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ImgageGenerator/README.md.txt

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+---
+title: Image Generator
+emoji: 🤔📊
+colorFrom: yellow
+colorTo: red
+sdk: gradio
+sdk_version: 4.38.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Image generator agent.
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ImgageGenerator/app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Aug  1 19:04:11 2024
+
+@author: rkram
+"""
+
+import os
+import gradio as gr
+from transformers import HfEngine, Tool,CodeAgent,load_tool
+from gradio_tools import StableDiffusionPromptGeneratorTool 
+from streaming import stream_to_gradio
+from huggingface_hub import login
+
+# turn caching off
+#client.headers["x-use-cache"] = "0"
+
+#login
+login(os.getenv("HUGGINGFACEHUB_API_TOKEN"))
+#define llm engine
+llm_engine = HfEngine("meta-llama/Meta-Llama-3.1-70B-Instruct")
+#load tools
+image_gen_tool = load_tool("huggingface-tools/text-to-image")
+gradio_pg_tool = StableDiffusionPromptGeneratorTool()
+pg_tool = Tool.from_gradio(gradio_pg_tool)
+#create agent
+agent = CodeAgent(
+    tools=[pg_tool,image_gen_tool],
+    llm_engine=llm_engine,
+    additional_authorized_imports=[],
+    max_iterations=10,
+)
+#base prompt 
+base_prompt = """Improve the following prompt and generate an image.
+Prompt:"""
+#Main function to interact with streaming
+def interact_with_agent(add_prompt):
+    prompt = base_prompt
+    if add_prompt and len(add_prompt) > 0:
+        prompt += add_prompt
+    else:
+        prompt="There is no prompt made. Reply exactly with:'***ERROR: Please input a prompt.***'"
+        
+    messages = [gr.ChatMessage(role="assistant", content="⏳ _Generating image..._")]
+    yield messages
+
+    for msg in stream_to_gradio(agent, prompt):
+        messages.append(msg)
+        yield messages + [
+            gr.ChatMessage(role="assistant", content="⏳ _Still processing..._")
+        ]
+    yield messages
+
+#Gradio blocks and markdowns
+with gr.Blocks(
+    theme=gr.themes.Soft(
+        primary_hue=gr.themes.colors.blue,
+        secondary_hue=gr.themes.colors.yellow,
+    )
+) as demo:
+    gr.Markdown("""# Image Generator""")
+    chatbot = gr.Chatbot(
+        label="ImageBot",
+        type="messages",
+        avatar_images=(
+            None,
+            "https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png"
+        ),
+    )
+    text_input = gr.Textbox(
+        label="What image would you like to generate?"
+    )
+    submit = gr.Button("Run", variant="primary")
+    
+    # gr.Examples(
+    #     examples=[["./example/titanic.csv", example_notes]],
+    #     inputs=[file_input, text_input],
+    #     cache_examples=False,
+    #     label='Click anywhere below to try this example.'
+    # )
+
+    submit.click(interact_with_agent, [text_input], [chatbot])
+
+if __name__ == "__main__":
+    demo.launch()

ImgageGenerator/requirements.txt

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+git+https://github.com/huggingface/transformers.git#egg=transformers[agents]
+matplotlib
+seaborn
+scikit-learn
+scipy

ImgageGenerator/streaming.py

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+from transformers.agents.agent_types import AgentAudio, AgentImage, AgentText, AgentType
+from transformers.agents import ReactAgent,CodeAgent
+import spaces
+
+
+@spaces.GPU
+def stream_to_gradio(agent: CodeAgent, task: str, **kwargs):
+    """Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
+
+    try:
+        from gradio import ChatMessage
+    except ImportError:
+        raise ImportError("Gradio should be installed in order to launch a gradio demo.")
+
+    class Output:
+        output: AgentType | str = None
+
+    Output.output = agent.run(task,**kwargs)
+    if isinstance(Output.output, AgentText):
+        yield ChatMessage(role="assistant", content=f"{Output.output}")
+    elif isinstance(Output.output, AgentImage):
+        yield ChatMessage(
+            role="assistant",
+            content={"path": Output.output.to_string(), "mime_type": "image/png"},
+        )
+    elif isinstance(Output.output, AgentAudio):
+        yield ChatMessage(
+            role="assistant",
+            content={"path": Output.output.to_string(), "mime_type": "audio/wav"},
+        )
+    else:
+        yield ChatMessage(role="assistant", content=Output.output)