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app.py

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+import gradio as gr
+from langchain.document_loaders import UnstructuredURLLoader
+import os
+from langchain import PromptTemplate, HuggingFaceHub, LLMChain
+from gtts import gTTS
+from IPython.display import Audio
+
+from dotenv import load_dotenv 
+load_dotenv()  # to load all the env variables
+
+os.environ['HUGGINGFACEHUB_API_TOKEN']  = os.getenv("HUGGINGFACE_API_TOKEN")
+
+
+def get_summary_from_url(url):
+    loaders = UnstructuredURLLoader(urls=[url])
+    data = loaders.load()
+    question = data[0]
+    
+    template = """{question}"""
+    prompt = PromptTemplate(template=template, input_variables=["question"])
+    
+    llm_chain = LLMChain(prompt=prompt,
+                         llm=HuggingFaceHub(repo_id="Mr-Vicky-01/conversational_sumarization",
+                                            model_kwargs={"max_length":100,
+                                                          "max_new_tokens":100,
+                                                          "do_sample": False}))
+    answer = llm_chain.run(question)
+    return answer
+
+def text_to_speech(text, language='en'):
+    tts = gTTS(text=text, lang=language)
+    tts.save("output.mp3")
+    return "output.mp3"
+
+def summarize_and_convert_to_audio(url):
+    summary = get_summary_from_url(url)
+    audio_file = text_to_speech(summary)
+    return audio_file
+
+iface = gr.Interface(fn=summarize_and_convert_to_audio, inputs="text", outputs="audio", title="Text Summarization & Audio Generation", description="Enter the URL of the article to summarize and convert to audio.")
+iface.launch()

requirements.txt

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+unstructured==0.9.2
+langchain
+huggingface_hub
+transformers
+sentence_transformers
+gTTS
+gradio
+langchain-community
+IPython