Update app.py

Author: NikithaKunapareddy

app.py

@@ -12,21 +12,21 @@
 from transformers import pipeline
 from dotenv import load_dotenv
 
+
 load_dotenv()
 hf_token = os.getenv("HF_TOKEN")
 
 summarizer = pipeline(
     "summarization",
     model="facebook/bart-large-cnn",
-    device=-1  # Forces CPU usage to avoid meta tensor error
+    device=-1
 )
 
 st.set_page_config(page_title="Modern Edge Agent", layout="centered")
 st.title("Modern Edge Agent: Image Captioning & Generation")
 st.write("Upload an image to get a caption, or enter a prompt to generate an image. Runs on edge devices!")
 
 
-# --- Image Captioning (BLIP) ---
 @st.cache_resource
 def load_blip():
     processor = BlipProcessor.from_pretrained(
@@ -39,21 +39,18 @@ def load_blip():
     return processor, model
 
 
-# --- Text-to-Image (Stable Diffusion) ---
 def generate_image_via_api(prompt):
-    api_url = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
+    api_url = ("https://api-inference.huggingface.co/models/"
+               "black-forest-labs/FLUX.1-dev")
     headers = {"Authorization": f"Bearer {hf_token}"}
     payload = {"inputs": prompt}
     response = requests.post(api_url, headers=headers, json=payload)
-    
+
     if response.status_code == 200:
-        # Try to handle both raw image and base64 JSON
         try:
-            # Try raw image
             img = Image.open(BytesIO(response.content))
             return img
         except Exception:
-            # Try base64 JSON
             result = response.json()
             if isinstance(result, dict) and "image" in result:
                 img_data = base64.b64decode(result["image"])
@@ -74,40 +71,37 @@ def generate_image_via_api(prompt):
 with tab1:
     st.header("Image Captioning")
     uploaded_file = st.file_uploader(
-        "Upload an image", 
+        "Upload an image",
         type=["jpg", "jpeg", "png"]
     )
-    
+
     if uploaded_file:
         image = Image.open(uploaded_file).convert("RGB")
         st.image(image, caption="Uploaded Image", use_column_width=True)
         processor, model = load_blip()
-        
-        # Short caption
+
         inputs = processor(image, return_tensors="pt")
         with torch.no_grad():
             out = model.generate(**inputs)
             caption = processor.decode(out[0], skip_special_tokens=True)
         st.success(f"**Caption:** {caption}")
 
-        # OCR for text extraction
         st.write("Extracting text from image...")
         reader = easyocr.Reader(['en'], gpu=False)
         image_np = np.array(image)
         ocr_result = reader.readtext(image_np)
-        
-        # Only keep text with high confidence and filter out short/meaningless results
+
         filtered_text = [
-            item[1] for item in ocr_result 
-            if item[2] > 0.5 and len(item[1].strip()) > 2 and item[1].isalpha()
+            item[1] for item in ocr_result
+            if (item[2] > 0.5 and len(item[1].strip()) > 2 and
+                item[1].isalpha())
         ]
         extracted_text = " ".join(filtered_text)
-        
+
         if extracted_text:
             st.info(f"**Extracted Text:** {extracted_text}")
 
-        # Generate dynamic summary using Hugging Face Transformers (small LLM)
-        st.write("Generating dynamic summary with open-source LLM (Transformers)...")
+        st.write("Generating dynamic summary with open-source LLM...")
         try:
             if extracted_text:
                 summary_input = f"{caption}. {extracted_text}"
@@ -117,24 +111,25 @@ def generate_image_via_api(prompt):
             st.info(f"**Summary:** {output}")
         except Exception as e:
             st.warning(f"Summary generation failed: {e}")
-            # Fallback to previous logic
             if extracted_text:
-                summary = f"This image contains: {caption}. The following text is present: {extracted_text}"
+                summary = (f"This image contains: {caption}. "
+                          f"The following text is present: {extracted_text}")
             else:
-                summary = f"This image contains: {caption}. No readable text was detected."
+                summary = (f"This image contains: {caption}. "
+                          f"No readable text was detected.")
             st.info(f"**Summary:** {summary}")
 
 with tab2:
     st.header("Text-to-Image Generation")
     prompt = st.text_area("Enter a prompt to generate an image:")
-    
+
     if st.button("Generate Image") and prompt:
         with st.spinner("Generating image via Hugging Face API..."):
             try:
                 image = generate_image_via_api(prompt)
                 st.image(
-                    image, 
-                    caption="Generated Image", 
+                    image,
+                    caption="Generated Image",
                     use_column_width=True
                 )
             except Exception as e: