Update app.py

app.py

@@ -1,9 +1,12 @@
 import streamlit as st
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from transformers import pipeline
 from PIL import Image
-import requests
+import openai
 import os
 
+# Set your OpenAI API key (replace YOUR_OPENAI_API_KEY with your key)
+openai.api_key = "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"
+
 # Load the image classification pipeline
 @st.cache_resource
 def load_image_classification_pipeline():
@@ -11,33 +14,31 @@ def load_image_classification_pipeline():
 
 pipe_classification = load_image_classification_pipeline()
 
-# Load the Meta-Llama model and tokenizer for text generation
-@st.cache_resource
-def load_llama_pipeline():
-    # Retrieve Hugging Face token from environment variables
-    token = os.getenv("HF_AUTH_TOKEN")
-    tokenizer = AutoTokenizer.from_pretrained(
-        "meta-llama/Llama-3.2-3B-Instruct",
-        use_auth_token=token
-    )
-    model = AutoModelForCausalLM.from_pretrained(
-        "meta-llama/Llama-3.2-3B-Instruct",
-        use_auth_token=token
-    )
-    return pipeline("text-generation", model=model, tokenizer=tokenizer)
-
-pipe_llama = load_llama_pipeline()
-
-# Function to generate ingredients using Meta-Llama
-def get_ingredients(food_name):
+# Function to generate ingredients using OpenAI
+def get_ingredients_openai(food_name, model="text-davinci-003"):
     prompt = f"List the main ingredients typically used to prepare {food_name}:"
-    response = pipe_llama(prompt, max_length=50, num_return_sequences=1)
-    return response[0]['generated_text']
+    response = openai.Completion.create(
+        engine=model,  # Specify the model here
+        prompt=prompt,
+        max_tokens=50
+    )
+    return response['choices'][0]['text'].strip()
 
 # Streamlit app
-st.title("Food Image Classification with Ingredients Generation")
+st.title("Food Image Recognition Model")
 st.write("Upload an image to classify the type of food and get its ingredients!")
 
+# Display a sample image showing the concept of image recognition
+st.image("https://upload.wikimedia.org/wikipedia/commons/6/69/Classification_example_image.png", 
+         caption="Example of an Image Recognition Model", use_column_width=True)
+
+# Select OpenAI model
+st.sidebar.title("Choose a Model")
+model_choice = st.sidebar.selectbox(
+    "Select an OpenAI Model:",
+    ["text-davinci-003", "gpt-3.5-turbo", "gpt-4", "curie"]
+)
+
 # Upload image
 uploaded_file = st.file_uploader("Choose a food image...", type=["jpg", "png", "jpeg"])
 
@@ -52,14 +53,12 @@ if uploaded_file is not None:
     
     # Display only the top prediction
     top_food = predictions[0]['label']
-    confidence = predictions[0]['score']
-    st.subheader("Top Prediction")
-    st.write(f"**{top_food}** with confidence {confidence:.2f}")
+    st.header(f"Food: {top_food}")
     
     # Generate and display ingredients for the top prediction
     st.subheader("Ingredients")
     try:
-        ingredients = get_ingredients(top_food)
+        ingredients = get_ingredients_openai(top_food, model=model_choice)
         st.write(ingredients)
     except Exception as e:
         st.write("Could not generate ingredients. Please try again later.")