Create app.py

app.py

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+import whisper 
+import pytube
+import gradio as gr
+import openai
+import faiss
+from datetime import datetime
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores.faiss import FAISS
+from langchain.chains import RetrievalQAWithSourcesChain
+from langchain import OpenAI
+from langchain.vectorstores.base import VectorStoreRetriever
+import os
+
+video_data_cache = {}
+
+def get_answer(api_key, video_link, question):
+    os.environ["OPENAI_API_KEY"] = api_key
+
+    if video_link not in video_data_cache:
+        video = pytube.YouTube(video_link)
+        audio = video.streams.get_audio_only()
+        fn = audio.download(output_path="tmp.mp3")
+        model = whisper.load_model("base")
+        transcription = model.transcribe(fn)
+        res = transcription['text']
+
+        def store_segments(text):
+            segment_size = 1000
+            segments = [{'text': text[i:i+segment_size], 'start': i} for i in range(0, len(text), segment_size)]
+
+            texts = []
+            start_times = []
+
+            for segment in segments:
+                text = segment['text']
+                start = segment['start']
+
+                start_datetime = datetime.fromtimestamp(start)
+                formatted_start_time = start_datetime.strftime('%H:%M:%S')
+
+                texts.append(text)
+                start_times.append(formatted_start_time)
+
+            return texts, start_times
+
+        texts, start_times = store_segments(res)
+
+        text_splitter = CharacterTextSplitter(chunk_size=1500, separator="\n")
+        docs = []
+        metadatas = []
+        for i, d in enumerate(texts):
+            splits = text_splitter.split_text(d)
+            docs.extend(splits)
+            metadatas.extend([{"source": start_times[i]}] * len(splits))
+
+        embeddings = OpenAIEmbeddings()
+        store = FAISS.from_texts(docs, embeddings, metadatas=metadatas)
+        faiss.write_index(store.index, f"docs.index")
+
+        video_data_cache[video_link] = f"docs.index"
+    
+    index_file = video_data_cache[video_link]
+    store = faiss.read_index(index_file)
+
+    retri = VectorStoreRetriever(vectorstore=store)
+
+    chain = RetrievalQAWithSourcesChain.from_llm(llm=OpenAI(temperature=0), retriever=retri)
+
+    result = chain({"question": question})
+
+    return result['answer'], result['sources']
+
+
+iface = gr.Interface(
+    fn=get_answer,
+    inputs=["text", "text", "text"],
+    outputs=["text", "text"],
+    examples=[
+        ["sk-kVc5h5YtNXyD6WxUd4aSxIyWuGc", "https://www.youtube.com/watch?v=xNAm9O_duSA", "Who could be the next Prime Minister ?"]
+    ],
+)
+
+iface.queue().launch()