Upload app.py

app.py

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+import os
+import gradio as gr
+import google.generativeai as genai
+
+# Configure Gemini API
+GEMINI_API_KEY = "AIzaSyDooG1UD_7ae5lgl7HwfsVyGlwn2XycXko"
+genai.configure(api_key=GEMINI_API_KEY)
+
+# Create the model with the same configuration as the sample
+generation_config = {
+    "temperature": 1,
+    "top_p": 0.95,
+    "top_k": 40,
+    "max_output_tokens": 8192,
+    "response_mime_type": "text/plain",
+}
+
+model = genai.GenerativeModel(
+    model_name="gemini-1.5-pro",
+    generation_config=generation_config,
+    system_instruction="You are an expert in detecting objects from xray image. Your job is to detect the objects from x-ray images.",
+)
+
+def analyze_image(image_path):
+    """Analyze the uploaded image using Gemini model"""
+    try:
+        # Start a new chat session
+        chat = model.start_chat()
+        
+        # Upload and analyze the image
+        image_file = genai.upload_file(image_path.name)
+        
+        # Send the image with a prompt
+        response = chat.send_message([
+            image_file,
+            "Here is an xray image, describe all objects you can see in this image and their relative positions to each other."
+        ])
+        
+        return response.text
+    except Exception as e:
+        return f"Error analyzing image: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=analyze_image,
+    inputs=gr.File(label="Upload X-ray Image"),
+    outputs=gr.Textbox(label="Analysis Result", lines=10),
+    title="X-ray Image Object Detection",
+    description="Upload an X-ray image and get a detailed description of the objects detected in it.",
+    examples=[],
+    cache_examples=False
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()