newapp.py

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+from PIL import Image
+import io
+import base64
+import requests  # For making API requests
+
+# Function to encode image as base64
+def image_to_base64(image):
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
+    return img_str
+
+# Function to interact with LLAVA model
+def chat_with_llava(image, question):
+    try:
+        # Convert image to base64
+        image_b64 = image_to_base64(image)
+
+        # Crafting a prompt to instruct the model to respond as a doctor
+        doctor_prompt = (
+            "You are a highly experienced and knowledgeable medical doctor. "
+            "Please analyze the provided medical image and give a detailed medical explanation in response to the following question.\n\n"
+            f"Question: {question}\n"
+            "Please include relevant medical terminology and explanations in your response."
+        )
+
+        # URL for Ollama chat API endpoint
+        api_url = "https://api.ollama.com/chat"
+        
+        # Prepare headers with API key (replace 'YOUR_API_KEY' with your actual API key)
+        headers = {
+            "Authorization": "Bearer YOUR_API_KEY",
+            "Content-Type": "application/json"
+        }
+
+        # Prepare payload for the API request
+        payload = {
+            "model": "rohithbojja/llava-med-v1.6",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": doctor_prompt,
+                    "image": image_b64
+                }
+            ]
+        }
+
+        # Make POST request to Ollama chat API
+        response = requests.post(api_url, json=payload, headers=headers)
+        response_data = response.json()
+
+        # Extract and return model response
+        return response_data['message']['content']
+
+    except Exception as e:
+        return f"Error occurred: {str(e)}"
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=chat_with_llava,
+    inputs=[
+        gr.inputs.Image(type="pil", label="Upload Medical Image"),
+        gr.inputs.Textbox(lines=2, label="Ask a medical question about the image")
+    ],
+    outputs=gr.outputs.Textbox(label="Response", placeholder="Model response will appear here..."),
+    title="LLAVA Model - Medical Image and Question",
+    description="Upload a medical image and ask a specific question about the image for a medical description."
+)
+
+# Launch the Gradio interface
+if __name__ == "__main__":
+    iface.launch()