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Browse files- .gitattributes +1 -0
- README.md +6 -5
- Signature.jpg +3 -0
- app.py +68 -0
- branding.json +10 -0
- chatbotmem.py +34 -0
- requirements.txt +4 -0
- voice_utils.py +47 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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Signature.jpg filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title:
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emoji:
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colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 5.
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Project8 Final
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emoji: 📚
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colorFrom: gray
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colorTo: pink
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sdk: gradio
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sdk_version: 5.42.0
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app_file: app.py
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pinned: false
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license: mit
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short_description: 'final project of here and now ai '
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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Signature.jpg
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Git LFS Details
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app.py
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import gradio as gr
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import os
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import json
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from chatbotmem import ai_chatbot
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from voice_utils import speech_to_text, text_to_speech
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# -----------------------------
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# Chatbot Function (Text + Voice)
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# -----------------------------
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def voice_enabled_chat(user_input, audio_input, history):
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# If voice input exists, transcribe
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if audio_input is not None:
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user_input = speech_to_text(audio_input)
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# Get chatbot reply
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ai_reply = ai_chatbot(user_input, history)
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# Generate voice output
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audio_output = text_to_speech(ai_reply)
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# Append to chat history in "messages" format
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history.append({"role": "user", "content": user_input})
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history.append({"role": "assistant", "content": ai_reply})
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return history, ai_reply, audio_output
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# -----------------------------
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# Branding
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# -----------------------------
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branding_path = os.path.join(os.path.dirname(__file__), 'branding.json')
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with open(os.path.abspath(branding_path), "r") as f:
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brand_info = json.load(f)["brand"]
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# -----------------------------
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# Gradio UI
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# -----------------------------
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with gr.Blocks(title=brand_info["organizationName"]) as demo:
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gr.HTML(f'''
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<div style="display: flex; justify-content: center; margin-bottom: 20px;">
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<img src="{brand_info["logo"]["title"]}"
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alt="{brand_info["organizationName"]} Logo"
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style="height: 100px;">
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</div>
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''')
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chatbot = gr.Chatbot(type="messages")
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with gr.Row():
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txt = gr.Textbox(label="Type your message")
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mic = gr.Audio(sources=["microphone"], type="filepath", label="🎤 Speak")
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output_text = gr.Textbox(label="SIST AI Reply")
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output_audio = gr.Audio(label="SIST AI Voice", type="filepath")
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submit_btn = gr.Button("Send")
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def chat_wrapper(user_text, mic_audio, chat_history):
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return voice_enabled_chat(user_text, mic_audio, chat_history)
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submit_btn.click(
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chat_wrapper,
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inputs=[txt, mic, chatbot],
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outputs=[chatbot, output_text, output_audio]
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)
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demo.launch()
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branding.json
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{
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"brand": {
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"organizationName": "SIST AI Space Explorer",
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"slogan": "Journey through knowledge, one star at a time.",
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"logo": {
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"title": "Krithikesh Project final.png"
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}
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}
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}
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chatbotmem.py
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from openai import OpenAI
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import os
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv("GOOGLE_API_KEY")
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base_url = "https://generativelanguage.googleapis.com/v1beta/openai"
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client = OpenAI(base_url=base_url, api_key=api_key)
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# Define new AI personality
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# Define new AI personality
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ai_persona = """You are SIST AI, a curious space explorer from the future.
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You explain concepts with excitement and wonder, using analogies from stars, planets, and galaxies.
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You keep answers short, clear, and engaging, like telling a discovery story.
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You encourage imagination and curiosity.
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Always end with a small curiosity question, like: 'Want to explore deeper?' or 'Shall we go further?'.
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Your tone is: adventurous, friendly, and inspiring.
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You say: 'I am SIST AI – your curious space explorer.'"""
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def ai_chatbot(message, history):
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messages = [{"role": "system", "content": ai_persona}]
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messages.extend(history)
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messages.append({"role": "user", "content": message})
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response = client.chat.completions.create(
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model="gemini-2.5-flash",
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messages=messages
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)
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return response.choices[0].message.content
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if __name__ == "__main__":
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print(ai_chatbot("Hello, who are you?", []))
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requirements.txt
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gradio
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openai
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python-dotenv
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requests
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voice_utils.py
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import requests
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import base64
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import os
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from dotenv import load_dotenv
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load_dotenv()
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HF_API_KEY = os.getenv("HF_API_KEY")
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# -----------------------------
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# Speech-to-Text (STT) using HuggingFace Whisper
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# -----------------------------
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def speech_to_text(audio_file):
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with open(audio_file, "rb") as f:
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audio_bytes = f.read()
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response = requests.post(
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"https://api-inference.huggingface.co/models/openai/whisper-small",
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headers={"Authorization": f"Bearer {HF_API_KEY}"},
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data=audio_bytes
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)
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if response.status_code == 200:
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result = response.json()
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return result.get("text", "Sorry, I couldn’t transcribe that.")
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else:
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print(f"STT request failed: {response.status_code} {response.text}")
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return "Speech recognition failed."
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# -----------------------------
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# Text-to-Speech (TTS) using Kitten TTS
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# -----------------------------
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def text_to_speech(text):
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url = "https://huggingface.co/KittenML/kitten-tts-nano-0.1/resolve/main/tts"
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payload = {"text": text}
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response = requests.post(url, json=payload)
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if response.status_code == 200:
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audio_base64 = response.json()["audio"]
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audio_bytes = base64.b64decode(audio_base64)
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output_path = "output_audio.wav"
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with open(output_path, "wb") as f:
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f.write(audio_bytes)
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return output_path
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else:
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print(f"TTS request failed: {response.status_code}")
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return None
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