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linkedin_carousel_app/generator.py

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+import os
+from google import genai
+from google.genai import types
+from styles import CarouselContent, STYLES, Slide
+from PIL import Image
+import io
+import json
+
+# Global client - will be initialized in app.py
+client = None
+
+def configure_genai(api_key: str):
+    """Configures the Gemini API with the provided key."""
+    global client
+    if api_key:
+        client = genai.Client(api_key=api_key, http_options={'api_version': 'v1alpha'}) # Gemini 3 models often require v1alpha
+
+def generate_carousel_content(source_text: str, style_name: str, language: str = "English") -> tuple[CarouselContent, dict]:
+    """
+    Generates the text content for the carousel using Gemini 3 Flash Preview.
+    Returns: (CarouselContent, usage_metadata)
+    """
+    if not client:
+        raise ValueError("API Client not configured.")
+
+    style_info = STYLES.get(style_name, list(STYLES.values())[0])
+    style_desc = style_info["description"]
+
+    prompt = f"""
+    You are a professional LinkedIn content creator. 
+    Create a structured carousel based on the following source text.
+    
+    Target Audience: Professionals on LinkedIn.
+    Tone: Educational, Insightful, Professional.
+    Style: {style_name} - {style_desc}
+    Output Language: {language}
+    
+    Source Text:
+    {source_text}
+    
+    Requirements:
+    1.  Create 5 to 7 slides.
+    2.  Each slide must have a 'headline' (short, punchy) and 'body' (clear, concise) in {language}.
+    3.  **Code Snippets**: If the source text contains code or technical examples, extract relevant snippets (max 5-8 lines) and include them in the 'code_snippet' field. Ensure the code is properly formatted and adds educational value.
+    4.  **Image Integration**: If the source text references specific images via URL (e.g., diagrams, screenshots), extract the URL into the 'source_image_url' field for the relevant slide. Ensure the 'image_prompt' describes how this image should be integrated (e.g., "Display the provided diagram prominently on a clean background").
+    5.  'image_prompt': Describe a background image that fits the '{style_name}' style and the slide content. Use visual keywords. Do NOT request text in the image.
+    6.  'post_text': Write the accompanying LinkedIn post caption in {language}.
+    """
+
+    try:
+        response = client.models.generate_content(
+            model="gemini-3-flash-preview",
+            contents=prompt,
+            config=types.GenerateContentConfig(
+                response_mime_type="application/json",
+                response_schema=CarouselContent,
+            )
+        )
+        
+        usage = {
+            "input_tokens": response.usage_metadata.prompt_token_count,
+            "output_tokens": response.usage_metadata.candidates_token_count
+        }
+
+        if response.parsed:
+            return response.parsed, usage
+        
+        data = json.loads(response.text)
+        return CarouselContent(**data), usage
+        
+    except Exception as e:
+        print(f"Content Generation Error: {e}")
+        raise e
+
+def generate_background_image(prompt: str, headline: str, body: str, code_snippet: str | None, style_name: str, reference_image: Image.Image = None, source_image: Image.Image | None = None) -> Image.Image:
+    """
+    Generates a slide image with text and optional code using Gemini 3 Image (Nano Banana Pro).
+    Can use a reference_image to maintain consistency.
+    Can integrate a source_image referenced in the text.
+    """
+    if not client:
+        raise ValueError("API Client not configured.")
+
+    style_info = STYLES.get(style_name, list(STYLES.values())[0])
+    style_suffix = style_info.get("bg_prompt_suffix", "")
+    
+    # instructions for code block
+    code_instr = ""
+    if code_snippet:
+        code_instr = (
+            f"CODE BLOCK RENDERING: Include a syntax-highlighted code block containing this code: "
+            f"\"\"\"{code_snippet}\"\"\" "
+            f"Place it in a dark box or distinct section, ensure it is perfectly legible with a monospaced font style. "
+        )
+
+    # improved prompt for text rendering
+    # If reference image is provided, we tell it to prioritize that over text style descriptions
+    style_instruction = f"in a {style_name} style" if not reference_image else "basing the style EXCLUSIVELY on the provided reference image"
+    
+    text_prompt = (
+        f"Create a professional presentation slide {style_instruction}. "
+        f"Visuals: {prompt}. {style_suffix if not reference_image else ''} "
+        f"TEXT RENDERING: The image MUST include the following text clearly and legibly. "
+        f"FONT CONSISTENCY: Use the 'Montserrat' font (or a clean, modern sans-serif like Roboto) for ALL text in this slide. "
+        f"Title: \"{headline}\" "
+        f"Subtitle: \"{body}\" "
+        f"{code_instr}"
+        f"Ensure the text is balanced, high contrast, and perfectly legible. "
+        f"High quality, professional design, aspect ratio 4:5 (portrait)."
+    )
+    
+    contents = [text_prompt]
+    
+    if source_image:
+        # Add instruction to integrate source image
+        contents[0] += " SOURCE IMAGE INTEGRATION: Prominently feature and integrate the provided source image (diagram/photo) into the slide layout. Ensure it fits harmoniously with the text and style."
+        # Convert source image to bytes
+        src_byte_arr = io.BytesIO()
+        source_image.save(src_byte_arr, format='PNG')
+        src_bytes = src_byte_arr.getvalue()
+        # Add source image part
+        contents.append(types.Part.from_bytes(data=src_bytes, mime_type="image/png"))
+
+    if reference_image:
+        # Add instruction to use reference
+        contents[0] += (
+            " STYLE REFERENCE: Match the exact layout, font size, color palette, "
+            "background texture, and overall composition of the provided reference image. "
+            "Ignore any other style descriptions."
+        )
+        # Convert ref image to bytes
+        ref_byte_arr = io.BytesIO()
+        reference_image.save(ref_byte_arr, format='PNG')
+        ref_bytes = ref_byte_arr.getvalue()
+        
+        # Add ref image part
+        contents.append(types.Part.from_bytes(data=ref_bytes, mime_type="image/png"))
+
+    try:
+        response = client.models.generate_content(
+            model="gemini-3-pro-image-preview",
+            contents=contents
+        )
+        
+        # Search for image in parts
+        for part in response.candidates[0].content.parts:
+            if part.inline_data:
+                return Image.open(io.BytesIO(part.inline_data.data))
+        
+        print("No inline_data found in response.")
+        return create_placeholder_image(style_name)
+            
+    except Exception as e:
+        print(f"Image generation failed: {e}. Using placeholder.")
+        return create_placeholder_image(style_name)
+
+def create_placeholder_image(style_name: str) -> Image.Image:
+    """Creates a solid color placeholder if generation fails."""
+    style_info = STYLES.get(style_name, list(STYLES.values())[0])
+    color = style_info.get("overlay_color", (50, 50, 50))
+    if len(color) == 4:
+        color = color[:3] # Drop alpha
+    return Image.new('RGB', (1080, 1350), color=color)