Update app.py

app.py

@@ -1,121 +1,121 @@
-import os
-from PIL import Image as PILImage
-from agno.agent import Agent
-from agno.models.google import Gemini
-from agno.tools.duckduckgo import DuckDuckGoTools
-from agno.media import Image as AgnoImage
-import gradio as gr
-
-# Set your API Key (Replace with your actual key)
-GOOGLE_API_KEY = "AIzaSyCUPYFW5ESk1YIwSjwpGd3jZJJ7oPAX4_s"
-os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
-
-# Ensure API Key is provided
-if not GOOGLE_API_KEY:
-    raise ValueError("⚠️ Please set your Google API Key in GOOGLE_API_KEY")
-
-# Initialize the Medical Agent
-medical_agent = Agent(
-    model=Gemini(id="gemini-2.0-flash-exp"),
-    tools=[DuckDuckGoTools()],
-    markdown=True
-)
-
-# Medical Analysis Query
-query = """
-You are a highly skilled medical imaging expert with extensive knowledge in radiology and diagnostic imaging. Analyze the medical image and structure your response as follows:
-
-### 1. Image Type & Region
-- Identify imaging modality (X-ray/MRI/CT/Ultrasound/etc.).
-- Specify anatomical region and positioning.
-- Evaluate image quality and technical adequacy.
-
-### 2. Key Findings
-- Highlight primary observations systematically.
-- Identify potential abnormalities with detailed descriptions.
-- Include measurements and densities where relevant.
-
-### 3. Diagnostic Assessment
-- Provide primary diagnosis with confidence level.
-- List differential diagnoses ranked by likelihood.
-- Support each diagnosis with observed evidence.
-- Highlight critical/urgent findings.
-
-### 4. Patient-Friendly Explanation
-- Simplify findings in clear, non-technical language.
-- Avoid medical jargon or provide easy definitions.
-- Include relatable visual analogies.
-
-### 5. Research Context
-- Use DuckDuckGo search to find recent medical literature.
-- Search for standard treatment protocols.
-- Provide 2-3 key references supporting the analysis.
-
-Ensure a structured and medically accurate response using clear markdown formatting.
-"""
-
-# Function to analyze medical image
-def analyze_medical_image(image):
-    """Processes and analyzes a medical image using AI."""
-    if image is None:
-        return "⚠️ Please upload an image to analyze."
-
-    # Save the input image to a temporary file
-    temp_path = "temp_image.png"
-    image.save(temp_path)
-
-    # Create AgnoImage object
-    agno_image = AgnoImage(filepath=temp_path)
-
-    # Run AI analysis
-    try:
-        response = medical_agent.run(query, images=[agno_image])
-        return response.content
-    except Exception as e:
-        return f"⚠️ Analysis error: {e}"
-    finally:
-        # Clean up temporary file
-        if os.path.exists(temp_path):
-            os.remove(temp_path)
-
-# Create Gradio interface
-with gr.Blocks(title="Medical Image Analysis") as demo:
-    gr.Markdown(
-        """
-        # 🩺 Medical Image Analysis Tool 🔬
-
-        Welcome to the **Medical Image Analysis** tool! 📸
-
-        Upload a medical image (X-ray, MRI, CT, Ultrasound, etc.), and our AI-powered system will analyze it,
-        providing detailed findings, diagnosis, and research insights.
-
-        Let's get started!
-        """
-    )
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            input_image = gr.Image(type="pil", label="Upload Medical Image")
-            analyze_button = gr.Button("Analyze Image", variant="primary")
-
-        with gr.Column(scale=2):
-            output_report = gr.Markdown(label="Analysis Report")
-
-    analyze_button.click(
-        fn=analyze_medical_image,
-        inputs=input_image,
-        outputs=output_report
-    )
-
-    gr.Examples(
-        examples=[
-            "examples/xray_chest.jpg",
-            "examples/mri_brain.jpg",
-            "examples/ct_abdomen.jpg"
-        ],
-        inputs=input_image
-    )
-
-# Launch the application
-if __name__ == "__main__":
+import os
+from PIL import Image as PILImage
+from agno.agent import Agent
+from agno.models.google import Gemini
+from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.media import Image as AgnoImage
+import gradio as gr
+
+# Set your API Key (Replace with your actual key)
+GOOGLE_API_KEY = "AIzaSyCUPYFW5ESk1YIwSjwpGd3jZJJ7oPAX4_s"
+os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
+
+# Ensure API Key is provided
+if not GOOGLE_API_KEY:
+    raise ValueError("⚠️ Please set your Google API Key in GOOGLE_API_KEY")
+
+# Initialize the Medical Agent
+medical_agent = Agent(
+    model=Gemini(id="gemini-2.0-flash-exp"),
+    tools=[DuckDuckGoTools()],
+    markdown=True
+)
+
+# Medical Analysis Query
+query = """
+You are a highly skilled medical imaging expert with extensive knowledge in radiology and diagnostic imaging. Analyze the medical image and structure your response as follows:
+
+### 1. Image Type & Region
+- Identify imaging modality (X-ray/MRI/CT/Ultrasound/etc.).
+- Specify anatomical region and positioning.
+- Evaluate image quality and technical adequacy.
+
+### 2. Key Findings
+- Highlight primary observations systematically.
+- Identify potential abnormalities with detailed descriptions.
+- Include measurements and densities where relevant.
+
+### 3. Diagnostic Assessment
+- Provide primary diagnosis with confidence level.
+- List differential diagnoses ranked by likelihood.
+- Support each diagnosis with observed evidence.
+- Highlight critical/urgent findings.
+
+### 4. Patient-Friendly Explanation
+- Simplify findings in clear, non-technical language.
+- Avoid medical jargon or provide easy definitions.
+- Include relatable visual analogies.
+
+### 5. Research Context
+- Use DuckDuckGo search to find recent medical literature.
+- Search for standard treatment protocols.
+- Provide 2-3 key references supporting the analysis.
+
+Ensure a structured and medically accurate response using clear markdown formatting.
+"""
+
+# Function to analyze medical image
+def analyze_medical_image(image):
+    """Processes and analyzes a medical image using AI."""
+    if image is None:
+        return "⚠️ Please upload an image to analyze."
+
+    # Save the input image to a temporary file
+    temp_path = "temp_image.png"
+    image.save(temp_path)
+
+    # Create AgnoImage object
+    agno_image = AgnoImage(filepath=temp_path)
+
+    # Run AI analysis
+    try:
+        response = medical_agent.run(query, images=[agno_image])
+        return response.content
+    except Exception as e:
+        return f"⚠️ Analysis error: {e}"
+    finally:
+        # Clean up temporary file
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+
+# Create Gradio interface
+with gr.Blocks(title="Medical Image Analysis") as demo:
+    gr.Markdown(
+        """
+        # 🩺 Medical Image Analysis Tool 🔬
+
+        Welcome to the **Medical Image Analysis** tool! 📸
+
+        Upload a medical image (X-ray, MRI, CT, Ultrasound, etc.), and our AI-powered system will analyze it,
+        providing detailed findings, diagnosis, and research insights.
+
+        Let's get started!
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_image = gr.Image(type="pil", label="Upload Medical Image")
+            analyze_button = gr.Button("Analyze Image", variant="primary")
+
+        with gr.Column(scale=2):
+            output_report = gr.Markdown(label="Analysis Report")
+
+    analyze_button.click(
+        fn=analyze_medical_image,
+        inputs=input_image,
+        outputs=output_report
+    )
+
+    # gr.Examples(
+    #     examples=[
+    #         "examples/xray_chest.jpg",
+    #         "examples/mri_brain.jpg",
+    #         "examples/ct_abdomen.jpg"
+    #     ],
+    #     inputs=input_image
+    # )
+
+# Launch the application
+if __name__ == "__main__":
     demo.launch(debug=True, share=True)