Delete app_test.py

app_test.py

@@ -1,129 +0,0 @@
-from huggingface_hub import InferenceClient
-from langchain_community.tools import DuckDuckGoSearchResults
-from langchain.agents import create_react_agent, AgentExecutor
-from langchain_core.tools import BaseTool
-from pydantic import Field
-from PIL import Image, ImageDraw, ImageFont
-from functools import lru_cache
-import gradio as gr
-from io import BytesIO
-import os
-
-# === Setup Inference Clients ===
-# Use your Hugging Face token if necessary:
-# client = InferenceClient(repo_id="model", token="YOUR_HF_TOKEN")
-
-image_client = InferenceClient("m-ric/text-to-image")
-text_client = InferenceClient("Qwen/Qwen2.5-72B-Instruct")
-
-# === LangChain wrapper using InferenceClient for text generation ===
-class InferenceClientLLM(BaseTool):
-    name: str = "inference_text_generator"
-    description: str = "Generate text using HF Inference API."
-    client: InferenceClient = Field(default=text_client, exclude=True)
-
-    def _run(self, prompt: str) -> str:
-        print(f"[LLM] Generating text for prompt: {prompt}")
-        response = self.client.text_generation(prompt)
-        # response is usually a dict with 'generated_text'
-        return response.get("generated_text", "")
-
-    def _arun(self, prompt: str):
-        raise NotImplementedError("Async not supported.")
-
-# === Image generation tool ===
-class TextToImageTool(BaseTool):
-    name: str = "text_to_image"
-    description: str = "Generate an image from a text prompt."
-    client: InferenceClient = Field(default=image_client, exclude=True)
-
-    def _run(self, prompt: str) -> Image.Image:
-        print(f"[Image Tool] Generating image for prompt: {prompt}")
-        image_bytes = self.client.text_to_image(prompt)
-        return Image.open(BytesIO(image_bytes))
-
-    def _arun(self, prompt: str):
-        raise NotImplementedError("Async not supported.")
-
-# === Initialize tools ===
-text_to_image_tool = TextToImageTool()
-text_gen_tool = InferenceClientLLM()
-search_tool = DuckDuckGoSearchResults()
-
-# === Create agent ===
-agent = create_react_agent(llm=text_gen_tool, tools=[text_to_image_tool, search_tool])
-agent_executor = AgentExecutor(agent=agent, tools=[text_to_image_tool, search_tool], verbose=True)
-
-# === Image labeling ===
-def add_label_to_image(image, label):
-    draw = ImageDraw.Draw(image)
-    font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf"
-    try:
-        font = ImageFont.truetype(font_path, 30)
-    except:
-        font = ImageFont.load_default()
-
-    text_width, text_height = draw.textsize(label, font=font)
-    position = (image.width - text_width - 20, image.height - text_height - 20)
-    rect_position = [position[0] - 10, position[1] - 10, position[0] + text_width + 10, position[1] + text_height + 10]
-    draw.rectangle(rect_position, fill=(0, 0, 0, 128))
-    draw.text(position, label, fill="white", font=font)
-    return image
-
-# === Prompt generation with caching ===
-@lru_cache(maxsize=128)
-def generate_prompts_for_object(object_name):
-    return {
-        "past": f"Show an old version of a {object_name} from its early days.",
-        "present": f"Show a {object_name} with current features/design/technology.",
-        "future": f"Show a futuristic version of a {object_name}, predicting future features/designs.",
-    }
-
-# === Cache generated images ===
-@lru_cache(maxsize=64)
-def generate_image_for_prompt(prompt, label):
-    img = text_to_image_tool._run(prompt)
-    return add_label_to_image(img, label)
-
-# === Main generation function ===
-def generate_object_history(object_name: str):
-    prompts = generate_prompts_for_object(object_name)
-    images = []
-    file_paths = []
-
-    for period, prompt in prompts.items():
-        label = f"{object_name} - {period.capitalize()}"
-        labeled_image = generate_image_for_prompt(prompt, label)
-
-        file_path = f"/tmp/{object_name}_{period}.png"
-        labeled_image.save(file_path)
-        images.append((file_path, label))
-        file_paths.append(file_path)
-
-    # Create GIF
-    gif_path = f"/tmp/{object_name}_evolution.gif"
-    pil_images = [Image.open(p) for p in file_paths]
-    pil_images[0].save(gif_path, save_all=True, append_images=pil_images[1:], duration=1000, loop=0)
-
-    return images, gif_path
-
-# === Gradio UI ===
-def create_gradio_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("# TimeMetamorphy: Evolution Visualizer")
-
-        with gr.Row():
-            with gr.Column():
-                object_input = gr.Textbox(label="Enter Object (e.g., car, phone)")
-                generate_button = gr.Button("Generate Evolution")
-                gallery = gr.Gallery(label="Generated Images").style(grid=3)
-                gif_display = gr.Image(label="Generated GIF")
-
-        generate_button.click(fn=generate_object_history, inputs=object_input, outputs=[gallery, gif_display])
-
-    return demo
-
-# === Launch app ===
-if __name__ == "__main__":
-    demo = create_gradio_interface()
-    demo.launch(share=True)