Added text generation via Mistral-7B-instruct

app.py

@@ -2,12 +2,21 @@ import gradio as gr
 import torch
 import torchaudio
 from diffusers import StableDiffusionPipeline
+from transformers import pipeline
 from TTS.api import TTS
 import moviepy.editor as mp
 import numpy as np
 import os
 from PIL import Image, ImageDraw, ImageFont
 
+def generate_script(topic):
+    """Uses an open-source LLM to generate an engaging script of 8-10 minutes."""
+    llm = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct")
+    prompt = (f"Write an engaging and informative script on the topic '{topic}'. "
+              "The text should take about 8-10 minutes to read aloud at a normal pace.")
+    response = llm(prompt, max_length=1500, do_sample=True, temperature=0.7)
+    return response[0]['generated_text']
+
 def create_centered_title(image_size, text, max_font_size=50, min_font_size=10, padding=20):
     """Creates a title image with auto-adjusting text size to fit within the image."""
     title_img = Image.new("RGB", image_size, (0, 0, 0))
@@ -121,7 +130,9 @@ def process_text(text, movie_title, image_size, use_diffusion, num_steps):
 
 with gr.Blocks() as demo:
     gr.Markdown("# Text-to-Video Generator for YouTubers using AI 🎥")
-    text_input = gr.Textbox(label="Enter your text")
+    text_input = gr.Textbox(label="Enter your text (or leave empty to use a topic)")
+    topic_input = gr.Textbox(label="Or enter a topic to generate text", placeholder="Example: The Future of AI")
+
     movie_title_input = gr.Textbox(label="Movie Title", value="")
     file_input = gr.File(label="Or upload a .txt file")
     image_size_input = gr.Radio(choices=["640x480", "800x600", "1024x768"], label="Select Image Size", value="640x480")
@@ -130,9 +141,11 @@ with gr.Blocks() as demo:
     process_btn = gr.Button("Generate Video")
     output_video = gr.Video()
     
-    def handle_request(text, movie_title, file, image_size, use_diffusion, num_steps):
+    def handle_request(text, topic, movie_title, file, image_size, use_diffusion, num_steps):
         if file is not None:
             text = open(file.name, "r").read()
+        elif not text and topic:
+            text = generate_script(topic)
         image_size_dict = {"640x480": (640, 480), "800x600": (800, 600), "1024x768": (1024, 768)}
         return process_text(text, movie_title, image_size_dict[image_size], use_diffusion, num_steps)