app_1.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import speech_recognition as sr
from gtts import gTTS
from pydub import AudioSegment
import io

device = "cuda" if torch.cuda.is_available() else "cpu"

def create_prompt_with_chat_format(messages, bos="<s>", eos="</s>", add_bos=True):
    formatted_text = ""
    for message in messages:
        if message["role"] == "system":
            formatted_text += "\n" + message["content"] + "\n"
        elif message["role"] == "user":
            formatted_text += "\n" + message["content"] + "\n"
        elif message["role"] == "assistant":
            formatted_text += "\n" + message["content"].strip() + eos + "\n"
        else:
            raise ValueError(
                "Tulu chat template only supports 'system', 'user', and 'assistant' roles. Invalid role: {}.".format(
                    message["role"]
                )
            )
    formatted_text += "\n"
    formatted_text = bos + formatted_text if add_bos else formatted_text
    return formatted_text

def inference(input_prompts, model, tokenizer):
    input_prompts = [
        create_prompt_with_chat_format([{"role": "user", "content": input_prompt}], add_bos=False)
        for input_prompt in input_prompts
    ]

    encodings = tokenizer(input_prompts, padding=True, return_tensors="pt")
    encodings = encodings.to(device)

    with torch.no_grad():
        outputs = model.generate(encodings.input_ids, do_sample=False, max_length=250)

    output_texts = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True)

    input_prompts = [
        tokenizer.decode(tokenizer.encode(input_prompt), skip_special_tokens=True) for input_prompt in input_prompts
    ]
    output_texts = [output_text[len(input_prompt) :] for input_prompt, output_text in zip(input_prompts, output_texts)]
    return output_texts

def recognize_speech():
    recognizer = sr.Recognizer()
    microphone = sr.Microphone()

    with microphone as source:
        print("Listening...")
        recognizer.adjust_for_ambient_noise(source)
        audio_data = recognizer.listen(source, timeout=5)

    try:
        print("Recognizing...")
        text = recognizer.recognize_google(audio_data, language="hi-IN")
        return text
    except sr.UnknownValueError:
        print("Speech Recognition could not understand audio.")
        return ""
    except sr.RequestError as e:
        print(f"Could not request results from Google Speech Recognition service; {e}")
        return ""

def text_to_speech(text):
    tts = gTTS(text=text, lang="hi")
    audio_stream = io.BytesIO()
    tts.save(audio_stream)
    audio = AudioSegment.from_file(io.BytesIO(audio_stream.read()), format="mp3")
    return audio

def respond_to_input(input_text):
    output_texts = inference([input_text], model, tokenizer)
    output_text = output_texts[0]
    output_audio = text_to_speech(output_text)
    return output_text, output_audio.export(format="wav")

examples = [
    ["मुझे अपने करियर के बारे में सुझाव दो", "मैं कैसे अध्ययन कर सकता हूँ?"],
    ["कृपया मुझे एक कहानी सुनाएं", "ताजमहल के बारे में कुछ बताएं"],
    ["मेरा नाम क्या है?", "आपका पसंदीदा फिल्म कौन सी है?"],
]

iface = gr.Interface(
    fn=respond_to_input,
    inputs=["text", "microphone"],
    outputs=["text", "audio"],
    live=True,
    examples=examples,
    title="CAMAI",
    description="Type or speak to me, and I'll generate a response!",
    theme="light",
)

iface.launch()






###############################################################################################################################
# import torch
# from transformers import AutoTokenizer, AutoModelForCausalLM
# import gradio as gr

# device = "cuda" if torch.cuda.is_available() else "cpu"


# def create_prompt_with_chat_format(messages, bos="<s>", eos="</s>", add_bos=True):
#     formatted_text = ""
#     for message in messages:
#         if message["role"] == "system":
#             formatted_text += "<|system|>\n" + message["content"] + "\n"
#         elif message["role"] == "user":
#             formatted_text += "<|user|>\n" + message["content"] + "\n"
#         elif message["role"] == "assistant":
#             formatted_text += "<|assistant|>\n" + message["content"].strip() + eos + "\n"
#         else:
#             raise ValueError(
#                 "Tulu chat template only supports 'system', 'user' and 'assistant' roles. Invalid role: {}.".format(
#                     message["role"]
#                 )
#             )
#     formatted_text += "<|assistant|>\n"
#     formatted_text = bos + formatted_text if add_bos else formatted_text
#     return formatted_text


# def inference(input_prompts, model, tokenizer):
#     input_prompts = [
#         create_prompt_with_chat_format([{"role": "user", "content": input_prompt}], add_bos=False)
#         for input_prompt in input_prompts
#     ]

#     encodings = tokenizer(input_prompts, padding=True, return_tensors="pt")
#     encodings = encodings.to(device)

#     with torch.inference_mode():
#         outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=250)

#     output_texts = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True)

#     input_prompts = [
#         tokenizer.decode(tokenizer.encode(input_prompt), skip_special_tokens=True) for input_prompt in input_prompts
#     ]
#     output_texts = [output_text[len(input_prompt) :] for input_prompt, output_text in zip(input_prompts, output_texts)]
#     return output_texts


# model_name = "ai4bharat/Airavata"

# tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
# tokenizer.pad_token = tokenizer.eos_token
# model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)

# def respond_to_text(input_text):
#     outputs = inference([input_text], model, tokenizer)
#     return outputs[0]


# input_prompts = [
#     "मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं।",
#     "मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं और उनका वर्णन करें।",
# ]
# iface = gr.Interface(fn=respond_to_text, inputs="text", outputs="text")
# iface.launch()
########################################################################################

# import gradio as gr
# from transformers import AutoTokenizer, AutoModelForCausalLM

# tokenizer = AutoTokenizer.from_pretrained("ai4bharat/Airavata")
# model = AutoModelForCausalLM.from_pretrained("ai4bharat/Airavata")

# def generate_response(prompt):
#     input_ids = tokenizer.encode(prompt, return_tensors="pt", max_length=50)
#     output_ids = model.generate(input_ids, max_length=100, num_beams=5, no_repeat_ngram_size=2)
#     response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
#     return response

# iface = gr.Interface(
#     fn=generate_response,
#     inputs="text",
#     outputs="text",
#     live=True,
#     title="Airavata LLMs Chatbot",
#     description="Ask me anything, and I'll generate a response!",
#     theme="light",
# )

# iface.launch()










# import gradio as gr
# import torch
# from transformers import AutoTokenizer, AutoModelForCausalLM

# device = "cuda" if torch.cuda.is_available() else "cpu"

# def create_prompt_with_chat_format(messages, bos="<s>", eos="</s>", add_bos=True):
#     formatted_text = ""
#     for message in messages:
#         if message["role"] == "system":
#             formatted_text += "\n" + message["content"] + "\n"
#         elif message["role"] == "user":
#             formatted_text += "\n" + message["content"] + "\n"
#         elif message["role"] == "assistant":
#             formatted_text += "\n" + message["content"].strip() + eos + "\n"
#         else:
#             raise ValueError(
#                 "Tulu chat template only supports 'system', 'user', and 'assistant' roles. Invalid role: {}.".format(
#                     message["role"]
#                 )
#             )
#     formatted_text += "\n"
#     formatted_text = bos + formatted_text if add_bos else formatted_text
#     return formatted_text

# def inference(input_prompts, model, tokenizer):
#     input_prompts = [
#         create_prompt_with_chat_format([{"role": "user", "content": input_prompt}], add_bos=False)
#         for input_prompt in input_prompts
#     ]

#     encodings = tokenizer(input_prompts, padding=True, return_tensors="pt")
#     encodings = encodings.to(device)

#     with torch.no_grad():
#         outputs = model.generate(encodings.input_ids, do_sample=False, max_length=250)

#     output_texts = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True)

#     input_prompts = [
#         tokenizer.decode(tokenizer.encode(input_prompt), skip_special_tokens=True) for input_prompt in input_prompts
#     ]
#     output_texts = [output_text[len(input_prompt) :] for input_prompt, output_text in zip(input_prompts, output_texts)]
#     return output_texts

# model_name = "ai4bharat/Airavata"
# tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
# tokenizer.pad_token = tokenizer.eos_token
# model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)
# examples = [
#     ["मुझे अपने करियर के बारे में सुझाव दो", "मैं कैसे अध्ययन कर सकता हूँ?"],
#     ["कृपया मुझे एक कहानी सुनाएं", "ताजमहल के बारे में कुछ बताएं"],
#     ["मेरा नाम क्या है?", "आपका पसंदीदा फिल्म कौन सी है?"],
# ]

# iface = gr.Chat(
#     model_fn=lambda input_prompts: inference(input_prompts, model, tokenizer),
#     inputs=["text"],
#     outputs="text",
#     examples=examples,
#     title="Airavata Chatbot",
#     theme="light",  # Optional: Set a light theme
# )

# iface.launch()