Rename requirements.txt to app.py

app.py

@@ -0,0 +1,51 @@
+# app.py
+import os
+import gradio as gr
+from transformers import pipeline
+from diffusers import StableDiffusionPipeline
+
+# Install necessary dependencies
+os.system("pip install transformers diffusers gradio")
+
+# Text Understanding with Hugging Face Transformers
+def analyze_text(input_text):
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+    summary = summarizer(input_text, max_length=50, min_length=10, do_sample=False)[0]['summary_text']
+
+    ner_pipeline = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
+    entities = ner_pipeline(input_text)
+    key_entities = [entity["word"] for entity in entities]
+
+    return summary, key_entities
+
+# Generate Images Using Stable Diffusion
+def generate_images(prompts):
+    model_id = "CompVis/stable-diffusion-v1-4"
+    sd_pipeline = StableDiffusionPipeline.from_pretrained(model_id)
+    images = []
+    for prompt in prompts:
+        image = sd_pipeline(prompt).images[0]
+        images.append(image)
+    return images
+
+# Gradio App Interface
+def generate_video_from_text(input_text):
+    # Analyze text
+    summary, key_entities = analyze_text(input_text)
+    
+    # Generate prompts and images
+    prompts = [f"{entity}, cinematic, ultra-realistic" for entity in key_entities]
+    images = generate_images(prompts)
+
+    return summary, images
+
+interface = gr.Interface(
+    fn=generate_video_from_text,
+    inputs="text",
+    outputs=["text", gr.outputs.Image(type="pil", label="Generated Images")],
+    title="Hugging Face Text Analysis & Image Generator",
+    description="Analyze text to extract key entities and generate corresponding images using open-source AI models."
+)
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

@@ -1,4 +0,0 @@
-groq 
-transformers 
-diffusers 
-gradio