Update app.py

app.py

@@ -1,14 +1,18 @@
 import streamlit as st
 import base64
 import json
-from huggingface_hub import InferenceClient
+import requests
 
-# Function to read the image file and return a base64-encoded string
-def get_image_base64(image_file):
-    return base64.b64encode(image_file.read()).decode('utf-8')
+# Function to read the image file
+def get_image_bytes(image_file):
+    return image_file.read()
 
 # Streamlit page setup
-st.set_page_config(page_title="MTSS Image Accessibility Alt Text Generator", layout="centered", initial_sidebar_state="auto")
+st.set_page_config(
+    page_title="MTSS Image Accessibility Alt Text Generator",
+    layout="centered",
+    initial_sidebar_state="auto"
+)
 
 # Add the image with a specified width
 image_width = 300  # Set the desired width in pixels
@@ -17,8 +21,8 @@ st.image('MTSS.ai_Logo.png', width=image_width)
 st.header('VisionTexts™ | Accessibility')
 st.subheader('Image Alt Text Creator')
 
-# Initialize the Hugging Face InferenceClient with the API key from secrets
-client = InferenceClient(api_key=st.secrets["huggingface_api_key"])
+# Initialize the API key from Streamlit secrets
+api_key = st.secrets["huggingface_api_key"]
 
 # File uploader
 uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
@@ -63,9 +67,8 @@ complex_image_prompt_text = (
 if uploaded_file is not None and analyze_button:
 
     with st.spinner("Analyzing the image ..."):
-        # Get base64-encoded image string
-        image_bytes = uploaded_file.read()
-        base64_image_string = base64.b64encode(image_bytes).decode('utf-8')
+        # Read the image bytes
+        image_bytes = get_image_bytes(uploaded_file)
 
         # Detect the image content type
         import imghdr
@@ -99,32 +102,45 @@ if uploaded_file is not None and analyze_button:
                 }
             ]
 
-            # Attachments array containing the image
-            attachments = [
-                {
-                    "type": "image",
-                    "content": base64_image_string,
-                    "content_type": content_type,
-                }
-            ]
+            # Prepare headers and endpoint
+            headers = {
+                "Authorization": f"Bearer {api_key}"
+            }
+            api_url = "https://api-inference.huggingface.co/v1/chat/completions"
+
+            # Prepare the data payload
+            payload = {
+                "model": "meta-llama/Llama-3.2-11B-Vision-Instruct",
+                "messages": messages,
+                "max_tokens": 500
+            }
 
             # Make the request to the Hugging Face API
             try:
-                # Send the request to the model
-                completion = client.chat.completions.create(
-                    model="meta-llama/Llama-3.2-11B-Vision-Instruct",
-                    messages=messages,
-                    attachments=attachments,
-                    max_tokens=500
+                # Send the request with the image file in the 'files' parameter
+                response = requests.post(
+                    api_url,
+                    headers=headers,
+                    data={"data": json.dumps(payload)},
+                    files={"file": ("image", image_bytes, content_type)},
+                    timeout=60  # Optional: increase timeout if needed
                 )
 
+                # Check for errors
+                response.raise_for_status()
+
+                # Parse the response
+                completion = response.json()
+
                 # Extract the assistant's response
-                assistant_response = completion.choices[0].message['content']
+                assistant_response = completion['choices'][0]['message']['content']
 
                 # Display the response
                 st.markdown(assistant_response)
 
                 st.success('Powered by MTSS GPT. AI can make mistakes. Consider checking important information.')
+            except requests.exceptions.HTTPError as http_err:
+                st.error(f"HTTP error occurred: {http_err}")
             except Exception as e:
                 st.error(f"An error occurred: {e}")
 else: