Update app.py

app.py

@@ -1,109 +1,81 @@
-# 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
-
-
-# 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 + "!"
-    
+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
+
 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"
+print("donnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn")
+
 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():
@@ -115,9 +87,38 @@ 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=greet, inputs=[text1, stored_message], outputs=out_text)
+    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.launch()
+    
+# 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."
+    
+# 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=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.launch()