Update app.py

app.py

@@ -1,6 +1,6 @@
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import T5Tokenizer, T5ForConditionalGeneration  # Changed to specific classes
 import logging
 import os
 
@@ -28,26 +28,52 @@ async def predict(request: PredictionRequest):
         logger.info(f"Loading model: {request.model}")
         model_path = MODELS[request.model]
         
-        tokenizer = AutoTokenizer.from_pretrained(model_path, token=HF_TOKEN)
-        model = AutoModelForSeq2SeqLM.from_pretrained(model_path, token=HF_TOKEN)
+        # Add debug logging
+        logger.info("Attempting to load tokenizer...")
+        tokenizer = T5Tokenizer.from_pretrained(
+            model_path,
+            token=HF_TOKEN,
+            local_files_only=False,  # Force download if needed
+            return_special_tokens_mask=True
+        )
+        logger.info("Tokenizer loaded successfully")
+        
+        logger.info("Attempting to load model...")
+        model = T5ForConditionalGeneration.from_pretrained(
+            model_path,
+            token=HF_TOKEN,
+            local_files_only=False  # Force download if needed
+        )
+        logger.info("Model loaded successfully")
         
         full_input = "Interpret this dream: " + request.inputs
         logger.info(f"Processing input: {full_input}")
         
+        logger.info("Tokenizing input...")
         inputs = tokenizer(
             full_input,
             return_tensors="pt",
             truncation=True,
-            max_length=512
+            max_length=512,
+            padding=True
         )
+        logger.info("Input tokenized successfully")
         
+        logger.info("Generating output...")
         outputs = model.generate(**inputs, max_length=200)
+        logger.info("Output generated successfully")
+        
         result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        logger.info(f"Final result: {result}")
         
         return PredictionResponse(generated_text=result)
         
     except Exception as e:
         logger.error(f"Error: {str(e)}")
+        logger.error(f"Error type: {type(e)}")
+        # Log the full traceback
+        import traceback
+        logger.error(f"Traceback: {traceback.format_exc()}")
         raise HTTPException(status_code=500, detail=str(e))
 
 @app.get("/health")