Update app.py

app.py

@@ -45,53 +45,51 @@ def classifier(userin, SearchType):
     return broad_scope_predictions[1], searchlink
     
 
-# def generateresponse(history, task):
-#     """
-#     Model definition here:
-#     """
-#     '''
-#     global model
-#     global tokenizer
-
-#     PROMPT = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-#     ### Instruction:
-#     {user}
-#     ### Response:"""
-
-#     inputs = tokenizer(
-#         PROMPT,
-#         return_tensors="pt",
-#     )
-#     input_ids = inputs["input_ids"].cuda()
-
-#     generation_config = GenerationConfig(
-#         temperature=0.6,
-#         top_p=0.95,
-#         repetition_penalty=1.15,
-#     )
-#     print("Generating...")
-#     generation_output = model.generate(
-#         input_ids=input_ids,
-#         generation_config=generation_config,
-#         return_dict_in_generate=True,
-#         output_scores=True,
-#         max_new_tokens=256,
-#     )
-#     output = []
-#     for s in generation_output.sequences:
-#         outputs.append(tokenizer.decode(s))
-#         print(tokenizer.decode(s))
+def generateresponse(history, task):
+    """
+    Model definition here:
+    """
+
+    global model
+    global tokenizer
+
+    user = history[-1][0]
+
+    PROMPT = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+    ### Instruction:
+    {user}
+    ### Response:"""
+
+    inputs = tokenizer(
+        PROMPT,
+        return_tensors="pt",
+    ) 
+    input_ids = inputs["input_ids"].cuda()
+
+    generation_config = GenerationConfig(
+        temperature=0.6,
+        top_p=0.95,
+        repetition_penalty=1.15,
+    ) 
+    print("Generating...")
+    generation_output = model.generate(
+        input_ids=input_ids,
+        generation_config=generation_config,
+        return_dict_in_generate=True,
+        output_scores=True,
+        max_new_tokens=256,
+    ) 
+    output = []
+    for s in generation_output.sequences:
+        outputs.append(tokenizer.decode(s))
+        print(tokenizer.decode(s))
     
-#     output = (outputs[0].split('### Response:'))[1]
-
-#     '''
-
-    # user = history[-1][0]
+    output = (outputs[0].split('### Response:'))[1]
     
-    # response = f"You asked: {user}"
-    # history[-1][1] = response
-    # print(history)
-    # return history
+    response = f"Response: {output}"
+    history[-1][1] = response
+    print(history)
+    return history
 
 
 theme = gr.themes.Base(
@@ -207,10 +205,9 @@ with gr.Blocks(title='Claimed', theme=theme) as demo:
             ).style(container=False)
         with gr.Column(scale=0.15, min_width=0):
             btn = gr.Button("Submit")
-
-    # txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
-    #     generateresponse, chatbot, chatbot
-    # )
+        
+        txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
+            generateresponse, chatbot, chatbot)
 
     gr.Markdown("""
     # HAVE AN IDEA? GET IT CLAIMED