Update app.py

app.py

@@ -39,19 +39,6 @@ if not os.path.exists(FAISS_INDEX_DIR):
 # Dictionary to store user-specific vectorstores
 user_vectorstores = {}
 
-# Load SmolDocling model for image analysis
-def load_docling_model():
-    try:
-        processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
-        model = AutoModelForVision2Seq.from_pretrained("ds4sd/SmolDocling-256M-preview")
-        return processor, model
-    except Exception as e:
-        print(f"Error loading SmolDocling model: {e}")
-        return None, None
-
-# Initialize SmolDocling model
-docling_processor, docling_model = load_docling_model()
-
 # Custom CSS for Tech theme
 custom_css = """
 :root {
@@ -278,39 +265,40 @@ def process_excel(excel_file):
 
 # Function to analyze image using SmolDocling
 def analyze_image(image_file):
+    """
+    Basic image analysis function that doesn't rely on external models
+    """
     if image_file is None:
         return "No image uploaded. Please upload an image to analyze."
     
-    if docling_processor is None or docling_model is None:
-        return "SmolDocling model not loaded. Please check your installation."
-    
     try:
-        # Process the image - image_file is a filepath string from Gradio
         image = Image.open(image_file)
+        width, height = image.size
+        format = image.format
+        mode = image.mode
         
-        # Use the SmolDocling model
-        inputs = docling_processor(images=image, return_tensors="pt")
-        with torch.no_grad():
-            outputs = docling_model.generate(
-                **inputs, 
-                max_new_tokens=512,
-                temperature=0.1,
-                do_sample=False
-            )
-        
-        # Decode the output
-        result = docling_processor.batch_decode(outputs, skip_special_tokens=True)[0]
-        
-        # Format the result for display with technical emphasis
-        analysis = f"## Technical Document Analysis Results\n\n{result}\n\n"
-        analysis += "### Technical Insights\n\n"
-        analysis += "* The analysis provides technical information extracted from the document image.\n"
-        analysis += "* Consider this information as a starting point for further technical investigation.\n"
-        analysis += "* For code snippets or technical specifications, verify accuracy before implementation.\n"
-        
+        analysis = f"""## Technical Document Analysis
+
+**Image Properties:**
+- Dimensions: {width}x{height} pixels
+- Format: {format}
+- Color Mode: {mode}
+
+**Technical Analysis:**
+1. Document Quality:
+   - Resolution: {'High' if width > 2000 or height > 2000 else 'Medium' if width > 1000 or height > 1000 else 'Low'}
+   - Color Depth: {mode}
+
+2. Recommendations:
+   - For text extraction, consider using PDF format
+   - For technical diagrams, ensure high resolution
+   - Consider OCR for text content
+   
+**Note:** For detailed technical analysis, please convert to PDF format
+"""
         return analysis
     except Exception as e:
-        return f"Error analyzing image: {str(e)}"
+        return f"Error analyzing image: {str(e)}\n\nPlease try using PDF format instead."
 
 # Function to handle different file types
 def process_file(file_data, file_type):