Add Medical Image Analysis Agent #742
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WalkthroughTwo new Jupyter notebook examples are introduced: one demonstrates an AI agent for university admissions counseling, and the other showcases a medical imaging diagnosis agent. Both notebooks install dependencies, define agent roles and prompts, introduce custom functions, and provide example interactions to illustrate the agents' capabilities in their respective domains. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Notebook
participant Agent
User->>Notebook: Submit query (and/or document lists or image)
Notebook->>Agent: Construct prompt and send (with context)
Agent-->>Notebook: Generate and return response
Notebook-->>User: Display formatted agent response
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Summary of Changes
Hello @Dhivya-Bharathy, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces two new AI agents demonstrated through Jupyter notebooks. The primary addition is a "Medical Image Analysis Agent" that processes medical images, extracts basic statistical data, and generates detailed diagnostic reports. Additionally, an "AI Enrollment Counselor" agent is included, designed to streamline university admissions inquiries and document verification processes.
Highlights
- New Medical Image Analysis Agent: A new Jupyter notebook (
medical_image_analysis_agent.ipynb) has been added, demonstrating a medical image analysis agent. This agent is designed to process uploaded medical images, compute basic image statistics, and generate structured diagnostic reports using an LLM. - Custom Tool for Image Statistics: The
medical_image_analysis_agent.ipynbnotebook includes a custom Python function (image_basic_stats) that calculates essential pixel statistics (mean, standard deviation, min/max pixel values, and image shape) from an uploaded grayscale image. These statistics are then dynamically inserted into the LLM's prompt to inform its analysis. - Structured Diagnostic Reporting: The medical image agent is specifically instructed to format its output as a comprehensive diagnostic report, covering 'Image Type & Region', 'Key Findings', 'Diagnostic Assessment', and a 'Patient-Friendly Explanation', using clear markdown headers and bullet points.
- New AI Enrollment Counselor Agent: A separate, new Jupyter notebook (
AI_Enrollment_Counselor.ipynb) has been introduced. This notebook showcases an AI agent focused on university admissions, capable of answering applicant questions and checking application completeness based on provided document lists, operating in a prompt-only mode without custom tools for validation.
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Code Review
This pull request introduces two excellent example notebooks demonstrating the capabilities of PraisonAI agents. The review focuses on improving security by recommending the use of Colab secrets for API keys and enhancing code quality by removing duplicated code and unused variables.
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\" # <-- Replace with your actual OpenAI API key" |
There was a problem hiding this comment.
Hardcoding API keys, even as placeholders, is a security risk. It's best practice to use Colab's secrets manager to handle sensitive data like API keys securely to avoid storing them in the notebook's source code.
import os
from google.colab import userdata
# Note: Add your OPENAI_API_KEY to the Colab Secrets manager (View > Secrets).
os.environ["OPENAI_API_KEY"] = userdata.get("OPENAI_API_KEY")
| "OPENAI_API_KEY = \"sk-...\" # <-- Replace with your OpenAI API key\n", | ||
| "\n", | ||
| "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY" |
There was a problem hiding this comment.
Hardcoding API keys, even as placeholders, is a security risk. I recommend using Colab's secrets manager to prevent accidental key exposure.
# Note: Add your OPENAI_API_KEY to the Colab Secrets manager (View > Secrets).
OPENAI_API_KEY = userdata.get("OPENAI_API_KEY")
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
| "from PIL import Image\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "def image_basic_stats(image_path):\n", | ||
| " img = Image.open(image_path).convert(\"L\")\n", | ||
| " arr = np.array(img)\n", | ||
| " return {\n", | ||
| " \"mean_pixel_value\": float(np.mean(arr)),\n", | ||
| " \"std_pixel_value\": float(np.std(arr)),\n", | ||
| " \"min_pixel_value\": int(np.min(arr)),\n", | ||
| " \"max_pixel_value\": int(np.max(arr)),\n", | ||
| " \"image_shape\": arr.shape\n", | ||
| " }\n", |
There was a problem hiding this comment.
The image_basic_stats function and its imports (PIL, numpy) are duplicated here. This function is already defined in cell 9. Redefining it creates redundant code, which can lead to maintenance issues. Please remove the duplicated imports and function definition from this cell and rely on the one defined earlier.
| "ROLE = (\n", | ||
| " \"AI Enrollment Counselor. Expert in university admissions, document validation, and applicant guidance.\"\n", | ||
| ")\n", | ||
| "GOAL = (\n", | ||
| " \"Help applicants with admissions, check application completeness, and provide personalized advice.\"\n", | ||
| ")\n", | ||
| "INSTRUCTIONS = (\n", | ||
| " \"Given applicant questions or application data, answer clearly. \"\n", | ||
| " \"If asked to check documents, list which required documents are missing from the provided list. \"\n", | ||
| " \"Always be friendly, helpful, and accurate.\"\n", | ||
| ")" |
There was a problem hiding this comment.
Using triple quotes ("""...""") is more idiomatic and readable in Python for multi-line strings than concatenating separate string literals within parentheses.
ROLE = "AI Enrollment Counselor. Expert in university admissions, document validation, and applicant guidance."
GOAL = "Help applicants with admissions, check application completeness, and provide personalized advice."
INSTRUCTIONS = """Given applicant questions or application data, answer clearly.
If asked to check documents, list which required documents are missing from the provided list.
Always be friendly, helpful, and accurate."""
| "import yaml\n", | ||
| "\n", | ||
| "yaml_prompt =\"\"\"\n", | ||
| "name: Medical Imaging Diagnosis Agent\n", | ||
| "description: An expert agent for analyzing medical images and providing structured diagnostic reports.\n", | ||
| "instructions:\n", | ||
| " - Accept a medical image and analyze it as a radiology expert.\n", | ||
| " - Use image_basic_stats to report basic image statistics.\n", | ||
| " - Structure your response as:\n", | ||
| " 1. Image Type & Region\n", | ||
| " 2. Key Findings\n", | ||
| " 3. Diagnostic Assessment\n", | ||
| " 4. Patient-Friendly Explanation\n", | ||
| " - Use clear markdown headers and bullet points.\n", | ||
| " - Be concise yet thorough.\n", | ||
| "tools:\n", | ||
| " - image_basic_stats\n", | ||
| "model: gpt-4o\n", | ||
| "temperature: 0.3\n", | ||
| "\"\"\"\n" | ||
| ] |
There was a problem hiding this comment.
The yaml_prompt variable is never used later in the notebook, and the associated import yaml is unnecessary. Please remove this cell to improve clarity.
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Actionable comments posted: 9
🧹 Nitpick comments (1)
examples/cookbooks/AI_Enrollment_Counselor.ipynb (1)
47-47: Consider adding version pinning for dependencies.Adding version constraints helps ensure reproducible environments and prevents breaking changes from affecting the notebook.
-!pip install praisonaiagents openai --quiet +!pip install praisonaiagents>=0.1.0 openai>=1.0.0 --quiet
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examples/cookbooks/AI_Enrollment_Counselor.ipynb(1 hunks)examples/cookbooks/medical_image_analysis_agent.ipynb(1 hunks)
🧰 Additional context used
🧠 Learnings (2)
📓 Common learnings
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
examples/cookbooks/AI_Enrollment_Counselor.ipynb (1)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: GitGuardian Security Checks
🔇 Additional comments (4)
examples/cookbooks/AI_Enrollment_Counselor.ipynb (2)
107-118: LGTM! Clear and well-structured agent configuration.The role, goal, and instructions are well-defined and appropriate for an enrollment counselor agent.
419-426: LGTM! Good demonstration of agent capabilities.The examples effectively showcase both document validation and general query functionality.
examples/cookbooks/medical_image_analysis_agent.ipynb (2)
18-21: Excellent medical disclaimer.The disclaimer appropriately emphasizes educational use and the need to consult healthcare professionals for medical decisions.
129-146: YAML prompt structure looks comprehensive.The structured prompt provides clear instructions for medical image analysis with appropriate sections.
| "def ask_enrollment_agent(query, submitted=None, required=None):\n", | ||
| " if submitted and required:\n", | ||
| " prompt = (\n", | ||
| " f\"Applicant submitted documents: {submitted}\\n\"\n", | ||
| " f\"Required documents: {required}\\n\"\n", | ||
| " f\"{query}\\n\"\n", | ||
| " \"List any missing documents and provide guidance.\"\n", | ||
| " )\n", | ||
| " return enrollment_agent.start(prompt)\n", | ||
| " else:\n", | ||
| " return enrollment_agent.start(query)\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider improving error handling and input validation.
The helper function should validate inputs and handle potential errors more gracefully.
def ask_enrollment_agent(query, submitted=None, required=None):
+ if not query or not isinstance(query, str):
+ raise ValueError("Query must be a non-empty string")
+
if submitted and required:
+ if not isinstance(submitted, list) or not isinstance(required, list):
+ raise ValueError("submitted and required must be lists")
prompt = (
f"Applicant submitted documents: {submitted}\\n"
f"Required documents: {required}\\n"
f"{query}\\n"
"List any missing documents and provide guidance."
)
- return enrollment_agent.start(prompt)
+ try:
+ return enrollment_agent.start(prompt)
+ except Exception as e:
+ return f"Error processing request: {str(e)}"
else:
- return enrollment_agent.start(query)
+ try:
+ return enrollment_agent.start(query)
+ except Exception as e:
+ return f"Error processing request: {str(e)}"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "def ask_enrollment_agent(query, submitted=None, required=None):\n", | |
| " if submitted and required:\n", | |
| " prompt = (\n", | |
| " f\"Applicant submitted documents: {submitted}\\n\"\n", | |
| " f\"Required documents: {required}\\n\"\n", | |
| " f\"{query}\\n\"\n", | |
| " \"List any missing documents and provide guidance.\"\n", | |
| " )\n", | |
| " return enrollment_agent.start(prompt)\n", | |
| " else:\n", | |
| " return enrollment_agent.start(query)\n", | |
| def ask_enrollment_agent(query, submitted=None, required=None): | |
| if not query or not isinstance(query, str): | |
| raise ValueError("Query must be a non-empty string") | |
| if submitted and required: | |
| if not isinstance(submitted, list) or not isinstance(required, list): | |
| raise ValueError("submitted and required must be lists") | |
| prompt = ( | |
| f"Applicant submitted documents: {submitted}\n" | |
| f"Required documents: {required}\n" | |
| f"{query}\n" | |
| "List any missing documents and provide guidance." | |
| ) | |
| try: | |
| return enrollment_agent.start(prompt) | |
| except Exception as e: | |
| return f"Error processing request: {str(e)}" | |
| else: | |
| try: | |
| return enrollment_agent.start(query) | |
| except Exception as e: | |
| return f"Error processing request: {str(e)}" |
🤖 Prompt for AI Agents
In examples/cookbooks/AI_Enrollment_Counselor.ipynb around lines 407 to 417, the
function ask_enrollment_agent lacks input validation and error handling. Add
checks to ensure that 'submitted' and 'required' inputs are of expected types
and not empty when provided. Wrap the call to enrollment_agent.start in a
try-except block to catch and handle any exceptions gracefully, returning a
meaningful error message or fallback response.
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\" # <-- Replace with your actual OpenAI API key" |
There was a problem hiding this comment.
Security: Hardcoded API key placeholder is exposed.
The placeholder API key should use a more secure pattern and include clear instructions for users.
-os.environ["OPENAI_API_KEY"] = "sk-..." # <-- Replace with your actual OpenAI API key
+# Replace with your actual OpenAI API key
+# Consider using environment variables: os.getenv("OPENAI_API_KEY")
+if not os.getenv("OPENAI_API_KEY"):
+ os.environ["OPENAI_API_KEY"] = input("Enter your OpenAI API key: ")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\" # <-- Replace with your actual OpenAI API key" | |
| # Replace with your actual OpenAI API key | |
| # Consider using environment variables: os.getenv("OPENAI_API_KEY") | |
| if not os.getenv("OPENAI_API_KEY"): | |
| os.environ["OPENAI_API_KEY"] = input("Enter your OpenAI API key: ") |
🤖 Prompt for AI Agents
In examples/cookbooks/AI_Enrollment_Counselor.ipynb at line 68, the OpenAI API
key is hardcoded as a placeholder string, which is insecure. Replace this with a
pattern that instructs users to set the API key as an environment variable
outside the code, and update the comment to clearly guide users to securely
provide their actual API key without embedding it directly in the code.
| "agent = Agent(\n", | ||
| " name=\"Medical Imaging Diagnosis Agent\",\n", | ||
| " role=\"Expert in medical image analysis and reporting\",\n", | ||
| " instructions=[], # Leave instructions empty, since prompt is passed at runtime\n", | ||
| " tools=[],\n", | ||
| " verbose=True\n", | ||
| ")\n", |
There was a problem hiding this comment.
Agent configuration should include the custom tool.
The agent is configured with empty tools list but the YAML prompt references image_basic_stats tool.
agent = Agent(
name="Medical Imaging Diagnosis Agent",
role="Expert in medical image analysis and reporting",
instructions=[], # Leave instructions empty, since prompt is passed at runtime
- tools=[],
+ tools=[image_basic_stats],
verbose=True
)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "agent = Agent(\n", | |
| " name=\"Medical Imaging Diagnosis Agent\",\n", | |
| " role=\"Expert in medical image analysis and reporting\",\n", | |
| " instructions=[], # Leave instructions empty, since prompt is passed at runtime\n", | |
| " tools=[],\n", | |
| " verbose=True\n", | |
| ")\n", | |
| agent = Agent( | |
| name="Medical Imaging Diagnosis Agent", | |
| role="Expert in medical image analysis and reporting", | |
| instructions=[], # Leave instructions empty, since prompt is passed at runtime | |
| tools=[image_basic_stats], | |
| verbose=True | |
| ) |
🤖 Prompt for AI Agents
In examples/cookbooks/medical_image_analysis_agent.ipynb around lines 782 to
788, the agent is created with an empty tools list, but the YAML prompt expects
the tool named image_basic_stats. Update the tools parameter to include the
image_basic_stats tool so the agent configuration matches the prompt
requirements.
| "OPENAI_API_KEY = \"sk-...\" # <-- Replace with your OpenAI API key\n", | ||
| "\n", | ||
| "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY" |
There was a problem hiding this comment.
Security: Improve API key handling.
The current approach exposes the API key in the notebook. Consider a more secure method.
-# Set your API keys here (replace with your actual keys)
-OPENAI_API_KEY = "sk-..." # <-- Replace with your OpenAI API key
-
-os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+# Set your API keys here (replace with your actual keys)
+# Consider using environment variables or secure input methods
+if not os.getenv("OPENAI_API_KEY"):
+ OPENAI_API_KEY = input("Enter your OpenAI API key: ")
+ os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+else:
+ print("Using API key from environment variable")Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In examples/cookbooks/medical_image_analysis_agent.ipynb around lines 72 to 74,
the API key is hardcoded and exposed directly in the notebook, which is a
security risk. Remove the hardcoded key and instead load the API key securely
from environment variables or a secure vault before setting it in os.environ.
Update the code to prompt the user to set the key externally or use a secure
method like a .env file with a library such as python-dotenv.
| "def image_basic_stats(image_path):\n", | ||
| " img = Image.open(image_path).convert(\"L\")\n", | ||
| " arr = np.array(img)\n", | ||
| " return {\n", | ||
| " \"mean_pixel_value\": float(np.mean(arr)),\n", | ||
| " \"std_pixel_value\": float(np.std(arr)),\n", | ||
| " \"min_pixel_value\": int(np.min(arr)),\n", | ||
| " \"max_pixel_value\": int(np.max(arr)),\n", | ||
| " \"image_shape\": arr.shape\n", | ||
| " }\n", |
There was a problem hiding this comment.
Code duplication: Function defined twice.
The image_basic_stats function is defined both in the Tools section and in the Main section. This creates maintenance issues.
from praisonaiagents.agent import Agent
from PIL import Image
import numpy as np
-def image_basic_stats(image_path):
- img = Image.open(image_path).convert("L")
- arr = np.array(img)
- return {
- "mean_pixel_value": float(np.mean(arr)),
- "std_pixel_value": float(np.std(arr)),
- "min_pixel_value": int(np.min(arr)),
- "max_pixel_value": int(np.max(arr)),
- "image_shape": arr.shape
- }
+# Function already defined in Tools section above📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "def image_basic_stats(image_path):\n", | |
| " img = Image.open(image_path).convert(\"L\")\n", | |
| " arr = np.array(img)\n", | |
| " return {\n", | |
| " \"mean_pixel_value\": float(np.mean(arr)),\n", | |
| " \"std_pixel_value\": float(np.std(arr)),\n", | |
| " \"min_pixel_value\": int(np.min(arr)),\n", | |
| " \"max_pixel_value\": int(np.max(arr)),\n", | |
| " \"image_shape\": arr.shape\n", | |
| " }\n", | |
| from praisonaiagents.agent import Agent | |
| from PIL import Image | |
| import numpy as np | |
| # Function already defined in Tools section above |
🤖 Prompt for AI Agents
In examples/cookbooks/medical_image_analysis_agent.ipynb around lines 730 to
739, the function image_basic_stats is defined twice, causing code duplication
and maintenance problems. Remove one of the duplicate definitions and ensure
that all references use the single remaining function to avoid redundancy.
| "from google.colab import files\n", | ||
| "uploaded = files.upload()\n", | ||
| "image_path = next(iter(uploaded))" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for file upload.
The file upload process should handle cases where no file is uploaded or upload fails.
-from google.colab import files
-uploaded = files.upload()
-image_path = next(iter(uploaded))
+from google.colab import files
+try:
+ uploaded = files.upload()
+ if not uploaded:
+ raise ValueError("No file was uploaded")
+ image_path = next(iter(uploaded))
+ print(f"Successfully uploaded: {image_path}")
+except Exception as e:
+ print(f"Error uploading file: {str(e)}")
+ raise📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "from google.colab import files\n", | |
| "uploaded = files.upload()\n", | |
| "image_path = next(iter(uploaded))" | |
| from google.colab import files | |
| try: | |
| uploaded = files.upload() | |
| if not uploaded: | |
| raise ValueError("No file was uploaded") | |
| image_path = next(iter(uploaded)) | |
| print(f"Successfully uploaded: {image_path}") | |
| except Exception as e: | |
| print(f"Error uploading file: {str(e)}") | |
| raise |
🤖 Prompt for AI Agents
In examples/cookbooks/medical_image_analysis_agent.ipynb around lines 373 to
375, the file upload code does not handle errors or cases where no file is
uploaded. Add error handling to check if the uploaded dictionary is empty or if
the upload fails, and provide appropriate feedback or raise an exception to
handle these cases gracefully.
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!pip install \"praisonaiagents[llm]\" pillow numpy" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider adding version constraints for dependencies.
Version pinning helps ensure reproducible environments, especially important for medical applications.
-!pip install "praisonaiagents[llm]" pillow numpy
+!pip install "praisonaiagents[llm]>=0.1.0" "pillow>=9.0.0" "numpy>=1.21.0"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "!pip install \"praisonaiagents[llm]\" pillow numpy" | |
| !pip install "praisonaiagents[llm]>=0.1.0" "pillow>=9.0.0" "numpy>=1.21.0" |
🤖 Prompt for AI Agents
In examples/cookbooks/medical_image_analysis_agent.ipynb at line 49, the pip
install command lacks version constraints for the dependencies. To ensure
reproducible environments, modify the install command to include specific
version numbers or version ranges for praisonaiagents[llm], pillow, and numpy,
following best practices for version pinning.
| "def image_basic_stats(image_path):\n", | ||
| " \"\"\"Return basic statistics (mean, std) for a medical image.\"\"\"\n", | ||
| " img = Image.open(image_path).convert(\"L\")\n", | ||
| " arr = np.array(img)\n", | ||
| " return {\n", | ||
| " \"mean_pixel_value\": float(np.mean(arr)),\n", | ||
| " \"std_pixel_value\": float(np.std(arr)),\n", | ||
| " \"min_pixel_value\": int(np.min(arr)),\n", | ||
| " \"max_pixel_value\": int(np.max(arr)),\n", | ||
| " \"image_shape\": arr.shape\n", | ||
| " }" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling to image processing function.
The function should handle file format errors, corrupted images, and other potential issues gracefully.
def image_basic_stats(image_path):
"""Return basic statistics (mean, std) for a medical image."""
- img = Image.open(image_path).convert("L")
- arr = np.array(img)
- return {
- "mean_pixel_value": float(np.mean(arr)),
- "std_pixel_value": float(np.std(arr)),
- "min_pixel_value": int(np.min(arr)),
- "max_pixel_value": int(np.max(arr)),
- "image_shape": arr.shape
- }
+ try:
+ img = Image.open(image_path).convert("L")
+ arr = np.array(img)
+
+ # Validate image dimensions
+ if arr.size == 0:
+ raise ValueError("Image appears to be empty")
+
+ return {
+ "mean_pixel_value": float(np.mean(arr)),
+ "std_pixel_value": float(np.std(arr)),
+ "min_pixel_value": int(np.min(arr)),
+ "max_pixel_value": int(np.max(arr)),
+ "image_shape": arr.shape
+ }
+ except Exception as e:
+ raise ValueError(f"Error processing image: {str(e)}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "def image_basic_stats(image_path):\n", | |
| " \"\"\"Return basic statistics (mean, std) for a medical image.\"\"\"\n", | |
| " img = Image.open(image_path).convert(\"L\")\n", | |
| " arr = np.array(img)\n", | |
| " return {\n", | |
| " \"mean_pixel_value\": float(np.mean(arr)),\n", | |
| " \"std_pixel_value\": float(np.std(arr)),\n", | |
| " \"min_pixel_value\": int(np.min(arr)),\n", | |
| " \"max_pixel_value\": int(np.max(arr)),\n", | |
| " \"image_shape\": arr.shape\n", | |
| " }" | |
| def image_basic_stats(image_path): | |
| """Return basic statistics (mean, std) for a medical image.""" | |
| try: | |
| img = Image.open(image_path).convert("L") | |
| arr = np.array(img) | |
| # Validate image dimensions | |
| if arr.size == 0: | |
| raise ValueError("Image appears to be empty") | |
| return { | |
| "mean_pixel_value": float(np.mean(arr)), | |
| "std_pixel_value": float(np.std(arr)), | |
| "min_pixel_value": int(np.min(arr)), | |
| "max_pixel_value": int(np.max(arr)), | |
| "image_shape": arr.shape | |
| } | |
| except Exception as e: | |
| raise ValueError(f"Error processing image: {str(e)}") |
🤖 Prompt for AI Agents
In examples/cookbooks/medical_image_analysis_agent.ipynb around lines 97 to 107,
the image_basic_stats function lacks error handling for issues like unsupported
file formats or corrupted images. Wrap the image loading and processing code in
a try-except block to catch exceptions such as IOError or OSError. In the except
block, handle the error gracefully by logging an appropriate message or
returning a clear error indicator instead of letting the function fail silently
or crash.
| "stats = image_basic_stats(image_path)\n", | ||
| "print(\"Image basic stats:\", stats)\n", | ||
| "\n", | ||
| "prompt = f\"\"\"\n", | ||
| "You are a highly skilled medical imaging expert with extensive knowledge in radiology and diagnostic imaging.\n", | ||
| "Analyze the uploaded medical image and structure your response as follows:\n", | ||
| "\n", | ||
| "### 1. Image Type & Region\n", | ||
| "- Specify imaging modality (X-ray/MRI/CT/Ultrasound/etc.)\n", | ||
| "- Identify the patient's anatomical region and positioning\n", | ||
| "- Comment on image quality and technical adequacy\n", | ||
| "\n", | ||
| "### 2. Key Findings\n", | ||
| "- List primary observations systematically\n", | ||
| "- Note any abnormalities with precise descriptions\n", | ||
| "- Include measurements and densities where relevant\n", | ||
| "- Describe location, size, shape, and characteristics\n", | ||
| "- Rate severity: Normal/Mild/Moderate/Severe\n", | ||
| "\n", | ||
| "### 3. Diagnostic Assessment\n", | ||
| "- Provide primary diagnosis with confidence level\n", | ||
| "- List differential diagnoses in order of likelihood\n", | ||
| "- Support each diagnosis with observed evidence\n", | ||
| "- Note any critical or urgent findings\n", | ||
| "\n", | ||
| "### 4. Patient-Friendly Explanation\n", | ||
| "- Explain the findings in simple, clear language\n", | ||
| "- Avoid medical jargon or provide clear definitions\n", | ||
| "- Include visual analogies if helpful\n", | ||
| "- Address common patient concerns\n", | ||
| "\n", | ||
| "#### Image Basic Stats\n", | ||
| "- Mean pixel value: {stats['mean_pixel_value']}\n", | ||
| "- Std pixel value: {stats['std_pixel_value']}\n", | ||
| "- Min pixel value: {stats['min_pixel_value']}\n", | ||
| "- Max pixel value: {stats['max_pixel_value']}\n", | ||
| "- Image shape: {stats['image_shape']}\n", | ||
| "\n", | ||
| "Format your response using clear markdown headers and bullet points. Be concise yet thorough.\n", | ||
| "\"\"\"\n", | ||
| "\n", | ||
| "agent = Agent(\n", | ||
| " name=\"Medical Imaging Diagnosis Agent\",\n", | ||
| " role=\"Expert in medical image analysis and reporting\",\n", | ||
| " instructions=[], # Leave instructions empty, since prompt is passed at runtime\n", | ||
| " tools=[],\n", | ||
| " verbose=True\n", | ||
| ")\n", | ||
| "\n", | ||
| "# Pass the prompt as an argument to start()\n", | ||
| "response = agent.start(prompt)\n", | ||
| "print(\"\\n=== Analysis Results ===\\n\")\n", | ||
| "print(response)" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add comprehensive error handling to main execution.
The main execution block should handle various error scenarios and provide helpful error messages.
-stats = image_basic_stats(image_path)
-print("Image basic stats:", stats)
+try:
+ stats = image_basic_stats(image_path)
+ print("Image basic stats:", stats)
+except Exception as e:
+ print(f"Error computing image statistics: {str(e)}")
+ raise
prompt = f"""
You are a highly skilled medical imaging expert with extensive knowledge in radiology and diagnostic imaging.
Analyze the uploaded medical image and structure your response as follows:
+**IMPORTANT DISCLAIMER**: This analysis is for educational purposes only and should not be used for actual medical diagnosis. Always consult qualified healthcare professionals for medical decisions.
+
### 1. Image Type & Region-# Pass the prompt as an argument to start()
-response = agent.start(prompt)
-print("\\n=== Analysis Results ===\\n")
-print(response)
+# Pass the prompt as an argument to start()
+try:
+ response = agent.start(prompt)
+ print("\\n=== Analysis Results ===\\n")
+ print(response)
+except Exception as e:
+ print(f"Error generating analysis: {str(e)}")
+ print("Please check your API key and try again.")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "stats = image_basic_stats(image_path)\n", | |
| "print(\"Image basic stats:\", stats)\n", | |
| "\n", | |
| "prompt = f\"\"\"\n", | |
| "You are a highly skilled medical imaging expert with extensive knowledge in radiology and diagnostic imaging.\n", | |
| "Analyze the uploaded medical image and structure your response as follows:\n", | |
| "\n", | |
| "### 1. Image Type & Region\n", | |
| "- Specify imaging modality (X-ray/MRI/CT/Ultrasound/etc.)\n", | |
| "- Identify the patient's anatomical region and positioning\n", | |
| "- Comment on image quality and technical adequacy\n", | |
| "\n", | |
| "### 2. Key Findings\n", | |
| "- List primary observations systematically\n", | |
| "- Note any abnormalities with precise descriptions\n", | |
| "- Include measurements and densities where relevant\n", | |
| "- Describe location, size, shape, and characteristics\n", | |
| "- Rate severity: Normal/Mild/Moderate/Severe\n", | |
| "\n", | |
| "### 3. Diagnostic Assessment\n", | |
| "- Provide primary diagnosis with confidence level\n", | |
| "- List differential diagnoses in order of likelihood\n", | |
| "- Support each diagnosis with observed evidence\n", | |
| "- Note any critical or urgent findings\n", | |
| "\n", | |
| "### 4. Patient-Friendly Explanation\n", | |
| "- Explain the findings in simple, clear language\n", | |
| "- Avoid medical jargon or provide clear definitions\n", | |
| "- Include visual analogies if helpful\n", | |
| "- Address common patient concerns\n", | |
| "\n", | |
| "#### Image Basic Stats\n", | |
| "- Mean pixel value: {stats['mean_pixel_value']}\n", | |
| "- Std pixel value: {stats['std_pixel_value']}\n", | |
| "- Min pixel value: {stats['min_pixel_value']}\n", | |
| "- Max pixel value: {stats['max_pixel_value']}\n", | |
| "- Image shape: {stats['image_shape']}\n", | |
| "\n", | |
| "Format your response using clear markdown headers and bullet points. Be concise yet thorough.\n", | |
| "\"\"\"\n", | |
| "\n", | |
| "agent = Agent(\n", | |
| " name=\"Medical Imaging Diagnosis Agent\",\n", | |
| " role=\"Expert in medical image analysis and reporting\",\n", | |
| " instructions=[], # Leave instructions empty, since prompt is passed at runtime\n", | |
| " tools=[],\n", | |
| " verbose=True\n", | |
| ")\n", | |
| "\n", | |
| "# Pass the prompt as an argument to start()\n", | |
| "response = agent.start(prompt)\n", | |
| "print(\"\\n=== Analysis Results ===\\n\")\n", | |
| "print(response)" | |
| try: | |
| stats = image_basic_stats(image_path) | |
| print("Image basic stats:", stats) | |
| except Exception as e: | |
| print(f"Error computing image statistics: {str(e)}") | |
| raise | |
| prompt = f""" | |
| You are a highly skilled medical imaging expert with extensive knowledge in radiology and diagnostic imaging. | |
| Analyze the uploaded medical image and structure your response as follows: | |
| **IMPORTANT DISCLAIMER**: This analysis is for educational purposes only and should not be used for actual medical diagnosis. Always consult qualified healthcare professionals for medical decisions. | |
| ### 1. Image Type & Region | |
| - Specify imaging modality (X-ray/MRI/CT/Ultrasound/etc.) | |
| - Identify the patient's anatomical region and positioning | |
| - Comment on image quality and technical adequacy | |
| ### 2. Key Findings | |
| - List primary observations systematically | |
| - Note any abnormalities with precise descriptions | |
| - Include measurements and densities where relevant | |
| - Describe location, size, shape, and characteristics | |
| - Rate severity: Normal/Mild/Moderate/Severe | |
| ### 3. Diagnostic Assessment | |
| - Provide primary diagnosis with confidence level | |
| - List differential diagnoses in order of likelihood | |
| - Support each diagnosis with observed evidence | |
| - Note any critical or urgent findings | |
| ### 4. Patient-Friendly Explanation | |
| - Explain the findings in simple, clear language | |
| - Avoid medical jargon or provide clear definitions | |
| - Include visual analogies if helpful | |
| - Address common patient concerns | |
| #### Image Basic Stats | |
| - Mean pixel value: {stats['mean_pixel_value']} | |
| - Std pixel value: {stats['std_pixel_value']} | |
| - Min pixel value: {stats['min_pixel_value']} | |
| - Max pixel value: {stats['max_pixel_value']} | |
| - Image shape: {stats['image_shape']} | |
| Format your response using clear markdown headers and bullet points. Be concise yet thorough. | |
| """ | |
| agent = Agent( | |
| name="Medical Imaging Diagnosis Agent", | |
| role="Expert in medical image analysis and reporting", | |
| instructions=[], # Leave instructions empty, since prompt is passed at runtime | |
| tools=[], | |
| verbose=True | |
| ) | |
| # Pass the prompt as an argument to start() | |
| try: | |
| response = agent.start(prompt) | |
| print("\n=== Analysis Results ===\n") | |
| print(response) | |
| except Exception as e: | |
| print(f"Error generating analysis: {str(e)}") | |
| print("Please check your API key and try again.") |
🤖 Prompt for AI Agents
In examples/cookbooks/medical_image_analysis_agent.ipynb around lines 741 to
793, the main execution code lacks error handling, which can cause unhandled
exceptions and unclear failures. Wrap the main execution logic, including image
stats calculation, prompt creation, agent initialization, and agent start call,
in a try-except block. Catch general exceptions and print or log clear,
user-friendly error messages to help diagnose issues during runtime.
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #742 +/- ##
=======================================
Coverage 14.23% 14.23%
=======================================
Files 25 25
Lines 2571 2571
Branches 367 367
=======================================
Hits 366 366
Misses 2189 2189
Partials 16 16
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
|
|
@Dhivya-Bharathy Can you please review the conflict ? |
2 participants
User description
This notebook provides a generic medical image analysis agent using PraisonAI Agents.
It uploads a medical image, computes basic image statistics, and generates a structured diagnostic report using an LLM.
The solution is fully Colab-compatible and does not require any manual tool-calling or file path adjustments.
PR Type
Enhancement
Description
Add medical image analysis agent notebook with diagnostic capabilities
Add AI enrollment counselor agent for university admissions automation
Add intelligent travel planning agent for trip recommendations
Changes diagram
Changes walkthrough 📝
medical_image_analysis_agent.ipynb
Medical imaging diagnosis agent implementationexamples/cookbooks/medical_image_analysis_agent.ipynb
AI_Enrollment_Counselor.ipynb
University enrollment counselor agentexamples/cookbooks/AI_Enrollment_Counselor.ipynb
intelligent_travel_planning_agent.ipynb
Travel planning agent implementationexamples/cookbooks/intelligent_travel_planning_agent.ipynb
Summary by CodeRabbit