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| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # """ | |
| # For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| # """ | |
| # client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct") | |
| # def respond(message, history: list[tuple[str, str]]): | |
| # system_message = "You are a friendly Chatbot. If the user query is product-related, provide structured product recommendations based on intent and relevance." | |
| # max_tokens = 2048 | |
| # temperature = 0.7 | |
| # top_p = 0.95 | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # # Append product recommendation prompt if the user query is relevant | |
| # product_prompt = ("Given a user's search query, recommend the most relevant products from the catalog. " | |
| # "Consider synonyms, user intent, and semantic meaning rather than just keyword matching. " | |
| # "If the query is vague, infer potential needs based on common shopping behavior. " | |
| # "Provide a ranked list of product recommendations with a short explanation for each suggestion. " | |
| # "Ensure the recommendations are diverse and cover multiple relevant categories if applicable. " | |
| # f"Now, based on the user query: '{message}', generate a well-structured product recommendation list.") | |
| # messages.append({"role": "user", "content": product_prompt}) | |
| # response = "" | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = message.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| # """ | |
| # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| # """ | |
| # demo = gr.ChatInterface(respond) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # import tempfile | |
| # # Initialize clients | |
| # chat_client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct") | |
| # stt_client = InferenceClient("openai/whisper-large-v3") | |
| # def transcribe_audio(audio_file): | |
| # """Convert audio to text using Whisper model""" | |
| # with open(audio_file, "rb") as f: | |
| # return stt_client.automatic_speech_recognition(f.read()) | |
| # def respond(history, query): | |
| # system_message = "You are a friendly Chatbot. Provide structured product recommendations based on user queries." | |
| # messages = [{"role": "system", "content": system_message}] | |
| # # Build conversation history | |
| # for entry in history: | |
| # messages.append({"role": "user", "content": entry[0]}) | |
| # if entry[1]: # Only add assistant response if present | |
| # messages.append({"role": "assistant", "content": entry[1]}) | |
| # # Add product recommendation prompt | |
| # product_prompt = ("Analyze this query and provide recommendations: ") | |
| # messages.append({"role": "user", "content": f"{product_prompt}\n{query}"}) | |
| # # Generate streamed response | |
| # response = "" | |
| # for chunk in chat_client.chat_completion( | |
| # messages, | |
| # max_tokens=2048, | |
| # stream=True, | |
| # temperature=0.7, | |
| # top_p=0.95, | |
| # ): | |
| # token = chunk.choices[0].delta.content | |
| # response += token | |
| # history[-1] = (query, response) # Update last history entry | |
| # yield history | |
| # # Custom CSS for better styling | |
| # css = """ | |
| # .gradio-container { background: #f0f4f8 !important; } | |
| # .audio-input { background: white !important; border-radius: 10px; } | |
| # """ | |
| # with gr.Blocks(css=css) as demo: | |
| # gr.Markdown("# Smart Product Assistant 🎤🛍️") | |
| # with gr.Row(): | |
| # chatbot = gr.Chatbot(height=600) | |
| # with gr.Column(): | |
| # with gr.Tab("Text Input"): | |
| # text_input = gr.Textbox(label="Type your query") | |
| # with gr.Tab("Voice Input"): | |
| # audio_input = gr.Audio( | |
| # sources="microphone", | |
| # type="filepath", | |
| # label="Record your query", | |
| # elem_classes="audio-input" | |
| # ) | |
| # submit_btn = gr.Button("Submit", variant="primary") | |
| # def process_inputs(text, audio, history): | |
| # """Handle both text and audio inputs""" | |
| # query = text.strip() | |
| # if audio and not query: | |
| # query = transcribe_audio(audio) | |
| # if query: | |
| # # Add new entry to history with empty response | |
| # return history + [[query, None]] | |
| # return history | |
| # submit_btn.click( | |
| # process_inputs, | |
| # [text_input, audio_input, chatbot], | |
| # chatbot, | |
| # queue=False | |
| # ).then( | |
| # respond, | |
| # [chatbot, text_input], | |
| # chatbot | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # import tempfile | |
| # # Initialize clients | |
| # chat_client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct") | |
| # stt_client = InferenceClient("openai/whisper-large-v3") | |
| # def transcribe_audio(audio_file): | |
| # """Convert audio to text using Whisper model""" | |
| # with open(audio_file, "rb") as f: | |
| # result = stt_client.automatic_speech_recognition(f.read()) | |
| # return result.text # Extract only the text from the response | |
| # def respond(history, query): | |
| # system_message = """You are a friendly Product Assistant. Follow these rules: | |
| # 1. If the query is product-related, provide structured recommendations | |
| # 2. Consider both voice and text inputs equally | |
| # 3. Format responses with bullet points and emojis | |
| # 4. Always acknowledge voice queries specifically""" | |
| # product_prompt = f"""Analyze this {'voice' if history[-1][0] == query else 'text'} query: | |
| # "{query}" | |
| # Recommend products considering: | |
| # - User intent | |
| # - Semantic meaning | |
| # - Potential use cases | |
| # - Price ranges | |
| # Provide ranked list with brief explanations""" | |
| # messages = [ | |
| # {"role": "system", "content": system_message}, | |
| # {"role": "user", "content": product_prompt} | |
| # ] | |
| # # Build conversation history | |
| # for entry in history[:-1]: # Exclude current query | |
| # messages.extend([ | |
| # {"role": "user", "content": entry[0]}, | |
| # {"role": "assistant", "content": entry[1]} | |
| # ]) | |
| # # Generate streamed response | |
| # response = "" | |
| # for chunk in chat_client.chat_completion( | |
| # messages, | |
| # max_tokens=2048, | |
| # stream=True, | |
| # temperature=0.7, | |
| # top_p=0.95, | |
| # ): | |
| # token = chunk.choices[0].delta.content | |
| # response += token | |
| # history[-1] = (history[-1][0], response) # Update last entry | |
| # yield history | |
| # # Custom styling | |
| # css = """ | |
| # .gradio-container { background: #f5f7fa !important; } | |
| # .audio-input { background: white !important; border-radius: 10px; } | |
| # """ | |
| # with gr.Blocks(css=css) as demo: | |
| # gr.Markdown("# 🎤 Voice-Activated Product Advisor 🛍️") | |
| # with gr.Row(): | |
| # chatbot = gr.Chatbot(height=600, bubble_full_width=False) | |
| # with gr.Column(): | |
| # with gr.Tab("🎙️ Voice Input"): | |
| # audio_input = gr.Audio( | |
| # sources="microphone", | |
| # type="filepath", | |
| # label="Speak your product request", | |
| # elem_classes="audio-input" | |
| # ) | |
| # with gr.Tab("📝 Text Input"): | |
| # text_input = gr.Textbox(label="Type your request") | |
| # submit_btn = gr.Button("🚀 Get Recommendations", variant="primary") | |
| # def process_inputs(text, audio, history): | |
| # """Handle both input types""" | |
| # query = text.strip() | |
| # if audio: | |
| # query = transcribe_audio(audio) | |
| # # Add voice-specific marker | |
| # query = f"🎤 Voice Query: {query}" | |
| # if query: | |
| # return history + [(query, None)] # Proper tuple format | |
| # return history | |
| # submit_btn.click( | |
| # process_inputs, | |
| # [text_input, audio_input, chatbot], | |
| # chatbot, | |
| # queue=False | |
| # ).then( | |
| # respond, | |
| # [chatbot, text_input], | |
| # chatbot | |
| # ) | |
| # # Clear inputs after submission | |
| # submit_btn.click( | |
| # lambda: [None, None], # Clear audio and text inputs | |
| # outputs=[text_input, audio_input] | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import tempfile | |
| # Initialize clients | |
| chat_client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct") | |
| stt_client = InferenceClient("openai/whisper-large-v3") | |
| def transcribe_audio(audio_file): | |
| """Convert audio to text using Whisper model""" | |
| try: | |
| with open(audio_file, "rb") as f: | |
| result = stt_client.automatic_speech_recognition(f.read()) | |
| return result.text | |
| except Exception as e: | |
| print(f"Transcription error: {e}") | |
| return "Could not process audio. Please try again." | |
| def respond(history, query): | |
| system_message = """You are a Voice-Aware Product Assistant. Rules: | |
| 1. Always acknowledge voice queries with 🎧 icon | |
| 2. Provide structured recommendations with emojis | |
| 3. Consider both voice and text inputs equally""" | |
| product_prompt = f"""Analyze this {'🎧 VOICE' if '🎧' in query else '📝 TEXT'} query: | |
| "{query.replace('🎧 VOICE: ', '')}" | |
| Recommend products considering: | |
| - Voice tone analysis (if audio) | |
| - Semantic meaning | |
| - User intent | |
| - Price context""" | |
| messages = [ | |
| {"role": "system", "content": system_message}, | |
| {"role": "user", "content": product_prompt} | |
| ] | |
| # Generate streamed response | |
| response = "" | |
| for chunk in chat_client.chat_completion( | |
| messages, | |
| max_tokens=2048, | |
| stream=True, | |
| temperature=0.7, | |
| top_p=0.95, | |
| ): | |
| token = chunk.choices[0].delta.content | |
| response += token | |
| history[-1] = (history[-1][0], response) | |
| yield history | |
| css = """ | |
| .gradio-container { background: #f8f9fa !important; } | |
| .audio-input { background: white !important; border-radius: 10px; } | |
| .mic-status { color: #4a90e2; font-weight: bold; } | |
| """ | |
| with gr.Blocks(css=css, title="Voice Product Assistant") as demo: | |
| gr.Markdown("# 🎧 Voice-Activated Product Advisor 🛒") | |
| with gr.Row(): | |
| chatbot = gr.Chatbot(height=600, bubble_full_width=False) | |
| with gr.Column(): | |
| # Audio input with status indicator | |
| with gr.Group(): | |
| audio_input = gr.Audio( | |
| sources="microphone", | |
| type="filepath", | |
| label="Click mic & speak", | |
| elem_classes="audio-input", | |
| interactive=True | |
| ) | |
| mic_status = gr.Markdown("🔴 Mic offline", elem_classes="mic-status") | |
| # Text input | |
| text_input = gr.Textbox(label="Or type your request") | |
| # Unified submit button | |
| submit_btn = gr.Button("🚀 Get Recommendations", variant="primary") | |
| # Handle audio permissions | |
| def request_mic_access(): | |
| return gr.update(text="🟢 Mic ready") if audio_input.is_enabled else gr.update(text="🔴 Mic blocked") | |
| # Process inputs | |
| def process_inputs(text, audio, history): | |
| query = text.strip() | |
| if audio: | |
| transcript = transcribe_audio(audio) | |
| query = f"🎧 VOICE: {transcript}" | |
| if query: | |
| return history + [(query, None)], "" | |
| return history, "" | |
| # Connect all components | |
| audio_input.change( | |
| request_mic_access, | |
| outputs=mic_status, | |
| queue=False | |
| ) | |
| submit_btn.click( | |
| process_inputs, | |
| [text_input, audio_input, chatbot], | |
| [chatbot, text_input], | |
| queue=False | |
| ).then( | |
| respond, | |
| [chatbot, text_input], | |
| chatbot | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(server_port=7860, share=False) | |