Update app.py

app.py

@@ -1,78 +1,128 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+# import gradio as gr
+# from transformers import AutoTokenizer, AutoModelForCausalLM
+# import torch
+
+# model = AutoModelForCausalLM.from_pretrained(
+#     "Cogwisechat/falcon-7b-finance",
+#     torch_dtype=torch.bfloat16,
+#     trust_remote_code=True,
+#     device_map="auto",
+#     low_cpu_mem_usage=True,
+# )
+# tokenizer = AutoTokenizer.from_pretrained("Cogwisechat/falcon-7b-finance")
+
+
+# def generate_text(input_text):
+#     global output_text
+
+#     input_ids = tokenizer.encode(input_text, return_tensors="pt")
+#     attention_mask = torch.ones(input_ids.shape)
+
+#     output = model.generate(
+#         input_ids,
+#         attention_mask=attention_mask,
+#         max_length=200,
+#         do_sample=True,
+#         top_k=10,
+#         num_return_sequences=1,
+#         eos_token_id=tokenizer.eos_token_id,
+#     )
+    
 
-model = AutoModelForCausalLM.from_pretrained(
-    "Cogwisechat/falcon-7b-finance",
-    torch_dtype=torch.bfloat16,
-    trust_remote_code=True,
-    device_map="auto",
-    low_cpu_mem_usage=True,
-)
-tokenizer = AutoTokenizer.from_pretrained("Cogwisechat/falcon-7b-finance")
+#     output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+#     print(output_text)
 
+#     # Remove Prompt Echo from Generated Text
+    
+#     cleaned_output_text = output_text.replace(input_text, "")
+#     return  cleaned_output_text
 
-def generate_text(input_text):
-    global output_text
+# block = gr.Blocks()
 
-    input_ids = tokenizer.encode(input_text, return_tensors="pt")
-    attention_mask = torch.ones(input_ids.shape)
 
-    output = model.generate(
-        input_ids,
-        attention_mask=attention_mask,
-        max_length=200,
-        do_sample=True,
-        top_k=10,
-        num_return_sequences=1,
-        eos_token_id=tokenizer.eos_token_id,
-    )
-    
+# with block:
+#     gr.Markdown("""<h1><center>CogwiseAI falcon7b</center></h1>
+#     """)
+#     # chatbot = gr.Chatbot()
+#     message = gr.Textbox(placeholder='Enter Your Question Here')
+#     state = gr.State()
+#     submit = gr.Button("SEND")
+#     submit.click(generate_text, inputs=[message, state], outputs=[output_text, state])
 
-    output_text = tokenizer.decode(output[0], skip_special_tokens=True)
-    print(output_text)
+# block.launch(debug = True)
 
-    # Remove Prompt Echo from Generated Text
-    
-    cleaned_output_text = output_text.replace(input_text, "")
-    return  cleaned_output_text
 
-block = gr.Blocks()
 
 
-with block:
-    gr.Markdown("""<h1><center>CogwiseAI falcon7b</center></h1>
-    """)
-    # chatbot = gr.Chatbot()
-    message = gr.Textbox(placeholder='Enter Your Question Here')
-    state = gr.State()
-    submit = gr.Button("SEND")
-    submit.click(generate_text, inputs=[message, state], outputs=[output_text, state])
 
-block.launch(debug = True)
 
 
 
 
 
+    
 
+# # logo = (
+# #             "<div >"
+# #             "<img  src='ai-icon.png'alt='image One'>"
+# #             + "</div>"
+# #     )
+# # text_generation_interface = gr.Interface(
+# #     fn=generate_text,
+# #     inputs=[
+# #         gr.inputs.Textbox(label="Input Text"),
+# #     ],
+# #     outputs=gr.inputs.Textbox(label="Generated Text"),
+# #     title="Falcon-7B Instruct",
+# #     image=logo
+# # ).launch()
 
 
 
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+import torch
 
-    
 
-# logo = (
-#             "<div >"
-#             "<img  src='ai-icon.png'alt='image One'>"
-#             + "</div>"
-#     )
-# text_generation_interface = gr.Interface(
-#     fn=generate_text,
-#     inputs=[
-#         gr.inputs.Textbox(label="Input Text"),
-#     ],
-#     outputs=gr.inputs.Textbox(label="Generated Text"),
-#     title="Falcon-7B Instruct",
-#     image=logo
-# ).launch()
+title = "🤖AI ChatBot"
+description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
+examples = [["How are you?"]]
+
+
+tokenizer = AutoTokenizer.from_pretrained("Cogwisechat/falcon-7b-finance")
+model = AutoModelForCausalLM.from_pretrained("Cogwisechat/falcon-7b-finance")
+
+
+def predict(input, history=[]):
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(
+        input + tokenizer.eos_token, return_tensors="pt"
+    )
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+
+    # generate a response
+    history = model.generate(
+        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
+    ).tolist()
+
+    # convert the tokens to text, and then split the responses into lines
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    # print('decoded_response-->>'+str(response))
+    response = [
+        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
+    ]  # convert to tuples of list
+    # print('response-->>'+str(response))
+    return response, history
+
+
+gr.Interface(
+    fn=predict,
+    title=title,
+    description=description,
+    examples=examples,
+    inputs=["text", "state"],
+    outputs=["chatbot", "state"],
+    theme="finlaymacklon/boxy_violet",
+).launch()