Update app.py

app.py

@@ -1,19 +1,20 @@
 import os
 import gradio as gr
 import requests
-from groq import Groq
+import openai
+from PIL import Image
 
-# Initialize Groq client
-visionkey = os.getenv("GroqVision")  # Your Groq Vision API key from environment variables
-client = Groq(api_key=visionkey)
+# Set your API keys (replace with your actual keys or environment variables)
+sambanova_api_key = os.getenv("Vision")  # Sambanova API Key
+imagebb_api_key = os.getenv("ImageAPI")  # Your imgbb API key from environment variables
 
-# Your imgbb API key
-imagekey = os.getenv("ImageAPI")  # Your imgbb API key from environment variables
-IMGBB_API_KEY = imagekey
+# Initialize Sambanova OpenAI client
+openai.api_key = sambanova_api_key
+openai.api_base = "https://api.sambanova.ai/v1"
 
 def upload_image_to_imgbb(image_path):
     """Uploads an image to imgbb and returns the URL."""
-    url = f"https://api.imgbb.com/1/upload?key={IMGBB_API_KEY}"
+    url = f"https://api.imgbb.com/1/upload?key={imagebb_api_key}"
     with open(image_path, "rb") as image_file:
         files = {"image": image_file.read()}
         response = requests.post(url, files=files)
@@ -23,7 +24,7 @@ def upload_image_to_imgbb(image_path):
             raise ValueError(f"Image upload failed: {response.json()}")
 
 def analyze_image(image, instruction):
-    """Analyzes the image using Groq's Llama 3.2 based on the instruction provided."""
+    """Analyzes the image using Sambanova's Llama 3.2 Vision Instruct based on the provided instruction."""
     try:
         # Save the uploaded image locally
         image_path = "uploaded_image.png"
@@ -35,22 +36,20 @@ def analyze_image(image, instruction):
         # Debug: Log the uploaded image URL
         print(f"Uploaded Image URL: {image_url}")
 
-        # Call the Groq API to analyze the image
-        # Call the Groq API to analyze the image
-        completion = client.chat.completions.create(
-            model="llama-3.2-90b-vision-preview",
-            messages=[{
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": instruction},
-                    {"type": "image_url", "image_url": {"url": image_url}}
-                ]
-            }],
-            temperature=0.7,
-            max_tokens=512,
-            top_p=1,
-            stream=False,
-            stop=None,
+        # Call the Sambanova API to analyze the image
+        completion = openai.ChatCompletion.create(
+            model="Llama-3.2-90B-Vision-Instruct",
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": instruction},
+                        {"type": "image_url", "image_url": {"url": image_url}}
+                    ]
+                }
+            ],
+            temperature=0.1,
+            top_p=0.1,
         )
 
         # Extract and return the response
@@ -77,4 +76,4 @@ iface = gr.Interface(
 )
 
 # Launch the app
-iface.launch()
+iface.launch()