Update app.py

Added Extra Features and Cleaned Output

app.py

@@ -198,7 +198,7 @@ def generateresponse(history, temp, top_p, tokens):
         "text-generation",
         model=model, 
         tokenizer=tokenizer, 
-        max_length=512,
+        max_length=2048,
         temperature=temp,
         top_p=top_p,
         repetition_penalty=1.15
@@ -208,25 +208,24 @@ def generateresponse(history, temp, top_p, tokens):
     outputs = outputs[0]['generated_text']
     outputs = str(outputs).split('### Response')[1]
     
-    response = f"Response: {outputs}"
+    response = f"Response{outputs}"
     return response
 
-
 def run_model(userin, dropd):
 
     global model
     global tokenizer
 
-    if dropd in ["Apparatus Claim", "Method of Use Claim", "Method Claim"]:
+    if dropd in ["An apparatus", "A method of use", "A method", "A method of making", "A system"]:
         PROMPT = claim_selector(userin, dropd)
-    elif dropd in ["Generate a Description Paragraph", "Generate a Abstract", "What are the Benefits/Technical Effects"]:
+    elif dropd in ["Generate a Detailed Description Paragraph", "Generate a Abstract", "What are the Benefits/Technical Effects"]:
         PROMPT = desc_selector(userin, dropd)
 
     pipe = pipeline(
         "text-generation",
         model=model, 
         tokenizer=tokenizer, 
-        max_length=512,
+        max_length=2048,
         temperature=0.7,
         top_p=0.95,
         repetition_penalty=1.15
@@ -236,11 +235,12 @@ def run_model(userin, dropd):
 
     outputs = outputs[0]['generated_text']
     outputs = str(outputs).split('### Response')[1]
+    outputs = outputs.split('\n    \n    \n    \n*')[0]
     
-    response = f"Response: {outputs}"
+    response = f"Response{outputs}"
     return response
 
-def prosecute(application, priorart, dropd):
+def prosecute(application, priorart):
 
     global model
     global tokenizer
@@ -249,44 +249,100 @@ def prosecute(application, priorart, dropd):
         "text-generation",
         model=model, 
         tokenizer=tokenizer, 
-        max_length=512,
+        max_length=2048,
         temperature=0.7,
         top_p=0.95,
         repetition_penalty=1.15
     )
 
     PROMPT = f"""
-    Below is an instruction that describes a task. Write a response that appropriately completes the request.
-    ### Instruction:
-    Draft a patent novelty/inventive step argument using the {dropd} approach:
+    Draft an argument for the patentability in favour of the application using the European Patent Office Problem Solution. Start by summarising the difference between the application and the prior art:
 
     Application: {application}
 
     Prior Art: {priorart}
 
+    ### Response: The objective technical problem solved by the present invention"""
+
+    outputs = pipe(PROMPT)
+
+    outputs = outputs[0]['generated_text']
+    outputs = str(outputs).split('### Response')[1]
+    outputs = outputs.split('\n    \n    \n    \n*')[0]
+    
+    response = f"Response{outputs}"
+    return response
+
+def ideator(userin):
+
+    global model
+    global tokenizer
+
+    pipe = pipeline(
+        "text-generation",
+        model=model, 
+        tokenizer=tokenizer, 
+        max_length=2048,
+        temperature=0.7,
+        top_p=0.95,
+        repetition_penalty=1.15
+    )
+
+    PROMPT = f"""
+    How can I make {userin}
+
+    ### Response: You could implement the invention as follows:"""
+
+    outputs = pipe(PROMPT)
+
+    outputs = outputs[0]['generated_text']
+    outputs = str(outputs).split('### Response')[1]
+    outputs = outputs.split('\n    \n    \n    \n*')[0]
+
+    
+    response = f"Response{outputs}"
+    return response
+
+def Chat(userin):
+
+    global model
+    global tokenizer
+
+    pipe = pipeline(
+        "text-generation",
+        model=model, 
+        tokenizer=tokenizer, 
+        max_length=2048,
+        temperature=0.7,
+        top_p=0.95,
+        repetition_penalty=1.15
+    )
+
+    PROMPT = f"""Below is a query from a user. Respond appropriately to the query.
+    ### Query:
+    {userin}
     ### Response:"""
 
     outputs = pipe(PROMPT)
 
     outputs = outputs[0]['generated_text']
     outputs = str(outputs).split('### Response')[1]
+    outputs = outputs.split('\n    \n    \n    \n*')[0]
     
-    response = f"Response: {outputs}"
+    response = f"Response{outputs}"
     return response
        
 def claim_selector(userin, dropd):
     
-    PROMPT = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-    ### Instruction:
-    Draft a patent claim 1 for {dropd} comprising {userin}
-    ### Response:"""
+    PROMPT = f"""
+    Draft a patent claim 1 for {dropd} for the following invention: {userin}
+    ### Response:{dropd} comprising:"""
 
     return PROMPT
 
 def desc_selector(userin, dropd):
     
-    PROMPT = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-    ### Instruction:
+    PROMPT = f"""
     {dropd} for a patent application for the following invention: {userin}
     ### Response:"""
 
@@ -301,27 +357,38 @@ theme = gr.themes.Base(
 )
 
 with gr.Blocks(title='Claimed', theme=theme) as demo:
-
     gr.Markdown("""
-    # CLAIMED - A GENERATIVE TOOLKIT FOR PATENT ATTORNEYS
-    The patenting process can be incredibly time-consuming and expensive. We're on a mission to change that.
+    # CLAIMED - A GENERATIVE TOOLKIT FOR PATENT ATTORNEYS AND INVENTORS
+    The patenting process can be complex, time-consuming and expensive. We believe that AI will one day solve these problems.
     
     Welcome to our demo! We've trained Meta's Llama on over 200k entries, with a focus on tasks related to the intellectual property domain.
     Please note that this is for research purposes and shouldn't be used commercially. 
     None of the outputs of this model, taken in part or in its entirety, constitutes legal advice. If you are seeking protection for you intellectual property, consult a registered patent/trademark attorney.
-    
     """)
-    
+
+    with gr.Tab("Ideator"):
+        gr.Markdown(""" 
+        Use this tool to generate ideas!
+         """)
+        with gr.Row(scale=1, min_width=600):
+            with gr.Column():
+              userin = gr.Text(label="Input", lines=5)
+            with gr.Column():
+              text2 = gr.Textbox(label="Output", lines=5)
+        with gr.Row():
+            btn = gr.Button("Submit")
+            btn.click(fn=ideator, inputs=[userin], outputs=text2)
+
     with gr.Tab("Claim Drafter"):
         gr.Markdown(""" 
         Use this tool to expand your idea into the technical language of a patent claim. You can specify the type of claim you want using the dropdown menu.
         """)
-        Claimchoices = gr.Dropdown(["Apparatus Claim", "Method of Use Claim", "Method Claim", ], label='Choose Claim Type Here')        
+        Claimchoices = gr.Dropdown(["An apparatus", "A method of use", "A method", "A method of making", "A system"], label='Choose Claim Type Here')        
         
         with gr.Row(scale=1, min_width=600):
             text1 = gr.Textbox(label="Input",
-                              placeholder='Type in your idea here!')
-            text2 = gr.Textbox(label="Output")
+                              placeholder='Type in your idea here!', lines=5)
+            text2 = gr.Textbox(label="Output", lines=5)
         with gr.Row():
             btn = gr.Button("Submit")
             btn.click(fn=claim_selector, inputs=[text1, Claimchoices]).then(run_model, inputs=[text1, Claimchoices], outputs=text2)
@@ -330,45 +397,30 @@ with gr.Blocks(title='Claimed', theme=theme) as demo:
         gr.Markdown(""" 
         Use this tool to expand your patent claim into a description. You can also use this tool to generate abstracts and give you ideas about the benefit of an invention by changing the settings in the dropdown menu.
          """)
-        Descriptionchoices = gr.Dropdown(["Generate a Description Paragraph", "Generate a Abstract", "What are the Benefits/Technical Effects"], label='Choose Generation Type Here')
+        Descriptionchoices = gr.Dropdown(["Generate a Detailed Description Paragraph", "Generate a Abstract", "What are the Benefits/Technical Effects"], label='Choose Generation Type Here')
         with gr.Row(scale=1, min_width=600):
                     
             text1 = gr.Textbox(label="Input",
-                              placeholder='Type in your idea here!')
-            text2 = gr.Textbox(label="Output")
+                              placeholder='Type in your idea here!', lines=5)
+            text2 = gr.Textbox(label="Output", lines=5)
         with gr.Row():
             btn = gr.Button("Submit")
             btn.click(fn=desc_selector, inputs=[text1, Descriptionchoices]).then(run_model, inputs=[text1, Descriptionchoices], outputs=text2)
     
     with gr.Tab("Prosecution Beta"):
         gr.Markdown(""" 
-        Use this tool to generate ideas for how to overcome objections to novelty and inventive step. Outputs are in the EPO's problem-solution format. For now, this tool only works on relatively short inputs, so maybe try with some simple claims or short paragraphs.
+        Use this tool to generate ideas for how to overcome objections to novelty and inventive step. For now, this tool only works on relatively short inputs, so maybe try with some simple claims or short paragraphs.
          """)
-        dropd = gr.Dropdown(["Problem Solution", "Windsurfing/Pozzoli"], label='Choose Generation Type Here')
         with gr.Row(scale=1, min_width=600):
             with gr.Column():
-              application = gr.Text(label="Present Invention")
-              priorart = gr.Text(label="Prior Art Document")        
-            text2 = gr.Textbox(label="Output")
+              application = gr.Text(label="Present Invention", lines=5)
+              priorart = gr.Text(label="Prior Art Document", lines=5)        
+            with gr.Column():
+              text2 = gr.Textbox(label="Output", lines=5)
         with gr.Row():
             btn = gr.Button("Submit")
-            btn.click(fn=prosecute, inputs=[application, priorart, dropd], outputs=text2)
-
-
-    # with gr.Tab("Claimed Infill"):
-    #     gr.Markdown(""" 
-    #     Below is our 
-
-    #     Example input: A device to help the visually impaired using proprioception.
-
-    #     Output: 
-    #     """)
-    #     with gr.Row(scale=1, min_width=600):
-    #         text1 = gr.Textbox(label="Input",
-    #                           placeholder='Type in your idea here!')
-    #         text2 = gr.Textbox(label="Output")
-
-    
+            btn.click(fn=prosecute, inputs=[application, priorart], outputs=text2)
+     
     with gr.Tab("CPC Search Tool"):
         gr.Markdown("""
         Use this tool to classify your invention according to the Cooperative Patent Classification system. 
@@ -377,36 +429,48 @@ with gr.Blocks(title='Claimed', theme=theme) as demo:
 
         ClassifyChoices = gr.Dropdown(["Google Patent Search", "Espacenet Patent Search"], label='Choose Search Type Here')        
         with gr.Row(scale=1, min_width=600):
-            userin = gr.Textbox(label="Input",
-                              placeholder='Type in your Claim/Description/Abstract Here')
-            output = gr.Textbox(label="Output")
+            userin = gr.Textbox(label="Input", placeholder='Type in your Claim/Description/Abstract Here',lines=5)
+            output = gr.Textbox(label="Output", lines=5)
         with gr.Row():
             classify_btn = gr.Button("Classify")
             classify_btn.click(fn=classifier, inputs=[userin, ClassifyChoices] , outputs=output)
 
-    gr.Markdown(""" 
-    # THE CHATBOT
-    Do you want a bit more freedom over the outputs you generate? No worries, you can use a chatbot version of our model below. You can ask it anything. 
-    If you're concerned about a particular output, hit the flag button and we will use that information to improve the model.
-    """)
+    with gr.Tab("Chat"):
+        gr.Markdown(""" 
+        Do you want a bit more freedom over the outputs you generate? No problem! You can use a chatbot version of our model below. You can ask it anything. 
+        If you're concerned about a particular output, hit the flag button and we will use that information to improve the model.
+         """)
+        with gr.Row(scale=1, min_width=600):
+            with gr.Column():
+              userin = gr.Text(label="Question", lines=5)
+            with gr.Column():
+              text2 = gr.Textbox(label="Answer", lines=5)
+        with gr.Row():
+            btn = gr.Button("Submit")
+            btn.click(fn=Chat, inputs=[userin], outputs=text2)
+
+#    gr.Markdown(""" 
+#    # THE CHATBOT
+#    Do you want a bit more freedom over the outputs you generate? No problem! You can use a chatbot version of our model below. You can ask it anything. 
+#    If you're concerned about a particular output, hit the flag button and we will use that information to improve the model.
+#    """)
    
-    chatbot = gr.Chatbot([], elem_id="Claimed Assistant").style(height=500)
-    with gr.Row():
-        with gr.Column(scale=1):
-            txt = gr.Textbox(
-                show_label=False,
-                placeholder="Enter text and submit",
-            ).style(container=False)
-    
-    with gr.Row():
-        with gr.Accordion("Parameters"):
-            temp = gr.Slider(minimum=0, maximum=1, value=0.6, label="Temperature", step=0.1)
-            top_p = gr.Slider(minimum=0.5, maximum=1, value=0.95, label="Top P", step=0.1)
-            tokens = gr.Slider(minimum=5, maximum=512, value=256, label="Max Tokens", step=1)
-        
-    txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
-            generateresponse, [chatbot, temp, top_p, tokens], chatbot)
-        
+#    chatbot = gr.Chatbot([], elem_id="Claimed Assistant").style(height=500)
+#    with gr.Row():
+#        with gr.Column(scale=1):
+#            txt = gr.Textbox(
+#                show_label=False,
+#                placeholder="Enter text and submit",
+#            ).style(container=False)
+#    
+#    with gr.Row():
+#        with gr.Accordion("Parameters"):
+#            temp = gr.Slider(minimum=0, maximum=1, value=0.6, label="Temperature", step=0.1)
+#            top_p = gr.Slider(minimum=0.5, maximum=1, value=0.95, label="Top P", step=0.1)
+#            tokens = gr.Slider(minimum=5, maximum=1024, value=256, label="Max Tokens", step=1)
+#        
+#    txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
+#            generateresponse, [chatbot, temp, top_p, tokens], chatbot)
 
     gr.Markdown("""
     # HAVE AN IDEA? GET IT CLAIMED 
@@ -415,6 +479,6 @@ with gr.Blocks(title='Claimed', theme=theme) as demo:
     As far as data is concerned, you have nothing to worry about! We don't store any of your inputs to use for further training, we're not OpenAI.  
     
     """)
-demo.queue(concurrency_count=9)
+demo.queue(max_size=15)
 demo.launch(share=True)