Create app.py
Browse files
app.py
ADDED
|
@@ -0,0 +1,65 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import streamlit as st
|
| 2 |
+
from smolagents.agents import ToolCallingAgent
|
| 3 |
+
from smolagents import tool, LiteLLMModel
|
| 4 |
+
from typing import Optional
|
| 5 |
+
import cv2
|
| 6 |
+
import pytesseract
|
| 7 |
+
from PIL import Image
|
| 8 |
+
import io
|
| 9 |
+
import numpy as np
|
| 10 |
+
import base64
|
| 11 |
+
|
| 12 |
+
# Define the LiteLLMModel with OpenAI key
|
| 13 |
+
model = LiteLLMModel(model_id="gpt-4o", api_key="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")
|
| 14 |
+
|
| 15 |
+
@tool
|
| 16 |
+
def extract_components(image_data_base64: str) -> str:
|
| 17 |
+
"""
|
| 18 |
+
Extract components from a web design image.
|
| 19 |
+
|
| 20 |
+
Args:
|
| 21 |
+
image_data_base64: The image data in base64 string format.
|
| 22 |
+
|
| 23 |
+
Returns:
|
| 24 |
+
A string describing the components found in the image.
|
| 25 |
+
"""
|
| 26 |
+
image_data = base64.b64decode(image_data_base64)
|
| 27 |
+
image = Image.open(io.BytesIO(image_data))
|
| 28 |
+
gray = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
|
| 29 |
+
components = pytesseract.image_to_string(gray)
|
| 30 |
+
return components
|
| 31 |
+
|
| 32 |
+
@tool
|
| 33 |
+
def generate_code(components: str) -> str:
|
| 34 |
+
"""
|
| 35 |
+
Generate code for the given components.
|
| 36 |
+
|
| 37 |
+
Args:
|
| 38 |
+
components: A string describing the components.
|
| 39 |
+
|
| 40 |
+
Returns:
|
| 41 |
+
The generated code for the components.
|
| 42 |
+
"""
|
| 43 |
+
# This is a placeholder implementation. You can replace it with actual code generation logic.
|
| 44 |
+
return f"Generated code for components: {components}"
|
| 45 |
+
|
| 46 |
+
# Define the ToolCallingAgent
|
| 47 |
+
agent = ToolCallingAgent(tools=[extract_components, generate_code], model=model)
|
| 48 |
+
|
| 49 |
+
# Streamlit app title
|
| 50 |
+
st.title("Web Design Component Extractor")
|
| 51 |
+
|
| 52 |
+
# File uploader for the web design image
|
| 53 |
+
uploaded_file = st.file_uploader("Upload a web design image", type=["png", "jpg", "jpeg"])
|
| 54 |
+
|
| 55 |
+
# Button to run the agent
|
| 56 |
+
if st.button("Extract and Generate Code"):
|
| 57 |
+
if uploaded_file is not None:
|
| 58 |
+
image_data = uploaded_file.read()
|
| 59 |
+
image_data_base64 = base64.b64encode(image_data).decode('utf-8')
|
| 60 |
+
components = agent.run(f"extract_components {image_data_base64}")
|
| 61 |
+
code = agent.run(f"generate_code {components}")
|
| 62 |
+
st.write("Extracted Components:", components)
|
| 63 |
+
st.write("Generated Code:", code)
|
| 64 |
+
else:
|
| 65 |
+
st.write("Please upload an image.")
|