initial commit

app.py

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

requirements.txt

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+gradio
+python-dotenv
+agno
+googlesearch-python
+pycountry