Update app.py

app.py

@@ -1,130 +1,72 @@
-import os
-import json
-from flask import Flask, request, jsonify
+# app.py
+import streamlit as st
 from PIL import Image
 import tensorflow as tf
 import numpy as np
 from tensorflow.keras.applications.efficientnet import preprocess_input
-from dotenv import load_dotenv
-import google.generativeai as genai
+import json
 from io import BytesIO
-
-# Suppress TensorFlow warnings
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
-os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
+import google.generativeai as genai
+from dotenv import load_dotenv
+import os
 
 # Load environment variables
 load_dotenv()
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-if not GOOGLE_API_KEY:
-    raise Exception("Google API key not found. Set it in your .env file")
 genai.configure(api_key=GOOGLE_API_KEY)
 
-# Load your model
+# Load model
 IMG_SIZE = (299, 299)
-if not os.path.exists("model.h5"):
-    raise Exception("Model file model.h5 not found in repo!")
 model = tf.keras.models.load_model("model.h5")
-model.summary()
-
 with open("trained_classes.json", "r") as f:
     class_labels = json.load(f)
 
-print("Class order in trained_classes.json:", class_labels)
-
-# Flask app
-app = Flask(__name__)
-
-ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
-
-def allowed_file(filename):
-    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
-
-def predict_food_from_image(image):
-    try:
-        img = image.resize(IMG_SIZE)
-        img_array = np.array(img)
-
-        if len(img_array.shape) == 2:
-            img_array = np.stack((img_array,) * 3, axis=-1)
-        if img_array.shape[-1] == 4:
-            img_array = img_array[..., :3]
-
-        img_array = preprocess_input(img_array)
-        img_array = np.expand_dims(img_array, axis=0)
-
-        predictions = model.predict(img_array)
-        predicted_index = np.argmax(predictions[0])
-        confidence = float(predictions[0][predicted_index])
-
-        predicted_food = class_labels[predicted_index] if predicted_index < len(class_labels) else "unknown"
-        return predicted_food, confidence
-    except Exception as e:
-        print("Prediction error:", e)
-        return "unknown", 0.0
-
-def input_image_setup(uploaded_file):
-    try:
-        return [{"mime_type": "image/jpeg", "data": uploaded_file.read()}]
-    except Exception as e:
-        print("Error in input_image_setup:", e)
-        return []
-
-def get_gemini_response(food_item, image_data, prompt):
+# Streamlit UI
+st.title("NutriSync: Food Nutrition Analyzer")
+uploaded_file = st.file_uploader("Upload food image", type=["jpg","jpeg","png"])
+
+def predict_food(img):
+    img = img.resize(IMG_SIZE)
+    img_array = np.array(img)
+    if len(img_array.shape) == 2:
+        img_array = np.stack((img_array,) * 3, axis=-1)
+    if img_array.shape[-1] == 4:
+        img_array = img_array[..., :3]
+    img_array = preprocess_input(img_array)
+    img_array = np.expand_dims(img_array, axis=0)
+    preds = model.predict(img_array)
+    idx = np.argmax(preds[0])
+    confidence = float(preds[0][idx])
+    food = class_labels[idx] if idx < len(class_labels) else "unknown"
+    return food, confidence
+
+def get_gemini_response(food_item, img_bytes, prompt):
     try:
         model_gemini = genai.GenerativeModel("gemini-1.5-flash-latest")
-        response = model_gemini.generate_content([food_item, image_data[0], prompt])
+        response = model_gemini.generate_content([food_item, {"mime_type":"image/jpeg","data":img_bytes}, prompt])
         return response.text
     except Exception as e:
-        print("Gemini API error:", e)
-        return f"Error retrieving nutritional details: {str(e)}"
-
-@app.route("/", methods=["GET"])
-def home():
-    return jsonify({"message": "Welcome to NutriSync Backend!"})
-
-@app.route("/upload", methods=["POST"])
-def upload_file():
-    if "image" not in request.files:
-        return jsonify({"error": "No image uploaded"}), 400
-    image_file = request.files["image"]
-    if image_file.filename == "" or not allowed_file(image_file.filename):
-        return jsonify({"error": "Invalid file"}), 400
-
-    try:
-        # Load image in memory
-        img = Image.open(BytesIO(image_file.read())).convert("RGB")
-        predicted_food, confidence = predict_food_from_image(img)
-        confidence_threshold = 0.6
-
-        # Reset pointer for Gemini
-        image_file.seek(0)
-        image_data = input_image_setup(image_file)
-
-        prompt = (
-            f"Analyze the nutritional content of {predicted_food}. "
-            "Provide estimated values for Calories, Protein (g), Carbohydrates (g), Fats (g), Fiber (g), and Sugars (g) "
-            "along with short dietary recommendations. Do not use bold formatting."
-            if predicted_food != "unknown" and confidence >= confidence_threshold
-            else "Analyze the nutritional content of food. Provide estimated values only."
-        )
-
-        nutritional_response = get_gemini_response(predicted_food, image_data, prompt)
-
-        return jsonify({
-            "predicted_food": predicted_food,
-            "confidence": confidence,
-            "nutritional_info": nutritional_response
-        })
-
-    except Exception as e:
-        print("Error processing image:", e)
-        return jsonify({"error": str(e)}), 500
-
-@app.route("/ping", methods=["GET"])
-def ping():
-    return jsonify({"message": "pong"})
-
-if __name__ == "__main__":
-    app.run(debug=True, host="0.0.0.0", port=5000)
-
+        return f"Error retrieving nutritional info: {e}"
+
+if uploaded_file is not None:
+    img = Image.open(uploaded_file).convert("RGB")
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+
+    food, confidence = predict_food(img)
+    st.write(f"Predicted Food: {food}, Confidence: {confidence:.2f}")
+
+    # Reset file pointer for Gemini
+    uploaded_file.seek(0)
+    img_bytes = uploaded_file.read()
+
+    prompt = (
+        f"Analyze the nutritional content of {food}. "
+        "Provide estimated values for Calories, Protein (g), Carbs (g), Fats (g), Fiber (g), Sugars (g) "
+        "with short dietary recommendations."
+        if food != "unknown" and confidence >= 0.6
+        else "Analyze the nutritional content of food. Provide estimated values only."
+    )
+
+    nutrition_info = get_gemini_response(food, img_bytes, prompt)
+    st.write("Nutritional Info:")
+    st.write(nutrition_info)