Create app.py

app.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+import tensorflow as tf
+import numpy as np
+import shutil
+import os
+from huggingface_hub import InferenceClient
+import json
+import requests
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import chain
+from langchain_huggingface import HuggingFaceEndpoint,ChatHuggingFace
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnableParallel
+from PIL import Image
+import json
+import requests
+
+# Initialize FastAPI app
+app = FastAPI()
+
+chat_nutrition_prompt = ChatPromptTemplate.from_template(
+    '''
+    Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6) for {prediction} per 100 grams, Output the information as a concise, formatted list without repetition.
+    '''
+)
+
+chat_health_benefits_prompt = ChatPromptTemplate.from_template(
+    '''
+    Provide the health benefits and considerations for {prediction}. Additionally, include practical tips for making {prediction} healthier. Keep the response focused on these two aspects only.
+    '''
+)
+chat_recipes_prompt = ChatPromptTemplate.from_template(
+    '''
+    Tell me about the two most famous recipes for {prediction}. Include the ingredients only.
+    '''
+)
+
+def load_and_prep_image(uploaded_file, img_shape=224):
+    img = Image.open(uploaded_file)  # Open uploaded image
+    img = img.resize((img_shape, img_shape))  # Resize image
+    img = tf.convert_to_tensor(img, dtype=tf.float32)  # Convert to tensor
+    return img
+@chain
+def predict_label(uploaded_file):
+    img = load_and_prep_image(uploaded_file, img_shape=224)  # Preprocess image
+    img = tf.expand_dims(img, axis=0)  # Add batch dimension
+    
+    pred = model.predict(img)  # Model prediction
+    pred_class_index = np.argmax(pred, axis=1)[0]  # Get highest probability index
+    pred_class_name = class_labels[pred_class_index]  # Convert index to class name
+    return pred_class_name
+    
+model = tf.keras.models.load_model("NewVersionModelOptimized40V2.keras")
+class_labels = {0: 'Baked Potato',1: 'Burger',2: 'Cake',3: 'Chips',4: 'Crispy Chicken',5: 'Croissant',
+ 6: 'Dount',7: 'Dragon Fruit',8: 'Frise',9: 'Hot Dog',10: 'Jalapeno',11: 'Kiwi',12: 'Lemon',13: 'Lettuce',
+ 14: 'Mango',15: 'Onion',16: 'Orange',17: 'Pizza',18: 'Taquito',19: 'apple',20: 'banana',21: 'beetroot',
+ 22: 'bell pepper',23: 'bread',24: 'cabbage',25: 'carrot',26: 'cauliflower',27: 'cheese',28: 'chilli pepper',
+ 29: 'corn',30: 'crab',31: 'cucumber',32: 'eggplant',33: 'eggs',34: 'garlic',36: 'grapes',37: 'milk',
+ 38: 'salamon',39: 'yogurt'}
+
+api_key='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1'
+llm = HuggingFaceEndpoint(
+    repo_id="Qwen/Qwen2.5-72B-Instruct",
+    max_new_tokens=512,
+    client=api_key[:-1]
+)
+chat = ChatHuggingFace(llm=llm, verbose=True)
+
+str_output_parser = StrOutputParser()
+
+chain_label = predict_label
+chain1=  chat_nutrition_prompt | chat | str_output_parser
+chain2=  chat_health_benefits_prompt | chat | str_output_parser
+chain3=  chat_recipes_prompt | chat | str_output_parser
+
+chain_parallel = RunnableParallel({'chat_nutrition_prompt':chain1,
+                                   'chat_health_benefits_prompt':chain2,
+                                   'chat_recipes_prompt':chain3})
+
+@app.post("/predictNUT")
+async def predict_image_and_nutrition(file: UploadFile = File(...)):
+    try:
+        # Save the uploaded file
+        file_location = f"./temp_{file.filename}"
+        with open(file_location, "wb") as f:
+            shutil.copyfileobj(file.file, f)
+        
+        # Predict the label using the same prediction logic
+        with open(file_location, "rb") as image_file:
+            prediction = predict_label.invoke(image_file)
+        
+        # Remove the temporary file
+        # os.remove(file_location)
+
+        result = chain_parallel.invoke(prediction)
+
+        return {
+            "Predicted_label": prediction, 
+            "Nutrition_info": result['chat_nutrition_prompt'],
+            "Information": result['chat_health_benefits_prompt'],
+            "Recipes":result['chat_recipes_prompt']
+        }
+    except Exception as e:
+        return JSONResponse(
+            status_code=500,
+            content={"error": f"An error occurred: {str(e)}"}
+        )