Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -8,7 +8,6 @@ import tempfile
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from threading import Thread
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import base64
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import shutil
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import re # Added for the new tools
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import gradio as gr
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import spaces
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@@ -17,7 +16,6 @@ import numpy as np
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from PIL import Image
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import edge_tts
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import trimesh
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import smolagents # For the new tools
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from transformers import (
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AutoModelForCausalLM,
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@@ -278,73 +276,28 @@ def generate_image_fn(
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return image_paths, seed
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# -----------------------------------------------------------------------------
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#
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# -----------------------------------------------------------------------------
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from smolagents.utils import truncate_content
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except ImportError as e:
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raise ImportError(
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"You must install packages `markdownify` and `requests` to run this tool: for instance run `pip install markdownify requests`."
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) from e
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try:
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response = requests.get(url, timeout=20)
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response.raise_for_status() # Raise an exception for bad status codes
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# Convert the HTML content to Markdown
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markdown_content = markdownify.markdownify(response.text).strip()
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# Remove multiple line breaks
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markdown_content = re.sub(r"\n{3,}", "\n\n", markdown_content)
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return truncate_content(markdown_content, 10000)
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except requests.exceptions.Timeout:
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return "The request timed out. Please try again later or check the URL."
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except RequestException as e:
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return f"Error fetching the webpage: {str(e)}"
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except Exception as e:
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return f"An unexpected error occurred: {str(e)}"
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class DuckDuckGoSearchTool(Tool):
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name = "web_search"
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description = "Performs a duckduckgo web search based on your query (think a Google search) then returns the top search results."
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inputs = {'query': {'type': 'string', 'description': 'The search query to perform.'}}
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output_type = "string"
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def __init__(self, max_results=10, **kwargs):
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super().__init__()
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self.max_results = max_results
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try:
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from duckduckgo_search import DDGS
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except ImportError as e:
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raise ImportError(
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"You must install package `duckduckgo_search` to run this tool: for instance run `pip install duckduckgo-search`."
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) from e
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self.ddgs = DDGS(**kwargs)
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def forward(self, query: str) -> str:
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results = self.ddgs.text(query, max_results=self.max_results)
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if len(results) == 0:
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raise Exception("No results found! Try a less restrictive/shorter query.")
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postprocessed_results = [
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f"[{result['title']}]({result['href']})\n{result['body']}" for result in results
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]
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return "## Search Results\n\n" + "\n\n".join(postprocessed_results)
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# -----------------------------------------------------------------------------
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# Chat Generation Function with support for @tts, @image,
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# -----------------------------------------------------------------------------
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@spaces.GPU
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"""
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Generates chatbot responses with support for multimodal input, TTS, image generation,
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3D model generation
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Special commands:
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- "@tts1" or "@tts2": triggers text-to-speech.
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- "@image": triggers image generation using the SDXL pipeline.
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- "@3d": triggers 3D model generation using the ShapE pipeline.
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- "@web": triggers a web command. Use "visit" to visit a URL (e.g., "@web visit https://example.com")
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or "search" to perform a DuckDuckGo search (e.g., "@web search AI news").
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"""
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text = input_dict["text"]
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files = input_dict.get("files", [])
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yield gr.Image(image_paths[0])
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return
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# --- Web Search/Visit branch ---
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if text.strip().lower().startswith("@web"):
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command_text = text[len("@web"):].strip()
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if command_text.lower().startswith("visit "):
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url = command_text[len("visit"):].strip()
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yield "Visiting webpage..."
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result = VisitWebpageTool().forward(url)
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yield result
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elif command_text.lower().startswith("search "):
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query = command_text[len("search"):].strip()
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yield "Performing web search..."
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result = DuckDuckGoSearchTool().forward(query)
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yield result
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else:
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# Default to web search if no subcommand is specified.
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yield "Performing web search..."
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result = DuckDuckGoSearchTool().forward(command_text)
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yield result
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return
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# --- Text and TTS branch ---
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tts_prefix = "@tts"
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is_tts = any(text.strip().lower().startswith(f"{tts_prefix}{i}") for i in range(1, 3))
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@@ -525,10 +456,9 @@ demo = gr.ChatInterface(
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examples=[
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["@tts1 Who is Nikola Tesla, and why did he die?"],
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["@3d A birthday cupcake with cherry"],
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["@web Is Grok-3 Beats DeepSeek-R1 at Reasoning ?"],
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[{"text": "summarize the letter", "files": ["examples/1.png"]}],
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["@image Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic"],
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["
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["@tts2 What causes rainbows to form?"],
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],
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cache_examples=False,
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@@ -550,4 +480,5 @@ from fastapi.staticfiles import StaticFiles
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demo.app.mount("/static", StaticFiles(directory="static"), name="static")
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if __name__ == "__main__":
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demo.queue(max_size=20).launch(share=True)
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from threading import Thread
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import base64
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import shutil
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import gradio as gr
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import spaces
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from PIL import Image
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import edge_tts
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import trimesh
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from transformers import (
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AutoModelForCausalLM,
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return image_paths, seed
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# -----------------------------------------------------------------------------
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# Text-to-3D Generation using the ShapE Pipeline
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# -----------------------------------------------------------------------------
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@spaces.GPU(duration=120, enable_queue=True)
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def generate_3d_fn(
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prompt: str,
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seed: int = 1,
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guidance_scale: float = 15.0,
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num_steps: int = 64,
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randomize_seed: bool = False,
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):
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"""
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Generate a 3D model from text using the ShapE pipeline.
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Returns a tuple of (glb_file_path, used_seed).
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"""
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seed = int(randomize_seed_fn(seed, randomize_seed))
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model3d = Model()
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glb_path = model3d.run_text(prompt, seed=seed, guidance_scale=guidance_scale, num_steps=num_steps)
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return glb_path, seed
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# -----------------------------------------------------------------------------
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# Chat Generation Function with support for @tts, @image, and @3d commands
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# -----------------------------------------------------------------------------
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@spaces.GPU
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):
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"""
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Generates chatbot responses with support for multimodal input, TTS, image generation,
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and 3D model generation.
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Special commands:
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- "@tts1" or "@tts2": triggers text-to-speech.
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- "@image": triggers image generation using the SDXL pipeline.
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- "@3d": triggers 3D model generation using the ShapE pipeline.
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"""
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text = input_dict["text"]
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files = input_dict.get("files", [])
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yield gr.Image(image_paths[0])
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return
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# --- Text and TTS branch ---
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tts_prefix = "@tts"
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is_tts = any(text.strip().lower().startswith(f"{tts_prefix}{i}") for i in range(1, 3))
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examples=[
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["@tts1 Who is Nikola Tesla, and why did he die?"],
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["@3d A birthday cupcake with cherry"],
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[{"text": "summarize the letter", "files": ["examples/1.png"]}],
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["@image Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic"],
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["Write a Python function to check if a number is prime."],
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["@tts2 What causes rainbows to form?"],
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],
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cache_examples=False,
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demo.app.mount("/static", StaticFiles(directory="static"), name="static")
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if __name__ == "__main__":
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# Launch without the unsupported static_dirs parameter.
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demo.queue(max_size=20).launch(share=True)
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