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app.py

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+import os
+import gradio as gr
+from argparse import ArgumentParser
+from groq import Groq
+from PIL import Image
+import base64
+import io
+
+# Initialize Groq client
+API_KEY = os.environ['GROQ_API_KEY']
+client = Groq(api_key=API_KEY)
+
+REVISION = 'v1.0.4'
+
+def _get_args():
+    parser = ArgumentParser()
+    parser.add_argument("--revision", type=str, default=REVISION)
+    parser.add_argument("--share", action="store_true", default=False, help="Create a publicly shareable link for the interface.")
+    return parser.parse_args()
+
+def process_image(image):
+    # Convert image to bytes for Groq API
+    buffered = io.BytesIO()
+    image.save(buffered, format="JPEG")
+    return buffered.getvalue()
+
+def create_messages(query, image_data):
+    messages = []
+    
+    # User query as text
+    if query:
+        messages.append({'role': 'user', 'content': query})
+
+    # Include image if provided
+    if image_data:
+        image_base64 = f"data:image/jpeg;base64,{base64.b64encode(image_data).decode()}"
+        messages.append({
+            'role': 'user',
+            'content': [
+                {"type": "text", "text": "Please analyze this image."},
+                {"type": "image_url", "image_url": {"url": image_base64}}
+            ]
+        })
+    
+    return messages
+
+def predict(chat_history, query, image):
+    # Process the image if provided
+    image_data = process_image(image) if image else None
+    messages = create_messages(query, image_data)
+
+    # Call the Groq API with the messages
+    try:
+        completion = client.chat.completions.create(
+            model="llama-3.2-11b-vision-preview",
+            messages=messages,
+            temperature=1,
+            max_tokens=1500,
+            top_p=1,
+            stream=False,
+        )
+
+        response_text = completion.choices[0].message.content.strip()
+    except Exception as e:
+        response_text = f"Error: {str(e)}"
+    
+    chat_history.append((query, response_text))
+    return chat_history
+
+def clear_history():
+    return []
+
+def main():
+    args = _get_args()
+    
+    with gr.Blocks() as demo:
+        gr.Markdown("<h1 style='text-align: center;'>Llama-3.2-11b-vision-preview</h1>")
+        
+        chatbox = gr.Chatbot()
+        query = gr.Textbox(label="Input", placeholder="Type your query here...")
+        image_input = gr.Image(type="pil", label="Upload Image")
+        
+        submit_btn = gr.Button("Submit")
+        clear_btn = gr.Button("Clear History")
+        
+        submit_btn.click(predict, inputs=[chatbox, query, image_input], outputs=chatbox)
+        clear_btn.click(clear_history, outputs=chatbox)
+        
+    demo.launch(share=args.share)
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+gradio
+groq
+Pillow