Update app.py

app.py

@@ -1,88 +1,28 @@
-import openai
-import gradio as gr
-import time
-import warnings
-import warnings
-import os
-from gtts import gTTS
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-
-warnings.filterwarnings("ignore")
-
-openai.api_key = "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"
-
-
-def chatgpt_api(input_text):
-    '''messages = [
-    {"role": "system", "content": "You are a helpful assistant."}]
-    
-    if input_text:
-        messages.append(
-            {"role": "user", "content": input_text},
-        )
-        chat_completion = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo", messages=messages
+import gradio as gr
+def chatbot(input):
+    if input:
+        input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
+        # concatenate new user input with chat history (if there is)
+        bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) 
+        # generate a bot response
+        chat_history_ids = model.generate(
+            bot_input_ids,
+            max_length=1000,
+            do_sample=True,
+            top_p=0.95,
+            top_k=0,
+            temperature=0.75,
+            pad_token_id=tokenizer.eos_token_id
         )
-    
-    reply = chat_completion.choices[0].message.content'''
-    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
-    # concatenate new user input with chat history (if there is)
-    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1)
-    # generate a bot response
-    chat_history_ids = model.generate(
-        bot_input_ids,
-        max_length=1000,
-        do_sample=True,
-        top_p=0.95,
-        top_k=0,
-        temperature=0.75,
-        pad_token_id=tokenizer.eos_token_id
-    )
-    #print the output
-    reply = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
-
-    return reply
-
-#ffmpeg -f lavfi -i anullsrc=r=44100:cl=mono -t 10 -q:a 9 -acodec libmp3lame Temp.mp3'
-
-def transcribe(audio, text):
-    language = "en"
-
-    if audio is not None:
-        with open(audio, "rb") as transcript:
-            prompt = openai.Audio.transcribe("whisper-1", transcript)
-        s = prompt["text"]
-    else:
-        s = text
-    
-    response = openai.Completion.create(
-        engine="text-davinci-002",
-        prompt=s,
-        max_tokens=60,
-        n=1,
-        stop=None,
-        temperature=0.5,
-    )
-    
-    out_result = chatgpt_api(s)
-    
-    audioobj = gTTS(text = out_result, 
-                    lang = language,  
-                    slow = False)
-    
-    audioobj.save("Temp.mp3")
-
-    return [s, out_result, "Temp.mp3"]
+        #print the output
+        output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+        return reply 
 
+inputs = gr.inputs.Textbox(lines=7, label="Chat with AI")
+outputs = gr.outputs.Textbox(label="Reply")
 
-with gr.Blocks() as demo:
-    gr.Markdown("Dilip can finally talk!?")
-    input1 = gr.inputs.Audio(source="microphone", type = "filepath", label="Use your voice to chat")
-    input2 = gr.inputs.Textbox(lines=7, label="Chat with AI")
-    output_1 = gr.Textbox(label="Text Input")
-    output_2 = gr.Textbox(label="Text Output")
-    output_3 = gr.Audio("Temp.mp3", label="Speech Output")
-    btn = gr.Button("Run")
-    btn.click(fn=transcribe, inputs=[input1, input2], outputs=[output_1, output_2, output_3])
-demo.launch(share=True)
+gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title="Boba Jarvis",
+             description="Ask anything you want",
+             ).launch(share=True)