prompt model change

app.py

@@ -1,5 +1,4 @@
 import asyncio
-# Prevent shutdown-time event loop errors
 try:
     asyncio.get_event_loop()
 except RuntimeError:
@@ -39,37 +38,30 @@ def classify_image(image):
 tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
 chat_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
 
-
 def explain_recycling(class_label):
     prompt = f"""
-    You are a waste-management specialist.
-    The item was classified as: **{class_label}**.
-
-    Write a detailed explanation with the following sections:
-
-    1. Recycling type (organic, inorganic, recyclable, hazardous, compostable, etc.)
-    2. Why this item belongs to this category
-    3. Correct and safe disposal method
-    4. How it should be processed or recycled at facilities
-    5. Environmental impact if handled incorrectly
-    6. Advice on reducing, reusing, or sustainable alternatives
-    
-    Write in clear, friendly paragraphs (4–7 paragraphs).
+    The waste item is: {class_label}.
+
+    Provide the answer in EXACTLY this format:
+
+    • Recycling type: <short answer>\n
+    • Disposal method: <short answer>\n
+    • Processing: <short answer>\n
+    • Tip: <short answer>\n
+
+    Use real line breaks. Keep each answer short.
     """
 
     inputs = tokenizer(prompt, return_tensors="pt").input_ids
 
     outputs = chat_model.generate(
         inputs,
-        max_length=420,
-        do_sample=True,
-        temperature=0.7,
-        top_p=0.9
+        max_length=180,
+        do_sample=False
     )
 
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-
 # ------------------ PIPELINE ------------------
 def full_pipeline(image):
     predictions = classify_image(image)
@@ -101,7 +93,6 @@ with gr.Blocks() as demo:
         outputs=[cls_output, explain_output]
     )
 
-# Theme and CSS moved to launch()
 demo.launch(
     theme=gr.themes.Soft(primary_hue="green"),
     css="#explainbox {height: 330px; font-size: 15px;}"