project - ai medical imaging diagnosis agent
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ai_agent_tutorials/ai_medical_imaging_agent/README.md
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ai_agent_tutorials/ai_medical_imaging_agent/README.md
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# Medical Imaging Diagnosis Agent
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A Medical Imaging Diagnosis Agent build on phidata powered by Gemini 2.0 Flash Experimental that provides AI-assisted analysis of medical images of various scans. The agent acts as a medical imaging diagnosis expert to analyze various types of medical images and videos, providing detailed diagnostic insights and explanations.
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## Features
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- **Comprehensive Image Analysis**
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- Image Type Identification (X-ray, MRI, CT scan, ultrasound)
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- Anatomical Region Detection
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- Key Findings and Observations
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- Potential Abnormalities Detection
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- Image Quality Assessment
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- Severity Assessment
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## How to Run
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1. **Setup Environment**
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```bash
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# Clone the repository
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd ai_medical_diagnosis_agent
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# Install dependencies
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pip install -r requirements.txt
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```
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2. **Configure API Keys**
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- Get Google API key from [Google AI Studio](https://aistudio.google.com)
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3. **Run the Application**
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```bash
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streamlit run medical_image_diagnosis.py
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```
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## Analysis Components
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- **Image Type and Region**
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- Identifies imaging modality
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- Specifies anatomical region
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- **Key Findings**
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- Systematic listing of observations
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- Detailed appearance descriptions
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- Abnormality highlighting
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- **Diagnostic Assessment**
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- Potential diagnoses ranking
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- Differential diagnoses
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- Severity assessment
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- **Patient-Friendly Explanations**
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- Simplified terminology
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- Detailed first-principles explanations
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- Visual reference points
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## Notes
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- Uses Gemini 2.0 Flash for analysis
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- Requires stable internet connection
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- API usage costs apply
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- For educational and development purposes only
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- Not a replacement for professional medical diagnosis
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## Disclaimer
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This tool is for educational and informational purposes only. All analyses should be reviewed by qualified healthcare professionals. Do not make medical decisions based solely on this analysis.
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import os
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from PIL import Image
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from phi.agent import Agent
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from phi.model.google import Gemini
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import streamlit as st
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from phi.tools.duckduckgo import DuckDuckGo
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if "GOOGLE_API_KEY" not in st.session_state:
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st.session_state.GOOGLE_API_KEY = None
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with st.sidebar:
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st.title("ℹ️ Configuration")
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if not st.session_state.GOOGLE_API_KEY:
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api_key = st.text_input(
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"Enter your Google API Key:",
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type="password"
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)
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st.caption(
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"Get your API key from [Google AI Studio]"
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"(https://aistudio.google.com/apikey) 🔑"
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)
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if api_key:
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st.session_state.GOOGLE_API_KEY = api_key
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st.success("API Key saved!")
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st.rerun()
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else:
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st.success("API Key is configured")
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if st.button("🔄 Reset API Key"):
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st.session_state.GOOGLE_API_KEY = None
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st.rerun()
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st.info(
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"This tool provides AI-powered analysis of medical imaging data using "
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"advanced computer vision and radiological expertise."
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)
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st.warning(
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"⚠DISCLAIMER: This tool is for educational and informational purposes only. "
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"All analyses should be reviewed by qualified healthcare professionals. "
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"Do not make medical decisions based solely on this analysis."
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)
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medical_agent = Agent(
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model=Gemini(
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api_key=st.session_state.GOOGLE_API_KEY,
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id="gemini-2.0-flash-exp"
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),
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tools=[DuckDuckGo()],
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markdown=True
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) if st.session_state.GOOGLE_API_KEY else None
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if not medical_agent:
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st.warning("Please configure your API key in the sidebar to continue")
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# Medical Analysis Query
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query = """
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You are a highly skilled medical imaging expert with extensive knowledge in radiology and diagnostic imaging. Analyze the patient's medical image and structure your response as follows:
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### 1. Image Type & Region
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- Specify imaging modality (X-ray/MRI/CT/Ultrasound/etc.)
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- Identify the patient's anatomical region and positioning
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- Comment on image quality and technical adequacy
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### 2. Key Findings
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- List primary observations systematically
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- Note any abnormalities in the patient's imaging with precise descriptions
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- Include measurements and densities where relevant
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- Describe location, size, shape, and characteristics
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- Rate severity: Normal/Mild/Moderate/Severe
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### 3. Diagnostic Assessment
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- Provide primary diagnosis with confidence level
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- List differential diagnoses in order of likelihood
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- Support each diagnosis with observed evidence from the patient's imaging
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- Note any critical or urgent findings
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### 4. Patient-Friendly Explanation
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- Explain the findings in simple, clear language that the patient can understand
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- Avoid medical jargon or provide clear definitions
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- Include visual analogies if helpful
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- Address common patient concerns related to these findings
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### 5. Research Context
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IMPORTANT: Use the DuckDuckGo search tool to:
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- Find recent medical literature about similar cases
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- Search for standard treatment protocols
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- Provide a list of relevant medical links of them too
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- Research any relevant technological advances
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- Include 2-3 key references to support your analysis
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Format your response using clear markdown headers and bullet points. Be concise yet thorough.
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"""
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st.title("🏥 Medical Imaging Diagnosis Agent")
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st.write("Upload a medical image for professional analysis")
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# Create containers for better organization
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upload_container = st.container()
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image_container = st.container()
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analysis_container = st.container()
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with upload_container:
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uploaded_file = st.file_uploader(
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"Upload Medical Image",
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type=["jpg", "jpeg", "png", "dicom"],
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help="Supported formats: JPG, JPEG, PNG, DICOM"
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)
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if uploaded_file is not None:
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with image_container:
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# Center the image using columns
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col1, col2, col3 = st.columns([1, 2, 1])
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with col2:
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image = Image.open(uploaded_file)
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# Calculate aspect ratio for resizing
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width, height = image.size
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aspect_ratio = width / height
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new_width = 500
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new_height = int(new_width / aspect_ratio)
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resized_image = image.resize((new_width, new_height))
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st.image(
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resized_image,
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caption="Uploaded Medical Image",
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use_container_width=True
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)
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analyze_button = st.button(
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"🔍 Analyze Image",
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type="primary",
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use_container_width=True
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)
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with analysis_container:
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if analyze_button:
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image_path = "temp_medical_image.png"
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with open(image_path, "wb") as f:
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f.write(uploaded_file.getbuffer())
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with st.spinner("🔄 Analyzing image... Please wait."):
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try:
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response = medical_agent.run(query, images=[image_path])
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st.markdown("### 📋 Analysis Results")
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st.markdown("---")
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st.markdown(response.content)
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st.markdown("---")
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st.caption(
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"Note: This analysis is generated by AI and should be reviewed by "
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"a qualified healthcare professional."
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)
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except Exception as e:
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st.error(f"Analysis error: {e}")
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finally:
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if os.path.exists(image_path):
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os.remove(image_path)
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else:
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st.info("👆 Please upload a medical image to begin analysis")
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streamlit==1.40.2
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phidata==2.7.3
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Pillow==10.0.0
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duckduckgo-search==6.4.1
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google-generativeai==0.8.3
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