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
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Class labels
+class_labels = {
+    0: 'Baked Potato', 1: 'Burger', 2: 'Crispy Chicken', 3: 'Donut', 4: 'Fries',
+    5: 'Hot Dog', 6: 'Jalapeno', 7: 'Kiwi', 8: 'Lemon', 9: 'Lettuce',
+    10: 'Mango', 11: 'Onion', 12: 'Orange', 13: 'Pizza', 14: 'Taquito', 
+    15: 'Apple', 16: 'Banana', 17: 'Beetroot', 18: 'Bell Pepper', 19: 'Bread',
+    20: 'Cabbage', 21: 'Carrot', 22: 'Cauliflower', 23: 'Cheese',
+    24: 'Chilli Pepper', 25: 'Corn', 26: 'Crab', 27: 'Cucumber',
+    28: 'Eggplant', 29: 'Eggs', 30: 'Garlic', 31: 'Ginger', 32: 'Grapes',
+    33: 'Milk', 34: 'Salmon', 35: 'Yogurt'
+}
+
+# Load the trained model
+model = tf.keras.models.load_model("model_unfreezeNewCorrectpredict.keras")
+
+# Image preprocessing function
+def load_and_prep_image(file_path, img_shape=224):
+    img = tf.io.read_file(file_path)
+    img = tf.image.decode_image(img, channels=3)
+    img = tf.image.resize(img, size=[img_shape, img_shape])
+    img = tf.expand_dims(img, axis=0)
+    return img
+
+# Predict label function
+def predict_label(model, image_path, class_names):
+    img = load_and_prep_image(image_path, img_shape=224)
+    pred = model.predict(img)
+    pred_class_index = np.argmax(pred, axis=1)[0]
+    pred_class_name = class_names[pred_class_index]
+    return pred_class_name
+
+
+@app.get("/")
+def read_root():
+    return {"message": "This is My Nutrionguid App"}
+
+# API endpoint for prediction
+@app.post("/predict")
+async def predict_image(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
+        prediction = predict_label(model, file_location, class_labels)
+        
+        # Remove the temporary file
+        os.remove(file_location)
+        
+        return {"predicted_label": prediction}
+    except Exception as e:
+        return JSONResponse(
+            status_code=500,
+            content={"error": f"An error occurred: {str(e)}"}
+        )
+@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
+        prediction = predict_label(model, file_location, class_labels)
+        
+        # Remove the temporary file
+        os.remove(file_location)
+
+        # Define the repository ID and your token
+        repo_id = "microsoft/Phi-3-mini-4k-instruct"
+        api_token = "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21"
+
+        # Initialize the InferenceClient with your token
+        llm_client = InferenceClient(
+            model=repo_id,
+            token=api_token[:-2],  # Pass the token here
+            timeout=120,
+        )
+
+        # Function to call the LLM
+        def call_llm(inference_client: InferenceClient, prompt: str):
+            response = inference_client.post(
+                json={
+                    "inputs": prompt,
+                    "parameters": {"max_new_tokens": 500},
+                    "task": "text-generation",
+                },
+            )
+            return json.loads(response.decode())[0]["generated_text"]
+        
+        # Use the prediction to generate nutrition information
+        prompt = f"Nutrition information for {prediction} in formatted list"
+        response = call_llm(llm_client, prompt)
+
+        return {"predicted_label": prediction, "nutrition_info": response}
+    except Exception as e:
+        return JSONResponse(
+            status_code=500,
+            content={"error": f"An error occurred: {str(e)}"}
+        )