initial

app.py

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+import traceback
+import gradio as gr 
+import numpy as np
+import os
+
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import HumanMessage, SystemMessage, AIMessage
+from langchain_anthropic import ChatAnthropic, ChatAnthropicMessages
+import openai
+
+os.environ["OPENAI_API_KEY"] = "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"
+os.environ["ANTHROPIC_API_KEY"] = \
+"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"
+openai.api_key = os.environ["OPENAI_API_KEY"]
+# chat = ChatOpenAI(model="gpt-4-turbo-preview")
+# chat = ChatOpenAI(model="gpt-3.5-turbo")
+
+structure = """
+# Headline {{ headline | bold, inspiring, action-oriented, max 8 words }}
+
+## Introduction
+{{ introduction_paragraph | motivational, passionate, 2-3 sentences }}
+
+## Features and Benefits
+{% for feature in features %}
+### Feature {{ loop.index }}: {{ feature.name | dynamic, direct, 5-6 words }}
+{{ feature.details | energetic, clear, 3-4 sentences }}
+{% endfor %}
+
+## Technical Specifications
+{{ technical_specs | informative, to the point, concise list format }}
+"""
+
+features = "Brand: Duckly. \nProduct name: Duck runner pro. \nKey properties: t-shirt, for running, sweat wicking, for marathon, 100% cotton."
+
+reference = " "
+
+import base64
+import requests
+
+# OpenAI API Key
+
+# Function to encode the image
+def encode_image(image_path):
+  with open(image_path, "rb") as image_file:
+    return base64.b64encode(image_file.read()).decode('utf-8')
+
+
+import json
+
+
+def get_json(text: str):
+    text = text.strip().replace('`', '').replace('json', '')
+    if text.startswith("No garment detected"):
+        return {
+            "features": [],
+            "intended_use": [],
+            "alt_text": []
+        }
+    return json.loads(text)
+
+
+def detect_features(image_paths):
+    # Path to your image
+    # image_path = "path_to_your_image.jpg"
+
+    # Getting the base64 string
+    try:
+        base64_images = [encode_image(image_path[0]) for image_path in image_paths]
+    
+        headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {openai.api_key}"
+        }
+
+        payload = {
+        "model": "gpt-4-vision-preview",
+        "messages": [
+            {
+            "role": "user",
+            "content": [
+                {
+                "type": "text",
+                "text": """Describe the features of the garment in the photos in less than 100 words.
+                What is the intended use of the garment in this image, use at most 5 words for intended use?
+                Generate alt text for each of the images.
+                If the photo does not contain a garment, return 'No garment detected'.
+                If the photo contains a garment, return the result in in the following JSON format without any preceding or trailing text: 
+                {
+                    "features": [list of comma separated features], 
+                    "intended_use": [list of comma separated intended uses], 
+                    "alt_text": [alt text for image 1, alt text for image 2]
+                }"""
+                },
+            ] + [{
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:image/jpeg;base64,{base64_image}"
+                }
+                } for base64_image in base64_images]
+            }
+        ],
+        "temperature": 0.0,
+        "max_tokens": 300
+        }
+
+        response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+
+        response = response.json()
+        print("image features", response["choices"][0]['message']['content'])
+        jresponse = get_json(response["choices"][0]['message']['content'])
+        return jresponse, base64_images
+    except Exception as e:
+        print(e.__class__, e)
+        traceback.print_exc()
+        return None, None
+
+
+def generate(features, image, structure, reference, model, temperature):
+    print(f"features {features},\n image {image},\n structure{structure},\n model{model},\n temperature {temperature},\n reference {reference}")
+    image_features, base64_images = detect_features(image)
+    detected_features = ""
+    intended_use = ""
+    alt_texts = ""
+    if image_features is not None:
+        alt_texts = image_features["alt_text"]
+        detected_features = ", ".join(image_features["features"])
+        intended_use = "Intended use: " + ", ".join(image_features["intended_use"])
+        print(f"Detected features: {detected_features}, Intended use: {intended_use}, Alt text: {alt_texts}")
+    if model.startswith("gpt"):
+        chat = ChatOpenAI(model=model)
+    else:
+        chat = ChatAnthropic(model_name=model, anthropic_api_key=os.environ["ANTHROPIC_API_KEY"])
+    messages = [HumanMessage(content=f"""Write a product description of about 200 words for a product with the following key properties. 
+                             Make sure that the description follows the structure of the reference structure below. 
+                             Use creative language that is suitable for e-commerce websites. Use a consistent tone of voice throughout the text. 
+                             If the reference text is not empty, produce the product description in the tone of voice and structure of the reference text. 
+                             \n\n {features + detected_features} \n{intended_use} \nReference structure: {structure}\n Reference text: {reference}""")]
+    chat_response = chat.invoke(messages, temperature=temperature)
+    print(messages[0].content)
+    return chat_response.content + '\n\n### Alt text\n\n' + '\n- ' + '\n- '.join(alt_texts) + '\n'.join([f'![Product photo](data:image/png;base64,{base64_image} "{alt_text}")' if base64_image != "" else "" for (base64_image, alt_text) in zip(base64_images, alt_texts)])
+
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=[gr.Textbox(features, label="Features"), 
+            gr.Gallery(label="Product image", type="filepath"), 
+            gr.Textbox(structure, label="Structure"), 
+            gr.Textbox(reference, label="Reference copy"),
+            gr.Dropdown(["gpt-4-turbo", "gpt-3.5-turbo", "claude-3-sonnet-20240229", "claude-3-opus-20240229"], value="gpt-3.5-turbo", label="Model"),
+            gr.Slider(minimum=0., maximum=1.0, value=0.5, label="Temperature")],
+    outputs=["markdown"],
+)
+
+# demo.launch()
+
+import argparse
+
+parser = argparse.ArgumentParser(description='Run the Gradio demo')
+parser.add_argument('--public', action='store_true', help='Expose the demo to the public')
+args = parser.parse_args()
+
+# if args.public:
+#     demo.launch(share=True)
+# else:
+#     demo.launch(server_name="0.0.0.0", server_port=7868)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+langchain
+langchain_openai
+langchain_anthropic
+openai