providers

huggingface_inference_node.py

@@ -1,17 +1,20 @@
 import os
 from openai import OpenAI
+import anthropic
+from groq import Groq
 import re
 from datetime import datetime
 
-
 huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+groq_api_key = os.getenv("GROQ_API_KEY")
 
-class HuggingFaceInferenceNode:
+class LLMInferenceNode:
     def __init__(self):
-        self.client = OpenAI(
+        self.huggingface_client = OpenAI(
             base_url="https://api-inference.huggingface.co/v1/",
             api_key=huggingface_token,
         )
+        self.groq_client = Groq(api_key=groq_api_key)
         self.prompts_dir = "./prompts"
         os.makedirs(self.prompts_dir, exist_ok=True)
 
@@ -28,7 +31,7 @@ class HuggingFaceInferenceNode:
         
         print(f"Prompt saved to {filename}")
 
-    def generate(self, input_text, happy_talk, compress, compression_level, poster, prompt_type, custom_base_prompt=""):
+    def generate(self, input_text, happy_talk, compress, compression_level, poster, prompt_type, custom_base_prompt="", provider="Hugging Face", api_key=None, model=None):
         try:
             default_happy_prompt = """Create a detailed visually descriptive caption of this description, which will be used as a prompt for a text to image AI system (caption only, no instructions like "create an image").Remove any mention of digital artwork or artwork style. Give detailed visual descriptions of the character(s), including ethnicity, skin tone, expression etc. Imagine using keywords for a still for someone who has aphantasia. Describe the image style, e.g. any photographic or art styles / techniques utilized. Make sure to fully describe all aspects of the cinematography, with abundant technical details and visual descriptions. If there is more than one image, combine the elements and characters from all of the images creatively into a single cohesive composition with a single background, inventing an interaction between the characters. Be creative in combining the characters into a single cohesive scene. Focus on two primary characters (or one) and describe an interesting interaction between them, such as a hug, a kiss, a fight, giving an object, an emotional reaction / interaction. If there is more than one background in the images, pick the most appropriate one. Your output is only the caption itself, no comments or extra formatting. The caption is in a single long paragraph. If you feel the images are inappropriate, invent a new scene / characters inspired by these. Additionally, incorporate a specific movie director's visual style and describe the lighting setup in detail, including the type, color, and placement of light sources to create the desired mood and atmosphere. Always frame the scene, including details about the film grain, color grading, and any artifacts or characteristics specific."""
 
@@ -86,20 +89,67 @@ You are allowed to make up film and branding names, and do them like 80's, 90's
             system_message = "You are a helpful assistant. Try your best to give the best response possible to the user."
             user_message = f"{base_prompt}\nDescription: {input_text}"
 
-            messages = [
-                {"role": "system", "content": system_message},
-                {"role": "user", "content": user_message}
-            ]
-
-            response = self.client.chat.completions.create(
-                model="meta-llama/Meta-Llama-3.1-70B-Instruct",
-                max_tokens=1024,
-                temperature=0.7,
-                top_p=0.95,
-                messages=messages,
-            )
+            if provider == "Hugging Face":
+                response = self.huggingface_client.chat.completions.create(
+                    model=model or "meta-llama/Meta-Llama-3.1-70B-Instruct",
+                    max_tokens=1024,
+                    temperature=0.7,
+                    top_p=0.95,
+                    messages=[
+                        {"role": "system", "content": system_message},
+                        {"role": "user", "content": user_message}
+                    ],
+                )
+                output = response.choices[0].message.content.strip()
+
+            elif provider == "OpenAI":
+                openai_client = OpenAI(api_key=api_key)
+                response = openai_client.chat.completions.create(
+                    model=model or "gpt-4",
+                    max_tokens=1024,
+                    temperature=0.7,
+                    messages=[
+                        {"role": "system", "content": system_message},
+                        {"role": "user", "content": user_message}
+                    ],
+                )
+                output = response.choices[0].message.content.strip()
+
+            elif provider == "Anthropic":
+                anthropic_client = anthropic.Anthropic(api_key=api_key)
+                response = anthropic_client.messages.create(
+                    model=model or "claude-3-5-sonnet-20240620",
+                    max_tokens=1024,
+                    temperature=0.7,
+                    system=system_message,
+                    messages=[
+                        {
+                            "role": "user",
+                            "content": [
+                                {
+                                    "type": "text",
+                                    "text": user_message
+                                }
+                            ]
+                        }
+                    ]
+                )
+                output = response.content[0].text
+
+            elif provider == "Groq":
+                response = self.groq_client.chat.completions.create(
+                    model=model or "llama-3.1-70b-versatile",
+                    max_tokens=1024,
+                    temperature=0.7,
+                    messages=[
+                        {"role": "system", "content": system_message},
+                        {"role": "user", "content": user_message}
+                    ],
+                )
+                output = response.choices[0].message.content.strip()
 
-            output = response.choices[0].message.content.strip()
+            else:
+                raise ValueError(f"Unsupported provider: {provider}")
 
             # Clean up the output
             if ": " in output:

ui_components.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from prompt_generator import PromptGenerator
-from huggingface_inference_node import HuggingFaceInferenceNode
+from huggingface_inference_node import LLMInferenceNode
 from caption_models import florence_caption, qwen_caption
 import random
 from prompt_generator import ARTFORM, PHOTO_TYPE, FEMALE_BODY_TYPES, MALE_BODY_TYPES, FEMALE_DEFAULT_TAGS, MALE_DEFAULT_TAGS, ROLES, HAIRSTYLES, FEMALE_CLOTHING, MALE_CLOTHING, PLACE, LIGHTING, COMPOSITION, POSE, BACKGROUND, FEMALE_ADDITIONAL_DETAILS, MALE_ADDITIONAL_DETAILS, PHOTOGRAPHY_STYLES, DEVICE, PHOTOGRAPHER, ARTIST, DIGITAL_ARTFORM
@@ -20,7 +20,7 @@ selected_prompt_type = "happy"  # Default value
 
 def create_interface():
     prompt_generator = PromptGenerator()
-    huggingface_node = HuggingFaceInferenceNode()
+    llm_node = LLMInferenceNode()
 
     with gr.Blocks(theme='bethecloud/storj_theme') as demo:
         
@@ -116,13 +116,17 @@ def create_interface():
                         interactive=True
                     )
                     custom_base_prompt = gr.Textbox(label="Custom Base Prompt", lines=5)
-                    def update_prompt_type(value):
-                        global selected_prompt_type
-                        selected_prompt_type = value
-                        print(f"Updated prompt type: {selected_prompt_type}")
-                        return value
-                    prompt_type.change(update_prompt_type, inputs=[prompt_type], outputs=[prompt_type])
-                generate_text_button = gr.Button("Generate Prompt with LLM (Llama 3.1 70B)")
+                    
+                    # Add new components for LLM provider selection
+                    llm_provider = gr.Dropdown(
+                        choices=["Hugging Face", "OpenAI", "Anthropic", "Groq"],
+                        label="LLM Provider",
+                        value="Hugging Face"
+                    )
+                    api_key = gr.Textbox(label="API Key", type="password", visible=False)
+                    model = gr.Dropdown(label="Model", choices=["meta-llama/Meta-Llama-3.1-70B-Instruct"], value="meta-llama/Meta-Llama-3.1-70B-Instruct")
+
+                generate_text_button = gr.Button("Generate Prompt with LLM")
                 text_output = gr.Textbox(label="Generated Text", lines=10)
 
         def create_caption(image, model):
@@ -180,16 +184,32 @@ def create_interface():
             outputs=[output]
         )
 
-        def generate_text_with_llm(output, happy_talk, compress, compression_level, custom_base_prompt):
+        def update_model_choices(provider):
+            provider_models = {
+                "Hugging Face": ["meta-llama/Meta-Llama-3.1-70B-Instruct"],
+                "Groq": ["llama-3.1-70b-versatile"],
+                "OpenAI": ["gpt-4o", "gpt-4o-mini"],
+                "Anthropic": ["claude-3-5-sonnet-20240620"],
+            }
+            models = provider_models[provider]
+            return gr.Dropdown(choices=models, value=models[0])
+
+        def update_api_key_visibility(provider):
+            return gr.update(visible=(provider in ["OpenAI", "Anthropic"]))
+
+        llm_provider.change(update_model_choices, inputs=[llm_provider], outputs=[model])
+        llm_provider.change(update_api_key_visibility, inputs=[llm_provider], outputs=[api_key])
+
+        def generate_text_with_llm(output, happy_talk, compress, compression_level, custom_base_prompt, provider, api_key, model):
             global selected_prompt_type
             print(f"Prompt type selected in UI: {selected_prompt_type}")  # Debug print
-            return huggingface_node.generate(output, happy_talk, compress, compression_level, False, selected_prompt_type, custom_base_prompt)
+            return llm_node.generate(output, happy_talk, compress, compression_level, False, selected_prompt_type, custom_base_prompt, provider, api_key, model)
 
         generate_text_button.click(
             generate_text_with_llm,
-            inputs=[output, happy_talk, compress, compression_level, custom_base_prompt],
+            inputs=[output, happy_talk, compress, compression_level, custom_base_prompt, llm_provider, api_key, model],
             outputs=text_output,
-            api_name="generate_text"  # Add this line
+            api_name="generate_text"
         )
 
         # Add this line to disable caching for the generate_text_with_llm function