicate the sentiment expressed in each phrase.The sentiment evaluations should range from 0 to 5, with 5 represents extremely positive sentiment, 4 indicates very positive sentiment, 3 suggests neutral sentiment, 2 reflects somewhat negative sentiment, 1 represents highly negative sentiment, and 0 means not mentioned. Provide the output in JSON format.
-### Input:
-{review}
-[/INST]
+
+[INST]
+For a given e-commerce product title.
+[TITLE]
+Pantene Hair Science Silky Smoth Shampo with Pro-Vitamins & Vitamin E, shampoo for woman & men, shampoo for frizzy and dry hairs, 650ml
+[/TITLE]
+Create a new, easy-to-read, error-free title.
+[/INST] 
+
+Suggested Title:
 """
     predictions = pipeline(prompt)
     return (predictions)
@@ -26,5 +33,5 @@ gr.Interface(
     predict,
     inputs='text',
     outputs='text',
-    title="Printer review categorization with sentiments",
+    title="Title Suggestion",
 ).launch()