Update app.py

app.py

@@ -66,52 +66,22 @@ class LangChainAgentWrapper:
     def __init__(self):
         print("Initializing LangChainAgentWrapper...")
         
-        model_id = "google/gemma-2b-it"
+        # --- CHANGE 1: Switched to a smaller, CPU-friendly model ---
+        model_id = "google/flan-t5-base"
         
         try:
-            hf_auth_token = os.getenv("HF_TOKEN")
-            if not hf_auth_token:
-                raise ValueError("HF_TOKEN secret is missing. It is required for downloading models.")
-            else:
-                print("HF_TOKEN secret found.")
-
-            # --- CORRECTED MODEL LOADING ---
-
-            # 1. Create the 4-bit quantization configuration
-            print("Creating 4-bit quantization config...")
-            quantization_config = transformers.BitsAndBytesConfig(
-                load_in_4bit=True,
-                bnb_4bit_quant_type="nf4",
-                bnb_4bit_compute_dtype="bfloat16"
-            )
-            print("Quantization config created.")
-
-            # 2. Load the tokenizer
-            print(f"Loading tokenizer for: {model_id}")
-            tokenizer = transformers.AutoTokenizer.from_pretrained(model_id, token=hf_auth_token)
-            print("Tokenizer loaded successfully.")
-
-            # 3. Load the model with the quantization config
-            print(f"Loading model '{model_id}' with quantization...")
-            model = transformers.AutoModelForCausalLM.from_pretrained(
-                model_id,
-                quantization_config=quantization_config,
-                device_map="auto", # Automatically maps model to available hardware (CPU/GPU)
-                token=hf_auth_token
-            )
-            print("Model loaded successfully.")
-
-            # 4. Create the Hugging Face pipeline with the pre-loaded model and tokenizer
-            print("Creating text-generation pipeline...")
+            hf_auth_token = os.getenv("HF_TOKEN") # Good practice to keep, but not needed for FLAN-T5
+            
+            # --- CHANGE 2 & 3: Use the correct task for T5 and remove quantization ---
+            # We no longer need to load the tokenizer and model separately,
+            # as we are not applying a custom quantization config.
+            print(f"Loading model pipeline for: {model_id}")
             llm_pipeline = transformers.pipeline(
-                "text-generation",
-                model=model,
-                tokenizer=tokenizer,
-                # No need to pass quantization_config here anymore
+                "text2text-generation", # <<< IMPORTANT: Changed task for T5 models
+                model=model_id,
+                device_map="auto"
             )
-            print("Model pipeline created successfully.")
-            
-            # --- END CORRECTION ---
+            print("Model pipeline loaded successfully.")
             
             # Wrap the pipeline in a LangChain LLM object
             self.llm = HuggingFacePipeline(pipeline=llm_pipeline)