Update app.py

app.py

@@ -1,22 +1,50 @@
 
 
 import gradio as gr
-
 from transformers import pipeline
-
-pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
-
-def predict(text):
-  return pipe(text)[0]["translation_text"]
-
-demo = gr.Interface(
-  fn=predict,
-  inputs='text',
-  outputs='text',
+import torch
+
+# Initialize the text generation pipeline
+generator = pipeline('text-generation', model='gpt2')
+
+def generate_recipe(ingredient):
+    """
+    Generate a recipe based on the input ingredient/word
+    """
+    # Create a prompt template
+    prompt = f"Recipe for {ingredient}:\n\nIngredients:\n"
+    
+    # Generate the recipe
+    try:
+        result = generator(
+            prompt,
+            max_length=300,
+            num_return_sequences=1,
+            temperature=0.7,
+            pad_token_id=generator.tokenizer.eos_token_id
+        )
+        
+        # Extract and format the generated recipe
+        recipe = result[0]['generated_text']
+        
+        # Clean up the output
+        recipe = recipe.replace(prompt, '')
+        
+        return recipe
+    
+    except Exception as e:
+        return f"Error generating recipe: {str(e)}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_recipe,
+    inputs=gr.Textbox(lines=1, placeholder="Enter an ingredient or dish name..."),
+    outputs=gr.Textbox(lines=10, label="Generated Recipe"),
+    title="Recipe Generator",
+    description="Enter an ingredient or dish name, and I'll generate a recipe for you!",
+    examples=[["chocolate cake"], ["chicken curry"], ["pasta"]]
 )
 
-demo.launch()
-
-
 # Launch the interface
-#iface.launch()
+if __name__ == "__main__":
+    iface.launch()