Step-by-step guide that makes Gemino-Powered Ai Startup Pitch Generator using Litoll Farkwork, Gradio, and FPDF in Google Colab with PDF Export support
In this lesson, we create a powerful and effective AI app that produces implementation ideas through the Geminic Pro engine for the frame of variable Gels. Grass Was the backbone of this implementation, providing an integrated interaction interface with more than 100 llm suppliers using compatible APIs, ending difficulties to deal with each SDKs. By installing Lidellm, we are connected without a seam of Gemini's ability to create creativity and disregarding the interface. Also, we used FPDF to generate the compatible, compatible PDFs containing the full Pitch Dek. This lesson shows how much ai tooling, including Litolpm, Gradio, Gradio, the productive Google of Ai, and FPDF, can create a final solution for business, founders and developers.
!pip install litellm gradio fpdf --quiet! PIP Install Layollm Gradio FPDF -QI install key information libraries required in this project. Bring a Gemini's social networking, gradio, and FPDF to shipping a well-pedf file in the well-redeemed PDF file – everything while pressing Vermose Input logs.
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 important Psythian libraries used in this work, including the file operation, the UUID of the formation of different symbols, and the Mrllib downloading fonts. We have also launched the UI Gradio, FPDF for the PDF and Laelll Completion Activity in Interface in Gemini. API_key variables saves the Gemini API Gemini API key, which needs to confirm 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.")
breakHere, we ensure that the Ejavusans.ttf font is available to create compatible PDFs. Download the Font Zip file from Soccedage, issue its content, and copy of the .tttttty file to the active indicator. This step is important to handling special characters from Gemini's output when producing the last 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 work, Call_Gind, the Wrapper using LidellM Lifting to complete API to contact Gemini 2.0 Flash Lite Lite. Accept System Prompt and a quick user, will be charged in an open format, sending an application using a given API key, and returns a produced response – making it easy to re-use in all different parts of the app.
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}nnTagline:n{tagline}nnPitch 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}", "", "", NoneThe function of production_startup_patch orchestrates all startup starts. Sends Gemini's motivation with Lighmi with LiterLM to produce a starting point of view, Tatchy Technique, and a fixed pitch porch. The answers are combined into the formatted PDF using FPDF, with appropriate Unicode support with Dijavu Font. The PDF is saved in a unique file name, which makes users to download their own-made voice. Error hosting ensures the operation of feedback and user's response 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 described the Gradio user interface of Ai Startup Pitch Generator. Using Gr.blocks () creates pure shape with the user's installation box to set a start-up theme or industry and the removal button. If you clicked, WRAPPER'S WORKSHIPLINED WRAPERSE_Starup_pitch, returns the concept of getting started, Tagine, the pitch summary, and the downloadable PDF. The Shuna = a true flag enables social access to the app, making it easier to defile or share tools with others with unique URL.
Application template to produce ideas
In conclusion, by combining Lidellm's power and design of Geminic Pro, the lesson informs how quickly the developers can evaluate the intelligence, production of production requests. Lilollm is very active working with a variety of the various LLM by maintaining an open driving interface across the providers, Claude, Ounai, and more. By using Gradio, we have added accurate privacy to accept user's installation and display results, while FPDF allows us to modify the AI content to the shared PDF documents, forming well. This lesson shows how we can build a lot of AI's AI in the environment that has columns and emphasizes the role of Livotal gate as the pivotal gate to an ecosyystem of the language. Whether you create MVP tools or production, Liallm offers fluctuations and stability to keep your llm travel early and there are coming testimony.
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