nothing

app.py

@@ -7,10 +7,7 @@ from pydantic import BaseModel
 
 import random
 import torch
-from transformers import ( T5Tokenizer, T5ForConditionalGeneration,
-    AutoTokenizer, AutoModelForCausalLM,
-    pipeline
-)
+from transformers import T5Tokenizer, T5ForConditionalGeneration, pipeline
 
 # =========================
 # FASTAPI APP
@@ -43,40 +40,34 @@ def classify_image(image):
 # ------------------ LOAD CHAT MODEL
 tiny_model = "google/flan-t5-small"
 
+# tokenizer + model
 tokenizer = T5Tokenizer.from_pretrained(tiny_model)
-chat_model = T5ForConditionalGeneration.from_pretrained(
-    tiny_model,
-    device_map="cpu"
-)
+chat_model = T5ForConditionalGeneration.from_pretrained(tiny_model)
 
+# Use text2text-generation for T5-style models
 pipe = pipeline(
-    "text2text-generation",
+    task="text2text-generation",
     model=chat_model,
     tokenizer=tokenizer,
+    device=-1,              # -1 = CPU (safe)
     max_new_tokens=80
 )
 
 def explain_recycling(class_label):
+    # Construct a simple single-string prompt for T5
     prompt = (
         "You are an expert in waste sorting. "
-        "You ALWAYS answer using exactly two bullet points:\n"
+        "Always answer using exactly two bullet points:\n"
         "• Recycling type: <Item category>\n"
         "• Disposal: <clear, detailed correct sentence>\n"
         f"Item: {class_label}\n"
         "Return the two bullet points now."
     )
 
-    output = pipe(prompt)[0]["generated_text"]
-    return output.strip()
-
-pipe = pipeline(
-    "text-generation",
-    model=chat_model,
-    tokenizer=tokenizer,
-    device_map="auto",
-    max_new_tokens=80
-)
-
+    outputs = pipe(prompt, max_new_tokens=80, do_sample=False)
+    # outputs is a list of dicts: [{"generated_text": "..."}]
+    text = outputs[0].get("generated_text", "").strip()
+    return text
 
 
 # =========================