Update app.py

app.py

@@ -1,41 +1,17 @@
 import streamlit as st
-import openai
-import os
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-# Set up the OpenAI API key from Hugging Face Secrets
-openai.api_key = os.getenv("OPENAI_API_KEY")
+# Load the model and tokenizer
+model_name = "flax-community/t5-recipe-generation"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+# Streamlit app for recipe generation
+st.title("AI-Powered Recipe Generator")
+prompt = st.text_input("Enter ingredients or a recipe title:")
 
-# Define a function to generate text using GPT-3.5-Turbo
-def generate_response(prompt):
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[{"role": "user", "content": prompt}],
-        max_tokens=150,
-        temperature=0.7
-    )
-    message = response['choices'][0]['message']['content']
-    return message
-
-# Streamlit app
-def main():
-    st.title("GPT-3.5 Turbo Text Generator")
-    st.write("Enter a prompt, and GPT-3.5 Turbo will generate a response.")
-
-    # Text input for user prompt
-    user_prompt = st.text_area("Enter your prompt:", "")
-
-    if st.button("Generate Response"):
-        if user_prompt:
-            # Get the response from GPT-3.5 Turbo
-            with st.spinner("Generating response..."):
-                response = generate_response(user_prompt)
-            
-            # Display the response
-            st.write("### GPT-3.5 Turbo Response")
-            st.write(response)
-        else:
-            st.warning("Please enter a prompt to generate a response.")
-
-if __name__ == "__main__":
-    main()
+if st.button("Generate Recipe"):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(inputs["input_ids"], max_length=150, num_return_sequences=1)
+    recipe = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    st.write(recipe)