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Duplicate from Warlord-K/IITI-GPT

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Co-authored-by: Yatharth Gupta <[email protected]>

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  1. .gitattributes +35 -0
  2. README.md +14 -0
  3. app.py +76 -0
  4. requirements.txt +4 -0
.gitattributes ADDED
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README.md ADDED
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+ ---
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+ title: IITI GPT
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+ emoji: 💻
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+ colorFrom: green
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+ colorTo: gray
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+ sdk: gradio
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+ sdk_version: 3.39.0
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+ app_file: app.py
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+ pinned: false
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+ license: openrail
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+ duplicated_from: Warlord-K/IITI-GPT
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ from gradio_client import Client
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+ from hugchat import hugchat
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+ from hugchat.login import Login
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+ from gtts import gTTS
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+ import gradio as gr
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+
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+ client = Client("https://sanchit-gandhi-whisper-large-v2.hf.space/")
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+
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+ retrieval = Client("https://warlord-k-iiti-similarity.hf.space/")
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+
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+ n_conv = 0
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+
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+ init_prompt ="## Instruction: You are an AI language model and must return truthful responses as per the information below\n ##Input: Information: My name is IITIGPT. I am a helpful and truthful chatbot. I can help answer any questions about the IIT Indore campus."
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+ info="Information: \n"
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+ q_prompt="\n ##Instruction: Please provide an appropriate response to the following: \n"
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+
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+ def login_hf():
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+ email="[email protected]"
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+ passwd="Qwertypassword1"
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+
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+ # login
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+ sign = Login(email, passwd)
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+ cookies = sign.login()
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+
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+ # Create a ChatBot
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+ chatbot = hugchat.ChatBot(cookies=cookies.get_dict()) # or cookie_path="usercookies/<email>.json"
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+ chatbot.chat(init_prompt)
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+
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+ print("Logged In")
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+ return chatbot
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+
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+ def change_conv():
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+ # Create a new conversation
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+ id = chatbot.new_conversation()
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+ chatbot.change_conversation(id)
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+ chatbot.chat(init_prompt)
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+ chatbot.cookies = {}
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+
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+ def answer_question(question):
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+ global n_conv
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+ if(n_conv > 3):
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+ n_conv = 0
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+ change_conv(chatbot)
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+ information = retrieval.predict(question, api_name = "/predict")
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+ answer = chatbot.chat(info+ information +q_prompt+" "+question+"""\n ##Response:""")
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+ n_conv+=1
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+ return answer
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+
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+ chatbot = login_hf()
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+
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+ def file_to_text(audio_fpath):
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+
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+ result = client.predict(
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+ audio_fpath,
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+ "transcribe", # str in 'Audio input' Radio component
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+ api_name="/predict"
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+ )
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+ return result
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+
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+ def text_file(text):
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+ tts = gTTS(text, lang = "en")
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+ tts.save("abc.mp3")
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+ return "abc.mp3"
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+
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+ def main(filename):
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+ text = file_to_text(filename)
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+ print(text)
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+ answer = answer_question(text)
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+ print(answer)
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+ output = text_file(answer)
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+ return output
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+
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+ demo = gr.Interface(main, "audio", "audio")
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+
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+ if __name__ == "__main__":
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+ demo.launch()
requirements.txt ADDED
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+ hugchat
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+ gradio
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+ gradio_client
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+ gtts