Update app/services/recipe_generator.py

app/services/recipe_generator.py

@@ -1,53 +1,75 @@
-# app/services/recipe_generator.py
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-class RecipeGenerator:
-    def __init__(self):
-        # Initialize your fine-tuned model and tokenizer
-        self.tokenizer = AutoTokenizer.from_pretrained("flax-community/t5-recipe-generation")
-        self.model = AutoModelForCausalLM.from_pretrained("flax-community/t5-recipe-generation")
-        
-    async def generate(self, ingredients: List[str]):
-        # Format ingredients for input
-        input_text = f"Generate a recipe using these ingredients: {', '.join(ingredients)}"
-        
-        # Tokenize and generate
-        inputs = self.tokenizer(input_text, return_tensors="pt", padding=True)
-        outputs = self.model.generate(
-            inputs.input_ids,
-            max_length=512,
-            num_return_sequences=1,
-            temperature=0.7,
-            top_p=0.9,
-            do_sample=True
-        )
-        
-        # Decode and parse the generated recipe
-        generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        
-        # Parse the generated text into structured format
-        # This is a simple example - adjust based on your model's output format
-        lines = generated_text.split('\n')
-        title = lines[0]
-        ingredients_list = []
-        instructions_list = []
-        
-        # Simple parsing logic - adjust based on your model's output format
-        current_section = None
-        for line in lines[1:]:
-            if "Ingredients:" in line:
-                current_section = "ingredients"
-            elif "Instructions:" in line:
-                current_section = "instructions"
-            elif line.strip():
-                if current_section == "ingredients":
-                    ingredients_list.append(line.strip())
-                elif current_section == "instructions":
-                    instructions_list.append(line.strip())
-        
-        return {
-            "title": title,
-            "ingredients": ingredients_list,
-            "instructions": instructions_list
-        }
+# app/services/recipe_generator.py
+from typing import List, Dict
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import os
+
+class RecipeGenerator:
+    def __init__(self):
+        # Set cache directory to a writable location
+        os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface'
+        
+        # Create cache directory if it doesn't exist
+        os.makedirs('/tmp/huggingface', exist_ok=True)
+        
+        # Initialize your fine-tuned model and tokenizer
+        try:
+            self.tokenizer = AutoTokenizer.from_pretrained("flax-community/t5-recipe-generation", cache_dir='/tmp/huggingface')
+            self.model = AutoModelForCausalLM.from_pretrained("flax-community/t5-recipe-generation", cache_dir='/tmp/huggingface')
+        except Exception as e:
+            print(f"Error loading model: {str(e)}")
+            # Provide a fallback or raise the error as needed
+            raise
+
+    async def generate(self, ingredients: List[str]) -> Dict[str, List[str]]:
+        try:
+            # Format ingredients for input
+            input_text = f"Generate a recipe using these ingredients: {', '.join(ingredients)}"
+            
+            # Tokenize and generate
+            inputs = self.tokenizer(input_text, return_tensors="pt", padding=True)
+            outputs = self.model.generate(
+                inputs.input_ids,
+                max_length=512,
+                num_return_sequences=1,
+                temperature=0.7,
+                top_p=0.9,
+                do_sample=True
+            )
+            
+            # Decode and parse the generated recipe
+            generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            
+            # Parse the generated text into structured format
+            lines = generated_text.split('\n')
+            title = lines[0] if lines else "Generated Recipe"
+            
+            # Initialize lists
+            ingredients_list = []
+            instructions_list = []
+            
+            # Simple parsing logic
+            current_section = None
+            for line in lines[1:]:
+                if "Ingredients:" in line:
+                    current_section = "ingredients"
+                elif "Instructions:" in line:
+                    current_section = "instructions"
+                elif line.strip():
+                    if current_section == "ingredients":
+                        ingredients_list.append(line.strip())
+                    elif current_section == "instructions":
+                        instructions_list.append(line.strip())
+            
+            return {
+                "title": title,
+                "ingredients": ingredients_list or ["No ingredients generated"],
+                "instructions": instructions_list or ["No instructions generated"]
+            }
+        except Exception as e:
+            print(f"Error generating recipe: {str(e)}")
+            return {
+                "title": "Error Generating Recipe",
+                "ingredients": ["Error occurred while generating recipe"],
+                "instructions": ["Please try again later"]
+            }