Update app.py

app.py

@@ -1,80 +1,109 @@
-import gradio as gr
+# import gradio as gr
+
+# from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline
+# from transformers import BitsAndBytesConfig
+# import re
+# from deep_translator import (GoogleTranslator,
+#                              PonsTranslator,
+#                              LingueeTranslator,
+#                              MyMemoryTranslator,
+#                              YandexTranslator,
+#                              DeeplTranslator,
+#                              QcriTranslator,
+#                              single_detection,
+#                              batch_detection)
+# from pyaspeller import YandexSpeller
+
+# def error_correct_pyspeller(sample_text):
+#     """ grammer correction of input text"""
+#     speller = YandexSpeller()
+#     fixed = speller.spelled(sample_text)
+#     return fixed
+
+# def postprocerssing(inp_text: str):
+#     """Post preocessing of the llm response"""
+#     inp_text = re.sub('<[^>]+>', '', inp_text)
+#     inp_text = inp_text.split('##', 1)[0]
+#     inp_text = error_correct_pyspeller(inp_text)
+#     return inp_text
+
 
-from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline
-from transformers import BitsAndBytesConfig
-import re
-from deep_translator import (GoogleTranslator,
-                             PonsTranslator,
-                             LingueeTranslator,
-                             MyMemoryTranslator,
-                             YandexTranslator,
-                             DeeplTranslator,
-                             QcriTranslator,
-                             single_detection,
-                             batch_detection)
-from pyaspeller import YandexSpeller
-
-def error_correct_pyspeller(sample_text):
-    """ grammer correction of input text"""
-    speller = YandexSpeller()
-    fixed = speller.spelled(sample_text)
-    return fixed
-
-def postprocerssing(inp_text: str):
-    """Post preocessing of the llm response"""
-    inp_text = re.sub('<[^>]+>', '', inp_text)
-    inp_text = inp_text.split('##', 1)[0]
-    inp_text = error_correct_pyspeller(inp_text)
-    return inp_text
-
-
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype="float16",
-    bnb_4bit_use_double_quant=True,
-)
-
-llm = HuggingFacePipeline.from_model_id(
-    model_id="Danielrahmai1991/nvlm_adapt_basic_model_16bit",
-    task="text-generation",
-    pipeline_kwargs=dict(
-        max_new_tokens=512,
-        do_sample=True,
-        repetition_penalty=1.15,
-        trust_remote_code= True,
-        temperature= 0.70
-
-    ),
-  model_kwargs={"quantization_config": quantization_config,
-                },
-    )
-
-chat_model = ChatHuggingFace(llm=llm)
-
-# history of the messages
+# quantization_config = BitsAndBytesConfig(
+#     load_in_4bit=True,
+#     bnb_4bit_quant_type="nf4",
+#     bnb_4bit_compute_dtype="float16",
+#     bnb_4bit_use_double_quant=True,
+# )
 
+# llm = HuggingFacePipeline.from_model_id(
+#     model_id="Danielrahmai1991/nvlm_adapt_basic_model_16bit",
+#     task="text-generation",
+#     pipeline_kwargs=dict(
+#         max_new_tokens=512,
+#         do_sample=True,
+#         repetition_penalty=1.15,
+#         trust_remote_code= True,
+#         temperature= 0.70
+
+#     ),
+#   model_kwargs={"quantization_config": quantization_config,
+#                 },
+#     )
+
+# chat_model = ChatHuggingFace(llm=llm)
+
+# # history of the messages
+
+# def clear_memory(messages):
+#     messages.clear()
+#     return "Memory cleaned."
+    
+# def llm_run(prompt, messages):
+#     print(f"question is {prompt}")
+#     lang = single_detection(prompt, api_key='4ab77f25578d450f0902fb42c66d5e11')
+#     if lang == 'en':
+#         prompt = error_correct_pyspeller(prompt)
+#     en_translated = GoogleTranslator(source='auto', target='en').translate(prompt)
+#     messages.append({"role": "user", "content": en_translated})
+#     ai_msg = chat_model.invoke(messages, skip_prompt = True)
+#     response_of_llm = postprocerssing(ai_msg.content)
+#     messages.append({"role": "assistant", "content": response_of_llm})
+#     response_of_llm = GoogleTranslator(source='auto', target=lang).translate(response_of_llm)
+#     print(f"out is: {response_of_llm}")
+#     return response_of_llm
+
+# # def greet(prompt, m_type):
+# #     return "hi"
+
+# with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.orange, secondary_hue=gr.themes.colors.pink)) as demo:
+#     stored_message = gr.State([])
+#     with gr.Row():
+#         with gr.Column(scale=2):
+#             text1 = gr.Textbox(lines=7, label="Prompt", scale=2)
+#             with gr.Row():
+#                 btn1 = gr.Button("Submit", scale=1)
+#                 btn2 = gr.Button("Clear",  scale=1)
+#                 btn3 = gr.Button("Clean Memory",  scale=2)
+#         with gr.Column(scale=2):
+#             out_text = gr.Text(lines=15, label="Output", scale=2)
+#     btn1.click(fn=llm_run, inputs=[text1, stored_message], outputs=out_text)
+#     btn2.click(lambda: [None, None], outputs=[text1, out_text])
+#     btn3.click(fn=clear_memory, inputs=[stored_message], outputs=[out_text])
+    
+# # demo = gr.Interface(fn=llm_run, inputs=["text"], outputs="text")
+# demo.launch(debug=True, share=True)
+
+import gradio as gr
+
+def greet(inp, messages):
+    messages.append(inp)
+    print(messages)
+    return "Hello " + inp + "!"
+    
 def clear_memory(messages):
     messages.clear()
     return "Memory cleaned."
     
-def llm_run(prompt, messages):
-    print(f"question is {prompt}")
-    lang = single_detection(prompt, api_key='4ab77f25578d450f0902fb42c66d5e11')
-    if lang == 'en':
-        prompt = error_correct_pyspeller(prompt)
-    en_translated = GoogleTranslator(source='auto', target='en').translate(prompt)
-    messages.append({"role": "user", "content": en_translated})
-    ai_msg = chat_model.invoke(messages, skip_prompt = True)
-    response_of_llm = postprocerssing(ai_msg.content)
-    messages.append({"role": "assistant", "content": response_of_llm})
-    response_of_llm = GoogleTranslator(source='auto', target=lang).translate(response_of_llm)
-    print(f"out is: {response_of_llm}")
-    return response_of_llm
-
-# def greet(prompt, m_type):
-#     return "hi"
-
 with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.orange, secondary_hue=gr.themes.colors.pink)) as demo:
     stored_message = gr.State([])
     with gr.Row():
@@ -86,11 +115,9 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.orange, seco
                 btn3 = gr.Button("Clean Memory",  scale=2)
         with gr.Column(scale=2):
             out_text = gr.Text(lines=15, label="Output", scale=2)
-    btn1.click(fn=llm_run, inputs=[text1, stored_message], outputs=out_text)
+    btn1.click(fn=greet, inputs=[text1, stored_message], outputs=out_text)
     btn2.click(lambda: [None, None], outputs=[text1, out_text])
     btn3.click(fn=clear_memory, inputs=[stored_message], outputs=[out_text])
-    
-# demo = gr.Interface(fn=llm_run, inputs=["text"], outputs="text")
-demo.launch(debug=True, share=True)
+demo.launch()