Create app.py

app.py

@@ -0,0 +1,125 @@
+import requests
+import re
+import base64
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+from flask import Flask, render_template, request, redirect, url_for, flash
+
+app = Flask(__name__)
+
+# Load the Hugging Face model and tokenizer
+model_id = "meta-llama/llama-3-2-90b-vision-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+def input_image_setup(uploaded_file):
+    """
+    Encodes the uploaded image file into a base64 string to be used with AI models.
+
+    Parameters:
+    - uploaded_file: File-like object uploaded via a file uploader
+
+    Returns:
+    - encoded_image (str): Base64 encoded string of the image data
+    """
+    if uploaded_file is not None:
+        bytes_data = uploaded_file.read()
+        encoded_image = base64.b64encode(bytes_data).decode("utf-8")
+        return encoded_image
+    else:
+        raise FileNotFoundError("No file uploaded")
+
+def format_response(response_text):
+    """
+    Formats the model response to display each item on a new line as a list.
+    Converts numbered items into HTML `<ul>` and `<li>` format.
+    """
+    response_text = re.sub(r"\*\*(.*?)\*\*", r"<p><strong>\1</strong></p>", response_text)
+    response_text = re.sub(r"(?m)^\s*\*\s(.*)", r"<li>\1</li>", response_text)
+    response_text = re.sub(r"(<li>.*?</li>)+", lambda match: f"<ul>{match.group(0)}</ul>", response_text, flags=re.DOTALL)
+    response_text = re.sub(r"</p>(?=<p>)", r"</p><br>", response_text)
+    response_text = re.sub(r"(\n|\\n)+", r"<br>", response_text)
+    return response_text
+
+def generate_model_response(encoded_image, user_query, assistant_prompt):
+    """
+    Sends an image and a query to the model and retrieves the description or answer.
+    Formats the response using HTML elements for better presentation.
+    """
+    # Prepare input for the model
+    input_text = assistant_prompt + "\n\n" + user_query + "\n![Image](data:image/jpeg;base64," + encoded_image + ")"
+    
+    inputs = tokenizer(input_text, return_tensors="pt")
+    
+    try:
+        # Generate the model's response
+        outputs = model.generate(**inputs)
+        raw_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # Format the raw response text using the format_response function
+        formatted_response = format_response(raw_response)
+        return formatted_response
+    except Exception as e:
+        print(f"Error in generating response: {e}")
+        return "<p>An error occurred while generating the response.</p>"
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    if request.method == "POST":
+        user_query = request.form.get("user_query")
+        uploaded_file = request.files.get("file")
+
+        if uploaded_file:
+            encoded_image = input_image_setup(uploaded_file)
+
+            if not encoded_image:
+                flash("Error processing the image. Please try again.", "danger")
+                return redirect(url_for("index"))
+
+            assistant_prompt = """
+            You are an expert nutritionist. Your task is to analyze the food items displayed in the image and provide a detailed nutritional assessment using the following format:
+
+            1. **Identification**: List each identified food item clearly, one per line.
+            2. **Portion Size & Calorie Estimation**: For each identified food item, specify the portion size and provide an estimated number of calories. Use bullet points with the following structure:
+            - **[Food Item]**: [Portion Size], [Number of Calories] calories
+
+            Example:
+            *   **Salmon**: 6 ounces, 210 calories
+            *   **Asparagus**: 3 spears, 25 calories
+
+            3. **Total Calories**: Provide the total number of calories for all food items.
+
+            Example:
+            Total Calories: [Number of Calories]
+
+            4. **Nutrient Breakdown**: Include a breakdown of key nutrients such as **Protein**, **Carbohydrates**, **Fats**, **Vitamins**, and **Minerals**. Use bullet points, and for each nutrient provide details about the contribution of each food item.
+
+            Example:
+            *   **Protein**: Salmon (35g), Asparagus (3g), Tomatoes (1g) = [Total Protein]
+
+            5. **Health Evaluation**: Evaluate the healthiness of the meal in one paragraph.
+
+            6. **Disclaimer**: Include the following exact text as a disclaimer:
+
+            The nutritional information and calorie estimates provided are approximate and are based on general food data. 
+            Actual values may vary depending on factors such as portion size, specific ingredients, preparation methods, and individual variations. 
+            For precise dietary advice or medical guidance, consult a qualified nutritionist or healthcare provider.
+
+            Format your response exactly like the template above to ensure consistency.
+            """
+
+            # Generate the model's response
+            response = generate_model_response(encoded_image, user_query, assistant_prompt)
+
+            # Render the result
+            return render_template("index.html", user_query=user_query, response=response)
+
+        else:
+            flash("Please upload an image file.", "danger")
+            return redirect(url_for("index"))
+
+    return render_template("index.html")
+
+if __name__ == "__main__":
+    app.run(debug=True)