Create app.py

app.py

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+import os
+from PIL import Image
+from phi.agent import Agent
+from phi.model.google import Gemini
+import streamlit as st
+from phi.tools.duckduckgo import DuckDuckGo
+
+if "GOOGLE_API_KEY" not in st.session_state:
+    st.session_state.GOOGLE_API_KEY = None
+
+with st.sidebar:
+    st.title("ℹ️ Configuration")
+    
+    if not st.session_state.GOOGLE_API_KEY:
+        api_key = st.text_input(
+            "Enter your Google API Key:",
+            type="password"
+        )
+        st.caption(
+            "Get your API key from [Google AI Studio]"
+            "(https://aistudio.google.com/apikey) 🔑"
+        )
+        if api_key:
+            st.session_state.GOOGLE_API_KEY = api_key
+            st.success("API Key saved!")
+            st.rerun()
+    else:
+        st.success("API Key is configured")
+        if st.button("🔄 Reset API Key"):
+            st.session_state.GOOGLE_API_KEY = None
+            st.rerun()
+    
+    st.info(
+        "This tool provides AI-powered analysis of medical imaging data using "
+        "advanced computer vision and radiological expertise."
+    )
+    st.warning(
+        "⚠DISCLAIMER: 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."
+    )
+
+medical_agent = Agent(
+    model=Gemini(
+        api_key=st.session_state.GOOGLE_API_KEY,
+        id="gemini-2.0-flash-exp"
+    ),
+    tools=[DuckDuckGo()],
+    markdown=True
+) if st.session_state.GOOGLE_API_KEY else None
+
+if not medical_agent:
+    st.warning("Please configure your API key in the sidebar to continue")
+
+# Medical Analysis Query
+query = """
+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:
+
+### 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 in the patient's imaging 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 from the patient's imaging
+- Note any critical or urgent findings
+
+### 4. Patient-Friendly Explanation
+- Explain the findings in simple, clear language that the patient can understand
+- Avoid medical jargon or provide clear definitions
+- Include visual analogies if helpful
+- Address common patient concerns related to these findings
+
+### 5. Research Context
+IMPORTANT: Use the DuckDuckGo search tool to:
+- Find recent medical literature about similar cases
+- Search for standard treatment protocols
+- Provide a list of relevant medical links of them too
+- Research any relevant technological advances
+- Include 2-3 key references to support your analysis
+
+Format your response using clear markdown headers and bullet points. Be concise yet thorough.
+"""
+
+st.title("🏥 Medical Imaging Diagnosis Agent")
+st.write("Upload a medical image for professional analysis")
+
+# Create containers for better organization
+upload_container = st.container()
+image_container = st.container()
+analysis_container = st.container()
+
+with upload_container:
+    uploaded_file = st.file_uploader(
+        "Upload Medical Image",
+        type=["jpg", "jpeg", "png", "dicom"],
+        help="Supported formats: JPG, JPEG, PNG, DICOM"
+    )
+
+if uploaded_file is not None:
+    with image_container:
+        # Center the image using columns
+        col1, col2, col3 = st.columns([1, 2, 1])
+        with col2:
+            image = Image.open(uploaded_file)
+            # Calculate aspect ratio for resizing
+            width, height = image.size
+            aspect_ratio = width / height
+            new_width = 500
+            new_height = int(new_width / aspect_ratio)
+            resized_image = image.resize((new_width, new_height))
+            
+            st.image(
+                resized_image,
+                caption="Uploaded Medical Image",
+                use_container_width=True
+            )
+            
+            analyze_button = st.button(
+                "🔍 Analyze Image",
+                type="primary",
+                use_container_width=True
+            )
+    
+    with analysis_container:
+        if analyze_button:
+            image_path = "temp_medical_image.png"
+            with open(image_path, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+            
+            with st.spinner("🔄 Analyzing image... Please wait."):
+                try:
+                    response = medical_agent.run(query, images=[image_path])
+                    st.markdown("### 📋 Analysis Results")
+                    st.markdown("---")
+                    st.markdown(response.content)
+                    st.markdown("---")
+                    st.caption(
+                        "Note: This analysis is generated by AI and should be reviewed by "
+                        "a qualified healthcare professional."
+                    )
+                except Exception as e:
+                    st.error(f"Analysis error: {e}")
+                finally:
+                    if os.path.exists(image_path):
+                        os.remove(image_path)
+else:
+    st.info("👆 Please upload a medical image to begin analysis")