wokring

pyproject.toml

@@ -8,6 +8,9 @@ dependencies = [
     "google>=3.0.0",
     "google-genai>=1.31.0",
     "huggingface-hub>=0.34.4",
+    "ipython>=9.5.0",
+    "langchain>=0.3.27",
+    "langchain-core>=0.3.75",
     "langfuse>=3.3.4",
     "nest-asyncio>=1.6.0",
     "openai-agents>=0.2.8",

src/_agents.py

@@ -1,20 +1,40 @@
 from agents import Agent, RunContextWrapper
 from model import get_model
+import os
+from dotenv import load_dotenv
+from agents import Agent, AsyncOpenAI, Runner,function_tool, AgentHooks, TContext
+from model import get_model
+from contextlib import AsyncExitStack
+from agents.mcp.server import MCPServerStdio, MCPServerStdioParams
+from agents.mcp import ToolFilterContext,  create_static_tool_filter
+import asyncio
+import uuid
+import base64
+from typing import Any
+
+
+
+
 
 
 #----------------------- content agent ----------------------------------
 
 def content_prompt(context: RunContextWrapper, agent: Agent):
-    """You are a LinkedIn Content Writer AI.  
-Your task is to take a user's description of an idea, event, or announcement and turn it into engaging LinkedIn post text.  
-
-Guidelines:
-- Start with a strong hook or headline (1 line).
-- Write a clear and engaging body (2-5 short sentences).
-- Use a professional but approachable tone suited for LinkedIn.
-- Add 3-6 relevant hashtags.
-- End with a call-to-action (CTA), e.g., “Let's connect”, “Check out the details”, “Join the discussion”.
-- Keep sentences concise and scannable for social media.
+    """You are a social media Content Writer Agent. 
+You will be provided a raw text on which you need to create a content of social media post. 
+Your task is to transform a user's idea, event, or announcement into a highly engaging post tailored for the specified platform.
+
+Instructions:
+- The user query will indicate the platform (LinkedIn, Twitter/X, Instagram, Facebook, etc.). Adjust tone, style, and length accordingly.
+    - LinkedIn: professional, insightful, concise.
+    - Twitter/X: short, punchy, with impactful hashtags.
+    - Instagram: friendly, visual, creative, with hashtags.
+    - Facebook: approachable, slightly longer, storytelling style.
+- Begin with a compelling hook or headline.
+- Write a concise, engaging body (2-5 sentences) that conveys the key message.
+- Include 3-6 relevant, trending hashtags appropriate for the platform.
+- End with a clear, platform-suitable call-to-action (CTA), e.g., “Join the discussion”, “Check it out”, “Comment below”.
+- Ensure readability: short sentences, scannable text, and consistent tone.
 """
 
 
@@ -35,8 +55,115 @@ content_agent = Agent(
 
 
 
+#----------------------- Web Browser agent ---------------------------Start----------------
+
+
+
+
+@function_tool
+def save_temp_screenshot(image_data: str, is_base64: bool = True) -> str:
+    """
+    Save a screenshot to the tmpshot folder.
+    
+    Args:
+        image_data: Either base64 string (default) or raw bytes.
+        is_base64: Set True if input is base64, False if raw bytes.
+    
+    Returns:
+        str: Path to the saved screenshot.
+    """
+    print(image_data, is_base64)
+    # Ensure folder exists
+    folder = "tmpshot"
+    os.makedirs(folder, exist_ok=True)
+
+    # Generate unique filename
+    filename = f"screenshot_{uuid.uuid4().hex}.png"
+    path = os.path.join(folder, filename)
+
+    # Decode and save
+    if is_base64:
+        with open(path, "wb") as f:
+            f.write(base64.b64decode(image_data))
+    else:
+        with open(path, "wb") as f:
+            f.write(image_data)
+
+    return path
+
+
+def site_analyzer_prompt():
+    
+    return """
+You are Site Analyzer, which extract the details for a website. 
+
+- Your role is to analyze a webpage and always return three things:
+    1. Raw visible text from the page.  
+    2. Extract only the 4-5 most frequent distinct colors from the page's CSS as theme colors, and indicate whether the overall theme is dark or light. 
+    3. A screenshot of the page saved via the `save_temp_screenshot` function for design/theme analysis.  
+
+- Must follow these *STEPS* in order to complete the task:  
+    1 Open the webpage in a browser.
+    2. Navigate to the given URL with browser_navigate.  
+    3. Wait for the body to load using browser_wait_for 2 seconds.  
+    4. Extract raw text using browser_evaluate with `document.body.innerText`.  
+    5. Extract color themes using browser_evaluate with `getComputedStyle`.  
+    6. Capture a screenshot with  browser_take_screenshot with fullPage=False.  
+
+- Final Output Format (MUST be JSON only):  
+```json
+{
+  "raw_text": "...raw text...",
+  "theme":{
+    "type": "dark"|"light"
+    "colors": ["#RRGGBB", "rgb(...)", ...],
+  },
+  "screenshot_file_name": "<screenshot file name returned by browser_take_screenshot>"
+}
+"""
+
+
+
+
+async def custom_filter(context: ToolFilterContext, tool) -> bool:
+    # Only allow tools that start with "mood"
+    return  context.agent.name == "site_analyzer_agent" and (tool.name == 'browser_navigate' or tool.name == 'browser_wait_for' or tool.name == 'browser_evaluate' or tool.name == 'browser_take_screenshot')
+
+
+
+
+
+async def get_site_analyzer_agent():
+    async with MCPServerStdio(
+            # params=MCPServerStdioParams(
+            #     command="npx", 
+            #     args=["@playwright/mcp@latest", "--output-dir", "./tmpshot", "--headless"],
+            #     ), name="Playwrite",
+            # client_session_timeout_seconds=30,
+            params={
+                "command": "npx", 
+                "args": ["@playwright/mcp@latest", "--output-dir", "./tmpshot", "--headless"],
+            },  
+            tool_filter=create_static_tool_filter(
+            allowed_tool_names=["browser_navigate", "browser_wait_for", "browser_evaluate", "browser_take_screenshot", "write_file"]
+        )
+    ) as playwrite_client:
+        
+         
+        site_analyzer_agent = Agent(
+            name="site_analyzer_agent",
+            instructions=site_analyzer_prompt(),
+            model=get_model('gemini-2.0-flash'),
+            mcp_servers=[playwrite_client],
+        )
+
+        return site_analyzer_agent, playwrite_client
+
 
 
 
 
+# if __name__ == "__main__":
+#    asyncio.run(create_browser_agent())
 
+#----------------------- Web Browser agent ---------------------------END----------------

src/main.py

@@ -1,14 +1,18 @@
 import os
 import asyncio
 from dataclasses import dataclass
-from agents import set_tracing_disabled, Runner, SQLiteSession
-from _agents import content_agent
+from agents import set_tracing_disabled, Runner, SQLiteSession, Agent, AgentHooks, RunContextWrapper, TContext
+from _agents import content_agent, get_site_analyzer_agent
 from pydantic import BaseModel, Field, ConfigDict
+from model import get_model
 from typing import Optional
 import nest_asyncio
 import logfire
 from langfuse import get_client
- 
+import shutil
+from typing import Any
+
+
  
 # set_tracing_disabled(disabled=True)
 
@@ -37,56 +41,98 @@ else:
 
 
 
+
 class AgentContext(BaseModel):
     user_input: Optional[str] = Field(default=None,description="The input provided by the user to the agent.")
     post_content: Optional[str] = Field(default=None,description="The generated content for the post.")
-
+    raw_text: Optional[str] = Field(default=None,description="Raw text extracted from web pages.")
     model_config= ConfigDict(extra='forbid')
 
+
+
+class MyAgentHooks(AgentHooks):
+    async def on_tool_start(self, context, agent, output):
+        print(f"Agent {agent.name} finished with output: {output}")
+    async def on_start(self, context, agent):
+        print(f"Agent {agent.name} started.")
+
+
 async def main():
     print("Hello from smpg!")
-
     session = SQLiteSession("conversation_123")
 
     agent_context = AgentContext(
         user_input="Write a LinkedIn post about the importance of AI in modern business strategies."
         )
 
+    site_analyzer_agent, playwrite_client = await get_site_analyzer_agent()
+ 
+    orchestrator_agent = Agent(
+        name="orchestrator_agent",
+        instructions="""You are the Orchestrator Agent for social media post creation. 
+    Your task is to coordinate multiple agents to produce a complete social media post. 
+
+    Steps:
+    1. Receive user input including a brief and a URL.
+    2. Call the Site Analysis Agent to extract key details from the URL.
+    3. Pass the extracted text and user brief to the Content Agent to generate engaging platform-specific post text.
+    4. Provide the generated content and relevant details to the Media Generation Agent to create images or media.
+    5. Combine all outputs into a coherent final post suitable for the requested platform.
+
+    Rules:
+    - Do not generate text or media yourself.
+    - Always follow the above order.
+    - Validate each agent's output before proceeding to the next step.
+    - Ask the user for clarification only if the input is missing or ambiguous.""",
+        tools=[
+            content_agent.as_tool(
+                tool_name="content_agent",
+                tool_description="""
+                Generates engaging social media post content based on user input and extracted text.
+                Input: A brief description and raw text from the Site Analysis Agent.
+                """
+            ),
+            site_analyzer_agent.as_tool(
+                tool_name="site_analyzer_agent",
+                tool_description="""Analyzes a webpage and extracts key details such as raw text, theme colors, and a screenshot.
+                Input: A valid URL to analyze.
+                Output: JSON with raw text, theme colors, and screenshot file name.
+                """
+            ),
+        ],
+        model=get_model('gemini-2.0-flash'),
+        hooks=MyAgentHooks(),
+    )
+
+    result = await Runner.run(orchestrator_agent, 
+            input="""
+                Create a LinkedIn post about the importance of AI in modern business strategies.
+                Website URL : https://denovers.com
+            """ ,
+        )
+    print(result)
+    playwrite_client.cleanup()
 
+
+
+    # result = await Runner.run(content_agent, context=agent_context, input=agent_context.user_input, session=session ,max_turns=5)
     
-# orchestrator_agent = Agent(
-#     name="orchestrator_agent",
-#     instructions=(
-#         "You are a translation agent. You use the tools given to you to translate."
-#         "If asked for multiple translations, you call the relevant tools in order."
-#         "You never translate on your own, you always use the provided tools."
-#     ),
-#     tools=[
-#         spanish_agent.as_tool(
-#             tool_name="translate_to_spanish",
-#             tool_description="Translate the user's message to Spanish",
-#         ),
-#         french_agent.as_tool(
-#             tool_name="translate_to_french",
-#             tool_description="Translate the user's message to French",
-#         ),
-#         italian_agent.as_tool(
-#             tool_name="translate_to_italian",
-#             tool_description="Translate the user's message to Italian",
-#         ),
-#     ],
-#     model=model
-# )
-
-
-
-    result = await Runner.run(content_agent, context=agent_context, input=agent_context.user_input, session=session ,max_turns=5)
-    
-    agent_context.post_content = result.final_output
-    print("Generated Post Content:")
-    print(agent_context.post_content)
+    # agent_context.post_content = result.final_output
+    # print("Generated Post Content:")
+    # print(agent_context.post_content)
+
+    # result = await Runner.run(site_analyzer_agent,  input="URL : https://www.denovers.com, give raw_text, theme and screenshot_name" ,max_turns=15)
+    # print(result)
+   
+    # agent_context.raw_text = result.final_output   
+    # print(agent_context.raw_text) 
+
+
 
 
 
 if __name__ == "__main__":
+   if not shutil.which("npx"):
+        raise RuntimeError("npx is not installed. Please install it with `npm install -g npx`.")
+
    asyncio.run(main())

src/utils/image-generation.py

@@ -3,6 +3,14 @@ import re
 from google import genai
 from huggingface_hub import InferenceClient
 from dotenv import load_dotenv, find_dotenv
+from pydantic import BaseModel, Field, ConfigDict
+from langchain_core.output_parsers import JsonOutputParser
+import json
+import time
+
+from PIL import Image
+from io import BytesIO
+from IPython.display import display
 
 _: bool = load_dotenv(find_dotenv())
 
@@ -12,14 +20,19 @@ client = genai.Client(
 )
 
 
+# aspect_ratios = {
+#     "1:1": (1328, 1328),
+#     "16:9": (1664, 928),
+#     "9:16": (928, 1664),
+#     "4:3": (1472, 1140),
+#     "3:4": (1140, 1472),
+#     "3:2": (1584, 1056),
+#     "2:3": (1056, 1584),
+# }
+
 aspect_ratios = {
-    "1:1": (1328, 1328),
-    "16:9": (1664, 928),
-    "9:16": (928, 1664),
-    "4:3": (1472, 1140),
-    "3:4": (1140, 1472),
-    "3:2": (1584, 1056),
-    "2:3": (1056, 1584),
+    "1:1": (1200, 1200),  # Standard LinkedIn square post
+    "16:9": (1200, 627),  # LinkedIn horizontal post
 }
 
 hf_client = InferenceClient(
@@ -28,17 +41,42 @@ hf_client = InferenceClient(
 )
 
 
-def generate_json_from_idea(idea: str, defaultValues:str) -> str:
-    """Generates a detailed JSON prompt from a simple idea using a Gemini model."""
-
-    # The prompt template includes the desired JSON schema structure.
-    schema_template = """Convert the attached user idea into a detailed JSON object for generating an image. The output should only be the raw JSON object, without any markdown formatting like ```json ... ```.
 
-Idea: "{idea}"
+post_schema = """{
+  "meta": {
+    "platform": "LinkedIn | Instagram | Twitter | TikTok",
+    "aspectRatio": "1:1 | 16:9 | 9:16 | 4:5",
+    "style": "Modern | Minimal | Bold | Corporate | Playful"
+  },
+  "brand": {
+    "company": "Company Name",
+    "logo": "Include | Exclude",
+    "colors": ["#HEX1", "#HEX2", "#HEX3"]
+  },
+  "content": {
+    "headline": "Main text (max 8 words)",
+    "subtext": "Optional supporting line (max 15 words)",
+    "tone": "Professional | Inspirational | Energetic | Friendly"
+  },
+  "visuals": {
+    "primarySubject": "Headline with abstract icons | Infographic shapes | Product mockup",
+    "elements": ["Icons", "Illustrations", "Charts", "Abstract shapes"],
+    "composition": "Centered | Rule of Thirds | Balanced layout"
+  },
+  "design": {
+    "typography": "Bold, clean, sans-serif | Minimal serif",
+    "contrast": "High | Medium",
+    "background": "Solid color | Gradient | Abstract"
+  },
+  "finishing": {
+    "quality": "Ultra-sharp, high-resolution",
+    "effects": "Subtle shadows | Soft gradients | None"
+  }
+}
 
-defaultValues:{defaultValues}
+"""
 
-Schema:
+normal_schema = """
 {{
     "meta": {{
         "styleName": "...", // A unique, descriptive name for this specific image style or preset (e.g., "Ethereal Forest Magic", "Cyberpunk Noir Alley").
@@ -97,15 +135,56 @@ Schema:
     }}
 }}
 """
-    prompt = schema_template.format(idea=idea,defaultValues=defaultValues)
 
+
+
+
+
+def generate_json_from_idea(user_input: str, defaultValues:str) -> dict:
+    """Generates a detailed JSON prompt from a simple idea using a Gemini model."""
+
+    # The prompt template includes the desired JSON schema structure.
+    schema_template = """
+You are a professional creative director preparing a design brief for a graphic designer or an AI image generation model. 
+The user will give you a topic, idea, or post concept. Your task is to create a structured, detailed, and clear **image brief** that a designer can immediately use to create the visual.
+
+Steps to follow:
+1. Understand the user query and extract the main theme and goal.
+2. Specify a **headline** (4–10 words, bold, attention-grabbing).
+3. Write a **subtext** (1–2 sentences, persuasive or explanatory).
+4. Suggest **visual style / mood** (e.g., modern, bold, minimal, futuristic, playful, professional, corporate).
+5. Recommend **visual elements** (icons, illustrations, product mockups, abstract shapes, characters).
+6. Suggest a **color palette** (2–3 colors, HEX or descriptive names).
+7. Provide **layout guidance** (hierarchy: headline first, subtext second, CTA last, placement of visuals).
+8. Optionally, include a **call-to-action** (if relevant).
+
+User's input: "{user_input}"
+
+defaultValues:{defaultValues}
+Schema:
+{post_schema}
+
+The output should only be the raw JSON object, without any markdown formatting like ```json ... ```.
+Output format:
+    imagefileName: str = name for the image file
+    image_brief: str = detailed image brief for designer/AI
+    generatedSchema: dict  = generated schema
+"""
+
+    prompt = schema_template.format(user_input=user_input,defaultValues=defaultValues,post_schema=post_schema, )
+
+    
     response = client.models.generate_content(
         model="gemini-2.5-pro",
         contents=prompt,
+        
     )
 
-    print(response.text)
-    return response.text
+    print(response)
+    data = JsonOutputParser().invoke(response.text)
+
+    print(data)
+    return data
 
 
 def generate_images(
@@ -114,6 +193,7 @@ def generate_images(
     aspectRatio: str = "1:1",  # "1:1", "3:4", "4:3", "9:16", and "16:9".
     output_dir: str = "images",
     num_images: int = 1,
+    file_name: str = "",
 ):
     """Generates images using Imagen and saves them to the output directory."""
 
@@ -130,32 +210,48 @@ def generate_images(
         #     ),
         # )
 
-        width, height = aspect_ratios[aspectRatio]
-        response = hf_client.text_to_image(
-            prompt=prompt,
-            model="Qwen/Qwen-Image",
-            width=width,
-            height=height,
+        # width, height = aspect_ratios[aspectRatio]
+        # print(width, height)
+        # response = hf_client.text_to_image(
+        #     prompt=prompt,
+        #     negative_prompt="social media logos, LinkedIn icon, Instagram logo, Twitter logo, browser bars, fake UI, buttons, links, gibberish text, distorted fonts",
+        #     model="Qwen/Qwen-Image",
+        #     width=width,
+        #     height=height,
+        #     guidance_scale=10.0,
+
+        # )
+        # print(response)
 
+        response = client.models.generate_content(
+            model="gemini-2.5-flash-image-preview",
+            contents=prompt,
         )
-        print(response)
-
-        clean_idea = (re.sub(r"[^a-zA-Z0-9\s]", "", idea[:30]).lower().replace(" ", "-"))
-        image_path = os.path.join(output_dir, f"{clean_idea}-.png")
-        response.save(image_path)
-        with open(os.path.join(output_dir, f"{clean_idea}.json"), "w") as f:
-            f.write(prompt)
-        print(f"Saved image and prompt to {image_path}")
-
-        # for i, generated_image in enumerate(response):
-        #     clean_idea = (
-        #         re.sub(r"[^a-zA-Z0-9\s]", "", idea[:30]).lower().replace(" ", "-")
-        #     )
-        #     image_path = os.path.join(output_dir, f"{clean_idea}-{i+1}.png")
-        #     generated_image.image.save(image_path)
-        #     with open(os.path.join(output_dir, f"{clean_idea}.json"), "w") as f:
-        #         f.write(prompt)
-        #     print(f"Saved image and prompt to {image_path}")
+
+        image_parts = [
+            part.inline_data.data
+            for part in response.candidates[0].content.parts
+            if part.inline_data
+        ]
+        
+        if image_parts:
+            image = Image.open(BytesIO(image_parts[0]))
+            timestamp = str(int(time.time()))
+            image_path = os.path.join(output_dir, f"{file_name}-{timestamp}.png")
+            image.save(image_path)
+            with open(os.path.join(output_dir, f"{file_name}.json"), "w") as f:
+                f.write(prompt)
+            print(f"Saved image and prompt to {image_path}")
+            display(image)
+
+        # clean_idea = (re.sub(r"[^a-zA-Z0-9\s]", "", idea[:30]).lower().replace(" ", "-"))
+        # timestamp = str(int(time.time()))
+        # image_path = os.path.join(output_dir, f"{file_name}-{timestamp}.png")
+        # response.save(image_path)
+        # with open(os.path.join(output_dir, f"{file_name}.json"), "w") as f:
+        #     f.write(prompt)
+        # print(f"Saved image and prompt to {image_path}")
+
 
     except Exception as e:
         print(f"An error occurred during image generation: {e}")
@@ -171,15 +267,37 @@ def generate(
     os.makedirs(output_dir, exist_ok=True)
 
     ideaJson = generate_json_from_idea(idea,f"aspect_ratio:{aspectRatio}")
-
+    
     generate_images(
+        file_name=ideaJson.get("imagefileName", "image"),
         idea=idea,
         prompt=f"""
+You are a professional social media graphic designer AI specialized in creating high-quality, visually striking images for social media posts. 
+Your goal is to generate attention-grabbing, brand-aligned images that clearly communicate the given message.
+
+Guidelines:
+- Style: Modern, clean, bold typography, high contrast, professional and visually engaging.
+- Composition: Balanced layout with focal points, readable text placement, and strong visual hierarchy.
+- Colors: Use brand-relevant palettes, with consistent tones suitable for LinkedIn/Twitter/Instagram audiences.
+- Typography: Ensure clear, legible fonts with strong emphasis for headlines.
+- Elements: Use relevant icons, illustrations, or abstract visuals to reinforce the message. Avoid clutter.
+- Aspect Ratios: Default to 1:1 (Instagram/LinkedIn) unless otherwise specified. Optionally support 16:9 (Twitter, YouTube) and 9:16 (Stories/Reels).
+- Text handling: Highlight key message with 4-10 words max, never overcrowd.
+- Do NOT include watermarks, stock photo watermarks, or irrelevant artifacts.
+- Quality: Ultra-sharp, high-resolution, and aesthetically polished for professional social media usage.
+
+
+
+User's input:
 {idea}
 
-<MetaSchemaForImageGeneration>
-{ideaJson}
-</MetaSchemaForImageGeneration>
+Image brief: Follow these steps to create the image
+{ideaJson.get("image_brief", "")}
+
+Schema (use this structured format when interpreting inputs/brief):
+{ideaJson.get("generatedSchema", {})}
+
+
 """,
         aspectRatio="1:1",
         output_dir=output_dir,
@@ -187,6 +305,9 @@ def generate(
     )
 
 
+
+
+
 if __name__ == "__main__":
     ideas = [
         # "A energy drink with water drops on it, ultra realistic, for a commercial.",
@@ -199,7 +320,13 @@ if __name__ == "__main__":
         # "A magical man with sparkling pink hair and large from an anime.",
         # "A cartoon robot waving happily, with a simple, bold outline and bright, flat colors. ",
         # "A full-body character sheet of a realistic pirate captain, showing front, back, and side views.",
-        """An AI brain made of digital circuits seamlessly integrated into a modern office skyline, representing artificial intelligence as essential for business growth and innovation"""
+        """
+Create a media for posting on linkedin plateform, make it modren and professional.
+
+companydetails:
+    Design Atom is a subscription-based design service tailored for AI and tech startups, offering comprehensive product design, branding, and web development solutions. With a fixed monthly rate, dedicated designers, and fast 4-hour average turnaround times, they have completed over 200 projects, helped clients raise $45M+, and reached 100M+ users with their designs. Trusted by 45+ global companies, including Y Combinator-backed startups, Design Atom delivers startup-proven results, streamlining UX, boosting retention, and enhancing conversions. Their free design trial eliminates bottlenecks, supporting startups in building investor-ready, user-friendly products.
+
+"""
     ]
 
     for idea in ideas:

uv.lock

@@ -153,6 +153,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/31/da/e42d7a9d8dd33fa775f467e4028a47936da2f01e4b0e561f9ba0d74cb0ca/argcomplete-3.6.2-py3-none-any.whl", hash = "sha256:65b3133a29ad53fb42c48cf5114752c7ab66c1c38544fdf6460f450c09b42591", size = 43708 },
 ]
 
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978 }
+wheels = [
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