Update app.py

app.py

@@ -1,18 +1,157 @@
 import gradio as gr
 from transformers import pipeline
+import librosa
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+########################ASR model###############################
 
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Hot Dog? Or Not?",
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+# load model and processor
+processor = WhisperProcessor.from_pretrained("openai/whisper-base")
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base")
+model.config.forced_decoder_ids = None
+
+sample_rate = 16000
+
+def ASR_model(audio, sr=16000):
+    DB_audio = audio
+    input_features = processor(audio, sampling_rate=sr, return_tensors="pt").input_features
+    # generate token ids
+    predicted_ids = model.generate(input_features)
+    # decode token ids to text
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
+
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+
+    return transcription
+
+########################LLama model###############################
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name_or_path = "TheBloke/llama2_7b_chat_uncensored-GPTQ"
+# To use a different branch, change revision
+# For example: revision="main"
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
+                                             device_map="auto",
+                                             trust_remote_code=True,
+                                             revision="main",
+                                             #quantization_config=QuantizationConfig(disable_exllama=True)
+                                             )
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+Llama_pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=20,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95,
+    top_k=40,
+    repetition_penalty=1.1
 )
+history="""User: Hello, Rally?
+Rally: I'm happy to see you again. What you want to talk to day?
+User: Let's talk about food
+Rally: Sure.
+User: I'm hungry right now. Do you know any Vietnamese food?"""
+
+prompt_template = f"""<|im_start|>system
+Talk one sentence to continue the conversation<|im_end|>
+{history}
+Rally:"""
+print(Llama_pipe(prompt_template)[0]['generated_text'])
+
+def RallyRespone(chat_history, message):
+    chat_history += "User: " + message + "\n"
+    t_chat = Llama_pipe(prompt_template)[0]['generated_text']
+    res = t_chat[t_chat.rfind("Rally: "):]
+    return res
+
+########################Gradio UI###############################
+    
+# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
+def add_file(files):
+    return files.name
+
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+def upfile(files):
+    x = librosa.load(files, sr=16000)
+    print(x[0])
+    text = ASR_model(x[0])
+    return [text[0], text[0]]
+
+def transcribe(audio):
+    sr, y = audio
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+
+    return transcriber({"sampling_rate": sr, "raw": y})["text"], transcriber({"sampling_rate": sr, "raw": y})["text"]
+
+
+# def recommand(text):
+#     ret = "answer for"
+
+#     return ret + text
+
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, gr.Textbox(value="", interactive=False)
+
+# def bot(history):
+#     response = "**That's cool!**"
+#     history[-1][1] = ""
+#     for character in response:
+#         history[-1][1] += character
+#         time.sleep(0.05)
+#         yield history
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(
+        [],
+        elem_id="chatbot",
+        bubble_full_width=False,
+    )
+    file_output = gr.File()
+
+    def respond(message, chat_history):
+        bot_message = RallyRespone(chat_history, message)
+
+
+        chat_history.append((message, bot_message))
+        time.sleep(2)
+        print (chat_history[-1])
+        return chat_history[-1][-1], chat_history
+
+    with gr.Row():
+        with gr.Column():
+            audio_speech = gr.Audio(sources=["microphone"])
+            submit = gr.Button("Submit")
+            send = gr.Button("Send")
+            btn = gr.UploadButton("📁", file_types=["audio"])
+
+        with gr.Column():
+            opt1 = gr.Button("1: ")
+            opt2 = gr.Button("2: ")
+
+    #submit.click(translate, inputs=audio_speech, outputs=[opt1, opt2])
+    # output is opt1 value, opt2 value [ , ]
+
+    file_msg = btn.upload(add_file, btn, file_output)
+    submit.click(upfile, inputs=file_output, outputs=[opt1, opt2])
+    send.click(transcribe, inputs=audio_speech, outputs=[opt1, opt2])
+    opt1.click(respond, [opt1, chatbot], [opt1, chatbot])
+
+    opt2.click(respond, [opt2, chatbot], [opt2, chatbot])
+
+    #opt2.click(recommand, inputs=opt2)
+    #click event maybe BOT . generate history = optx.value,
+
+    chatbot.like(print_like_dislike, None, None)
 
 if __name__ == "__main__":
-    gradio_app.launch()
+    demo.queue()
+    demo.launch(debug=True)