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import gradio as gr |
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import os |
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from dotenv import load_dotenv |
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from textwrap import dedent |
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from agno.agent import Agent |
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from agno.team.team import Team |
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from agno.media import Image |
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from agno.models.anthropic import Claude |
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from agno.tools.googlesearch import GoogleSearchTools |
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load_dotenv() |
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ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY") |
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if not ANTHROPIC_API_KEY: |
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raise ValueError("ANTHROPIC_API_KEY not found in .env file.") |
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image_agent = Agent( |
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name="Image Analyzer", |
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role="Analyze uploaded car issue images to extract any visible mechanical problems or symptoms.", |
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model=Claude(id="claude-3-5-sonnet-20240620", api_key=ANTHROPIC_API_KEY) |
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) |
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info_agent = Agent( |
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name="Info Gatherer", |
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role="Use online search to collect information about known issues for the given car brand, model, and year.", |
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model=Claude(id="claude-3-haiku-20240307", api_key=ANTHROPIC_API_KEY), |
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tools=[GoogleSearchTools()], |
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show_tool_calls=False |
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) |
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agent_team = Team( |
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name="Smart Repair Team", |
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mode="coordinate", |
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model=Claude(id="claude-3-haiku-20240307", api_key=ANTHROPIC_API_KEY), |
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members=[image_agent, info_agent], |
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description="You are a task router that routes tasks to a team of agents to help users diagnose and fix car issues.", |
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instructions=dedent("""\ |
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- First, use the uploaded image to detect possible issues visually. |
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- Then, use search tools to gather brand/model-specific info if available from the image extracted information. |
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- Give the user a helpful, step-by-step repair guide. |
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- Explain steps clearly for non-experts. |
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- Mention safety precautions and signs when to visit a professional. |
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"""), |
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expected_output=dedent("""\ |
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Final response should be structured with these sections: |
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- Diagnosis Summary |
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- Step-by-Step Repair Instructions |
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"""), |
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success_criteria="The user receives a clear, accurate step-by-step guide for handling the car issue.", |
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add_datetime_to_instructions=True, |
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show_tool_calls=False, |
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enable_agentic_context=True, |
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show_members_responses=False, |
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markdown=True |
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) |
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def diagnose_chat(image_pil, brand, model, year): |
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if image_pil is None: |
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return "Please upload an image of the car issue to proceed." |
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os.makedirs("temp_images", exist_ok=True) |
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temp_path = os.path.join("temp_images", "temp_car_issue.jpg") |
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image_pil.save(temp_path) |
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image_media = Image(filepath=temp_path) |
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extra_info = [] |
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if brand: |
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extra_info.append(f"Brand: {brand}") |
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if model: |
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extra_info.append(f"Model: {model}") |
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if year: |
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extra_info.append(f"Year: {year}") |
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info_text = "\n".join(extra_info) |
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message = "Diagnose the car issue based on the uploaded image." |
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if info_text: |
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message += f"\n\nAdditional details:\n{info_text}" |
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response = agent_team.run(message=message, images=[image_media]) |
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return response.content |
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with gr.Blocks() as demo: |
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gr.Markdown("# 🔧 SmartCarDoc\nUpload a car issue image to quickly get a repair plan.") |
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with gr.Row(): |
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with gr.Column(): |
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image_input = gr.Image(type="pil", label="Upload Image of Car Issue") |
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brand_input = gr.Textbox(label="Car Brand (optional)") |
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model_input = gr.Textbox(label="Car Model (optional)") |
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year_input = gr.Textbox(label="Year (optional)") |
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submit_button = gr.Button("Diagnose") |
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with gr.Column(): |
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response_text = gr.Textbox(label="Repair Plan", lines=30, interactive=False) |
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submit_button.click( |
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fn=diagnose_chat, |
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inputs=[image_input, brand_input, model_input, year_input], |
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outputs=response_text |
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) |
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if __name__ == "__main__": |
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demo.launch() |
