Update app.py

app.py

@@ -5,6 +5,7 @@ import base64
 from io import BytesIO
 from PIL import Image
 import numpy as np
+import json
 
 # Initialize the Together client
 api_key = os.environ.get('TOGETHER_API_KEY')
@@ -25,28 +26,29 @@ def generate_gradio_app(image):
         img_str = base64.b64encode(buffered.getvalue()).decode()
         
         # Prepare the message for the API call
-        # Using the format: <image>{base64_image}</image>\nHuman: {prompt}\nAssistant:
-        message = f"""<image>{img_str}</image>
-Human: Analyze this wireframe image and suggest a simple Gradio app layout based on it. Describe the main elements you see and how they could be implemented using Gradio components.
-Assistant: Certainly! I'll analyze the wireframe image and suggest a simple Gradio app layout based on what I see. Let me describe the main elements and how they could be implemented using Gradio components."""
+        message_content = f"""<image>{img_str}</image>
+Analyze this wireframe image and suggest a simple Gradio app layout based on it. Describe the main elements you see and how they could be implemented using Gradio components."""
+
+        # Prepare the API request payload
+        payload = {
+            "model": "meta-llama/Llama-Vision-Free",
+            "messages": [{"role": "user", "content": message_content}],
+            "max_tokens": 512,
+            "temperature": 0.7,
+            "top_p": 0.7,
+            "top_k": 50,
+            "repetition_penalty": 1,
+            "stop": ["<|eot_id|>", "<|eom_id|>"],
+            "stream_tokens": True
+        }
 
         # Make the API call
-        response = client.chat.completions.create(
-            model="meta-llama/Llama-Vision-Free",
-            messages=[{"role": "user", "content": message}],
-            max_tokens=512,
-            temperature=0.7,
-            top_p=0.7,
-            top_k=50,
-            repetition_penalty=1,
-            stop=["<|eot_id|>", "<|eom_id|>"],
-            stream=True
-        )
+        response = client.chat.completions.create(**payload)
         
         # Collect the streamed response
         generated_text = ""
         for chunk in response:
-            if chunk.choices[0].delta.content is not None:
+            if chunk.choices and chunk.choices[0].delta.content:
                 generated_text += chunk.choices[0].delta.content
         
         return generated_text