Create app.py

app.py

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+from langchain.llms import HuggingFacePipeline
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM
+
+from components import caption_chain, tag_chain
+from components import pexels
+import os
+
+model = AutoModelForSeq2SeqLM.from_pretrained("declare-lab/flan-alpaca-gpt4-xl")
+tokenizer = AutoTokenizer.from_pretrained("declare-lab/flan-alpaca-gpt4-xl")
+
+pipe = pipeline(
+    'text2text-generation',
+    model=model,
+    tokenizer= tokenizer,
+    max_length=120
+)
+
+local_llm = HuggingFacePipeline(pipeline=pipe)
+
+llm_chain = caption_chain.chain(llm=local_llm)
+sum_llm_chain = tag_chain.chain(llm=local_llm)
+
+pexels_api_key = os.getenv('pexels_api_key')
+
+def pred():
+    folder_name, sentences = pexels.generate_videos("Bluetooth Earphone", pexel_api_key, 1920, 1080)
+    pexels.combine_videos(folder_name)
+    return {
+        'video':folder_name, 
+        'captions':sentences.join("\n")
+    }
+
+with gr.Blocks() as demo:
+    textbox = gr.Textbox("Product Name")
+    captions = gr.Textbox()
+    video = gr.Video()
+    btn = gr.Button("Submit")
+    btn.click(pred, inputs=textbox, outputs=[captions,video])
+    
+    
+
+demo.launch()