├── app.py              # Streamlit web application & core logic (Tavily + LLM)
├── mcp_server.py       # FastMCP server exposing tool endpoints
├── assests/            # Project diagrams & images
│   └── 3242.png
├── pyproject.toml      # Project configuration & dependencies
├── .env                # API keys configuration (not committed)
└── README.md           # Project documentation

AICREDITS_API_KEY=your_aicredits_or_openai_key
TAVILY_API_KEY=your_tavily_api_key
GEMINI_API_KEY=your_google_gemini_api_key

uv sync

uv run streamlit run app.py

uv run mcp dev mcp_server.py

{
  "mcpServers": {
    "youtube-script-generator": {
      "command": "uv",
      "args": ["run", "python", "C:/Users/DELL/Desktop/New folder/mcp_server.py"]
    }
  }
}

import os
from tavily import TavilyClient

# Initialize client
tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))

# Execute web search
response = tavily_client.search(
    query="Latest developments in AI agents",
    max_results=3,
    topic="general",
    search_depth="advanced"
)

print("Search Response:", response)

{
  "query": "Latest developments in AI agents",
  "follow_up_questions": null,
  "answer": null,
  "images": [],
  "results": [
    {
      "title": "Autonomous AI Agents in 2026: Trends & Breakthroughs",
      "url": "https://example.com/ai-agents-2026",
      "content": "AI agents are transforming software engineering with multi-agent orchestration and tool calling capabilities...",
      "score": 0.9821,
      "raw_content": null
    },
    {
      "title": "Open Source AI Agent Frameworks Overview",
      "url": "https://example.com/agent-frameworks",
      "content": "A comprehensive review of modern agent frameworks built for fast model context protocol (MCP) integration...",
      "score": 0.9543,
      "raw_content": null
    }
  ],
  "response_time": 0.84
}

import os
from openai import OpenAI

# Initialize client pointing to AICredits endpoint
client = OpenAI(
    base_url="https://api.aicredits.in/v1",
    api_key=os.getenv("AICREDITS_API_KEY")
)

# Request completion
completion = client.chat.completions.create(
    model="gemini-2.0-flash-lite-001",
    messages=[
        {"role": "user", "content": "Summarize key features of quantum computing."}
    ],
    temperature=0.3
)

print(completion.choices[0].message.content)

{
  "id": "chatcmpl-8x92a01bf982",
  "object": "chat.completion",
  "created": 1772500000,
  "model": "gemini-2.0-flash-lite-001",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Key features of quantum computing include:\n- **Superposition**: Qubits exist in multiple states simultaneously.\n- **Entanglement**: Interconnected qubit states enable exponentially faster calculations.\n- **Quantum Interference**: Amplifies correct paths to solve complex optimization problems."
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 42,
    "completion_tokens": 88,
    "total_tokens": 130
  }
}

import os
from google import genai
from google.genai import types

# Initialize official Gemini client
client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))

# Generate content call
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Write a 30-second YouTube Short hook on space exploration.",
    config=types.GenerateContentConfig(
        temperature=0.7,
        max_output_tokens=500
    )
)

print("Generated Output:", response.text)

{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "[Visual Cue: Fast zoom onto Mars surface]\n(Voiceover): Did you know we just found proof of liquid water under the Martian crust?"
          }
        ],
        "role": "model"
      },
      "finish_reason": "STOP",
      "index": 0,
      "safety_ratings": []
    }
  ],
  "usage_metadata": {
    "prompt_token_count": 28,
    "candidates_token_count": 45,
    "total_token_count": 73
  }
}

from app import get_realtime_info, generate_video_script

query = "Latest SpaceX Launch"

# Step 1: Get real-time summary & raw search backup
summary_text, raw_search_backup = get_realtime_info(query)

# Step 2: Generate production script using context
script = generate_video_script(summary_text or raw_search_backup)

print("--- SUMMARY ---")
print(summary_text)

print("\n--- SCRIPT ---")
print(script)

def get_realtime_info(query: str) -> tuple[str, str]:
    """
    Inputs:
        query (str): The search topic or keyword string.

    Returns:
        tuple[str, str]: (llm_summary_text, raw_source_info_markdown)
    """

def generate_video_script(info_text: str) -> str:
    """
    Inputs:
        info_text (str): Summarized or raw information context.

    Returns:
        str: Production-ready YouTube Short / Reel script.
    """