initial commit

update images

update image

.

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*png filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+ai
+__pycache__/

README.md

@@ -1,7 +1,7 @@
 ---
 title: Sustainability Scorer
-emoji: 😻
-colorFrom: yellow
+emoji: 🏡
+colorFrom: green
 colorTo: gray
 sdk: gradio
 sdk_version: 4.41.0

app.py

@@ -0,0 +1,65 @@
+# app.py
+import gradio as gr
+from sustanability_scorer import (
+    sustainability_scorer,
+    openai_completion,
+    HouseFeature,
+    encode_image,
+)
+import json
+
+
+def combined_function(image):
+    base64_image = encode_image(image, output_format="PNG")
+
+    resp = openai_completion(base64_image)  # response in string
+    # Convert the response to a dictionary
+    house_features = json.loads(resp)
+    score = sustainability_scorer(HouseFeature(**house_features))
+    return score
+
+
+with gr.Blocks() as app:
+    gr.Markdown("## Sustainability Scorer")
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(
+                type="pil",
+                label="Upload Image",
+            )
+            submit_button = gr.Button("Submit")
+
+        with gr.Column():
+            score_output = gr.HTML()
+
+    submit_button.click(combined_function, inputs=image_input, outputs=score_output)
+
+    # Add CSS styles
+    gr.HTML(
+        """
+        <style>
+        .score_output {
+            font-size: 58px;
+            font-weight: bold;
+            padding: 10px;
+            border-radius: 5px;
+            background-color: #f4f4f9;
+            text-align: center;
+            transition: all 0.3s ease;
+            margin-top: 20px;
+        }
+        </style>
+        """
+    )
+    examples = gr.Examples(
+        examples=[
+            ["example1.png"],
+            ["example2.png"],
+            ["example3.png"],
+        ],
+        inputs=image_input,
+    )
+
+# interface.launch()
+app.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio==4.41.0
+openai==1.40.3
+Pillow==10.3.0

sustanability_scorer.py

@@ -0,0 +1,103 @@
+import os
+from openai import OpenAI
+import base64
+from pydantic import BaseModel
+from PIL import Image
+import io
+import json
+import random
+
+
+class HouseFeature(BaseModel):
+    age_in_years: int
+    solar_panel: bool
+    green_roof: bool
+    is_property: bool
+
+
+IMAGE_PATH = "house7.png"
+
+
+# Open the image file and encode it as a base64 string
+def encode_image(image, output_format="JPEG"):
+    # Resize the image
+    image = image.resize((256, 256))
+
+    # Convert the image to bytes
+    buffered = io.BytesIO()
+    # Save the image in the specified format
+    image.save(buffered, format=output_format)
+    image_bytes = buffered.getvalue()
+
+    # Encode the image bytes to base64
+    base64_encoded_data = base64.b64encode(image_bytes)
+
+    # Convert bytes to string
+    base64_encoded_string = base64_encoded_data.decode("utf-8")
+
+    return base64_encoded_string
+
+
+def openai_completion(base64_image):
+    # Initialize OpenAI API key from environment variable
+    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+    MODEL = "gpt-4o-mini"
+    response = client.beta.chat.completions.parse(
+        model=MODEL,
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a house expert who can estimate house's age. Help me identify houses!",
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "Estimate the house's age and whether it has solar panels.",
+                    },
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
+                    },
+                ],
+            },
+        ],
+        max_tokens=300,
+        response_format=HouseFeature,
+    )
+
+    return response.choices[0].message.content
+
+
+def sustainability_scorer(house_features: HouseFeature):
+    # Calculate sustainability score based on house features, best score 100, lowest 30
+    score = 0
+    if house_features.is_property:
+        if house_features.age_in_years < 10:
+            score += 70
+        elif house_features.age_in_years < 20:
+            score += 65
+        elif house_features.age_in_years < 30:
+            score += 45
+        else:
+            score += 25
+        if house_features.solar_panel:
+            score += 40
+        if house_features.green_roof:
+            score += 30
+        score += random.randint(0, 5)
+        # score can't be over 100
+        score = min(score, 100)
+        if score > 80:
+            color = "green"
+            feedback = "🌟 Excellent! 🌟"
+        elif score > 50:
+            color = "orange"
+            feedback = "👍 Good!"
+        else:
+            color = "red"
+            feedback = "❗️ Needs Improvement"
+        return f"<div class='score_output' style='color: {color};'>{feedback}<br>Sustainability Score: {score}</div>"
+    else:
+        return "Sorry, I'm unable to identify your house from the picture"