Update app.py

app.py

@@ -1,58 +1,71 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 from PIL import Image
-import openai
-
-# Set your OpenAI API key
-openai.api_key = "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"
-
-# OpenAI model to use
-OPENAI_MODEL = "gpt-4o"  # Replace with the model you want to display
 
 # Load the image classification pipeline
 @st.cache_resource
 def load_image_classification_pipeline():
+    """
+    Load the image classification pipeline using a pretrained model.
+    """
     return pipeline("image-classification", model="Shresthadev403/food-image-classification")
 
 pipe_classification = load_image_classification_pipeline()
 
-# Function to generate ingredients using OpenAI
-def get_ingredients_openai(food_name):
+# Load Qwen tokenizer and model
+@st.cache_resource
+def load_qwen_model():
+    """
+    Load the Qwen/Qwen2.5-Coder-32B-Instruct model and tokenizer.
+    """
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct")
+    model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct", device_map="auto")
+    return tokenizer, model
+
+# Function to generate ingredients using Qwen
+def get_ingredients_qwen(food_name, tokenizer, model):
+    """
+    Generate a list of ingredients for the given food item using the Qwen model.
+    """
     prompt = f"List the main ingredients typically used to prepare {food_name}:"
-    response = openai.Completion.create(
-        engine=OPENAI_MODEL,
-        prompt=prompt,
-        max_tokens=50
-    )
-    return response['choices'][0]['text'].strip()
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=50)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
 
 # Streamlit app
 st.title("Food Image Recognition with Ingredients")
 
-# Display OpenAI model being used
+# Add the provided image as a banner
+st.image("CTP_Project/IR_IMAGE", caption="Food Recognition Model", use_column_width=True)
+
+# Sidebar for model information
 st.sidebar.title("Model Information")
-st.sidebar.write(f"**OpenAI Model Used**: {OPENAI_MODEL}")
+st.sidebar.write("**Image Classification Model**: Shresthadev403/food-image-classification")
+st.sidebar.write("**LLM for Ingredients**: Qwen2.5-Coder-32B-Instruct")
 
 # Upload image
 uploaded_file = st.file_uploader("Choose a food image...", type=["jpg", "png", "jpeg"])
 
+# Load the Qwen model and tokenizer
+tokenizer, model = load_qwen_model()
+
 if uploaded_file is not None:
     # Display the uploaded image
     image = Image.open(uploaded_file)
     st.image(image, caption="Uploaded Image", use_column_width=True)
     st.write("Classifying...")
-    
+
     # Make predictions
     predictions = pipe_classification(image)
-    
+
     # Display only the top prediction
     top_food = predictions[0]['label']
     st.header(f"Food: {top_food}")
-    
+
     # Generate and display ingredients for the top prediction
     st.subheader("Ingredients")
     try:
-        ingredients = get_ingredients_openai(top_food)
+        ingredients = get_ingredients_qwen(top_food, tokenizer, model)
         st.write(ingredients)
     except Exception as e:
-        st.write("Could not generate ingredients. Please try again later.")
+        st.error(f"Error generating ingredients: {e}")