A Step-by-Step Coding Guide to Building a Gemini-Powered AI Startup Pitch Generator Using LiteLLM Framework, Gradio, and FPDF in Google Colab with PDF Export Support

In this tutorial, we have built a strong and interactive Amnesty International application that generates startup ideas using the Goiani Pro model of Google through the Multipul -Litellm framework. Litellm It is the backbone of this implementation, as it provides a unified interface to interact with more than 100 LLM providers using application programming facades compatible with Openai, which eliminates the complexity of dealing with individual SDKS. By taking advantage of the Litellm, we were smoothly with Gemini’s creative thinking capabilities and attracted the outputs in the facade of easy -to -use locusts. Also, we used FPDF to create a polished PDFS and compatible with Unicode that contains the full stadium surface. This tutorial shows how modern artificial intelligence tools, including Litellm, Gradio, Google Generalago AI and FPDF, can build a comprehensive solution for businessmen, innovators and developers.

!pip install litellm gradio fpdf --quiet

PIP Install Litellm Gradio FPDF – Quiet fixes the basic libraries for this project. It brings Litellm to interact with Gemini via a uniform applications interface, Gradio to create a simple web interface, and FPDF to export the stadium generated from artificial intelligence to a well-format PDF file-all with the suppression of prolonged installation records with-Quiet.

import os
import gradio as gr
import uuid
import urllib.request
from fpdf import FPDF
from litellm import completion


api_key = "Your API Key"

We import all the basic Python libraries used in the project, including the file operating system, UUID to create unique file names, and Urlib to download the lines. We also prepare Gradio for UI and FPDF to create PDF, and the Litellm completion function to interact with Gemini. The API_KEY variable stores the user’s API Gemini key, which is required to ratify applications.

import urllib.request
import zipfile
import os
import shutil


if not os.path.exists("DejaVuSans.ttf"):
    print("⏬ Downloading DejaVuSans.ttf...")
    font_zip_url = ""
    font_zip_path = "dejavu-fonts.zip"


    urllib.request.urlretrieve(font_zip_url, font_zip_path)


    with zipfile.ZipFile(font_zip_path, 'r') as zip_ref:
        zip_ref.extractall("dejavu-extracted")


    for root, dirs, files in os.walk("dejavu-extracted"):
        for file in files:
            if file == "DejaVuSans.ttf":
                ttf_path = os.path.join(root, file)
                shutil.copy(ttf_path, "DejaVuSans.ttf")
                print("✅ Font extracted and ready.")
                break

Here, we make sure that the Dejavusans.tf line is available to create a PDF compatible with the Unicode. It downloads the Sourceforge Zip file, extracts its contents, and heat the .TF file to the work guide. This step is extremely important to deal with special letters from Gemini when creating a final PDF using FPDF.

def call_gemini(system_prompt, user_prompt):
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ]
    response = completion(
        model="gemini/gemini-2.0-flash-lite",
        messages=messages,
        api_key=api_key
    )
    return response["choices"][0]["message"]["content"]

This function, Call_GIMINI, is a cover that uses the Litellm completion applications interface to interact with the Gemini 2.0 Flash Lite. It accepts the system’s mentor and demands the user, commits it in a compatible format with Openai, sends the request using the API key, and re-responds-which makes it easier to reuse it through different application parts.

def generate_startup_pitch(theme):
    try:
        idea_prompt = f"Generate an innovative startup idea in the field of {theme}. Focus on solving real problems using modern technology."
        tagline_prompt = "Based on the idea you just gave, generate a short, catchy tagline for the startup."
        pitch_prompt = """
        Based on the previous startup idea, write a concise pitch deck covering:
        1. Problem
        2. Solution
        3. Market Opportunity
        4. team Description
        5. Business Model
        6. Traction or Future Plan
        Format it in a way that looks like slide notes for a VC pitch.
        """


        idea = call_gemini("You are an innovation strategist.", idea_prompt)
        tagline = call_gemini("You are a branding expert.", tagline_prompt)
        pitch = call_gemini("You are a startup mentor writing a pitch deck.", pitch_prompt)


        filename = f"startup_pitch_{uuid.uuid4().hex[:8]}.pdf"
        pdf = FPDF()
        pdf.add_page()
        pdf.add_font("DejaVu", "", font_path, uni=True)
        pdf.set_font("DejaVu", size=12)


        full_text = f"Startup Idea:\n{idea}\n\nTagline:\n{tagline}\n\nPitch Deck:\n{pitch}"
        pdf.multi_cell(0, 10, full_text)
        pdf.output(filename)


        return idea, tagline, pitch, filename
    except Exception as e:
        return f"⚠️ Error: {e}", "", "", None

CaneTArtup_Pitch function regulates the entire start -up process. He sends designed claims to GEMINI via Litellm to produce the start -up idea, the attractive description logo, and the stadium surface is organized. Then the responses are combined with coordinated PDF using FPDF, with the appropriate Unicode support via the Dejavu line. PDF is saved with a unique file name, enabling users to download the dedicated stadium. Treating errors ensures smooth implementation and user reactions in case of failure.

with gr.Blocks() as demo:
    gr.Markdown("# 🚀 AI Startup Pitch Generator (with PDF Export)")
    theme_input = gr.Textbox(label="Enter a theme or industry", placeholder="e.g., mental health, fintech, climate tech")


    generate_button = gr.Button("Generate Pitch")


    idea_output = gr.Textbox(label="Startup Idea")
    tagline_output = gr.Textbox(label="Tagline")
    pitch_output = gr.Textbox(label="Pitch Deck Summary", lines=10)
    pdf_output = gr.File(label="Download Pitch as PDF")


    def wrapper(theme):
        idea, tagline, pitch, pdf_path = generate_startup_pitch(theme)
        return idea, tagline, pitch, pdf_path


    generate_button.click(fn=wrapper, inputs=theme_input, outputs=[idea_output, tagline_output, pitch_output, pdf_output])


demo.launch(share=True)

We have selected the Gradio user interface for the start -up stadium. Using GR.Blocks () it creates a clean design with a user’s input box to enter the feature or startup manufacture and a button to run the stadium generation. Once clicked, the Wrapper Convention_startup_Pitch function requires, return the start -up idea, description logo, stadium summary, and downloadable PDF. Share = True Flag allows general access to the application, making it easy to experimental or share the tool with others via the unique URL.

The application interface to create ideas

In conclusion, by combining the power of the abstraction of Litellm with the creative intelligence of the Goeni Pro from Google, this tutorial shows how developers can enable the smart model and prepare for production. Litellm greatly simplifies the work with various LLM applications programming facades by maintaining a recall interface similar to OpenAi consistent with service providers such as Gemini, Claude, Openai and more. Through Gradio, we added an intuitive front interface to accept the results of the user’s entry and display, while FPDF allowed us to convert the AI’s content to PDF documents that can be well shared. This tutorial shows how to create a multi -component Amnesty International application in a friendly friendly environment and emphasizes the role of Litellm as a pivotal gate on the increasing ecosystem of language models. Whether you are building MVPS or production tools, Litellm provides flexibility and expansion to maintain LLM workflow quickly and future.

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Asif Razzaq is the CEO of Marktechpost Media Inc .. As a pioneer and vision engineer, ASIF is committed to harnessing the potential of artificial intelligence for social goodness. His last endeavor is to launch the artificial intelligence platform, Marktechpost, which highlights its in -depth coverage of machine learning and deep learning news, which is technically sound and can be easily understood by a wide audience. The platform is proud of more than 2 million monthly views, which shows its popularity among the masses.