Update app.py

app.py

@@ -1,8 +1,15 @@
 import streamlit as st
 from transformers import pipeline
 from PIL import Image
+from huggingface_hub import InferenceClient
 import os
 
+# Hugging Face API key
+API_KEY = st.secrets["HF_API_KEY"]
+
+# Initialize the Hugging Face Inference Client
+client = InferenceClient(api_key=API_KEY)
+
 # Load the image classification pipeline
 @st.cache_resource
 def load_image_classification_pipeline():
@@ -13,43 +20,30 @@ def load_image_classification_pipeline():
 
 pipe_classification = load_image_classification_pipeline()
 
-# Load the BLOOM model for ingredient generation
-@st.cache_resource
-def load_bloom_pipeline():
-    """
-    Load the BLOOM model for ingredient generation.
-    """
-    return pipeline("text-generation", model="bigscience/bloom-1b7")
-
-pipe_bloom = load_bloom_pipeline()
-
-def get_ingredients_bloom(food_name):
+# Function to generate ingredients using Hugging Face Inference Client
+def get_ingredients_qwen(food_name):
     """
-    Generate a list of ingredients for the given food item using BLOOM.
+    Generate a list of ingredients for the given food item using Qwen NLP model.
     Returns a clean, comma-separated list of ingredients.
     """
-    prompt = (
-        f"Generate a list of the main ingredients used to prepare {food_name}. "
-        "Respond only with a concise, comma-separated list of ingredients, without any additional text, explanations, or placeholders. "
-        "For example, if the food is pizza, respond with 'cheese, tomato sauce, bread, olive oil, basil'."
-    )
+    messages = [
+        {
+            "role": "user",
+            "content": f"List only the main ingredients for {food_name}. "
+                       f"Respond in a concise, comma-separated list without any extra text or explanations."
+        }
+    ]
     try:
-        # Generate response from the model
-        response = pipe_bloom(prompt, max_new_tokens=50, num_return_sequences=1)
-        generated_text = response[0]["generated_text"].strip()
-        
-        # Post-process the response
-        ingredients = generated_text.split(":")[-1].strip()  # Handle cases like "Ingredients: ..."
-        ingredients = ingredients.replace(".", "").strip()  # Remove periods and extra spaces
-
-        # Validate the response to ensure no placeholders
-        if "ingredient1" in ingredients.lower() or "example" in ingredients.lower():
-            return "No valid ingredients found. Try again with a different food."
-
-        return ingredients
+        completion = client.chat.completions.create(
+            model="Qwen/Qwen2.5-Coder-32B-Instruct",
+            messages=messages,
+            max_tokens=50
+        )
+        generated_text = completion.choices[0].message["content"].strip()
+        return generated_text
     except Exception as e:
-        # Handle any errors that occur during the process
         return f"Error generating ingredients: {e}"
+
 # Streamlit app setup
 st.title("Food Image Recognition with Ingredients")
 
@@ -59,7 +53,7 @@ st.image("IR_IMAGE.png", caption="Food Recognition Model", use_column_width=True
 # Sidebar for model information
 st.sidebar.title("Model Information")
 st.sidebar.write("**Image Classification Model**: Shresthadev403/food-image-classification")
-st.sidebar.write("**LLM for Ingredients**: bigscience/bloom-1b7")
+st.sidebar.write("**LLM for Ingredients**: Qwen/Qwen2.5-Coder-32B-Instruct")
 
 # Upload image
 uploaded_file = st.file_uploader("Choose a food image...", type=["jpg", "png", "jpeg"])
@@ -80,7 +74,7 @@ if uploaded_file is not None:
     # Generate and display ingredients for the top prediction
     st.subheader("Ingredients")
     try:
-        ingredients = get_ingredients_bloom(top_food)
+        ingredients = get_ingredients_qwen(top_food)
         st.write(ingredients)
     except Exception as e:
         st.error(f"Error generating ingredients: {e}")