code updated

app.py

@@ -1,7 +1,47 @@
 import gradio as gr
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationChain  
+from transformers import pipeline
 
-def greet(name):
-    return "Hello " + name + "!!"
+model_name="nateraw/bert-base-uncased-emotion"
+model = pipeline('text-classification', model_name, truncation=True)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+from transformers import AutoTokenizer, AutoModelWithLMHead
+model_name = "mrm8488/t5-base-finetuned-emotion"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model_t5 = AutoModelWithLMHead.from_pretrained(model_name)
+
+def get_emotion(text):
+  input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
+  output = model_t5.generate(input_ids=input_ids, return_dict_in_generate=True, output_scores=True)
+  transition_scores = model_t5.compute_transition_scores(output.sequences, [s.softmax(dim=1) for s in output.scores], normalize_logits=False)
+  dec = [tokenizer.decode(ids) for ids in output.sequences]
+  score = transition_scores.min().item()
+  return f"{dec[0].replace('<pad>','').replace('</s>','').strip()} [{score}]"
+
+chat = ChatOpenAI()
+conversation = ConversationChain(llm=chat)  
+#Write a text example of someone angry
+with gr.Blocks() as demo:
+    label_text = gr.Textbox(label="Sentiment Text", text="")
+    chatbot = gr.Chatbot(scale=2)
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot])
+            
+
+    def respond(message, chat_history):
+        bot_message = conversation.run(message)
+        chat_history.append((message, bot_message))
+        
+        l = model(bot_message)[0]
+        label_value = f"{l['label']} [{l['score']}]"
+        label_value_t5 = get_emotion(bot_message)
+        
+        return "", chat_history, f"Model1: {label_value_t5} - Model2: {label_value}"
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot, label_text])
+
+demo.launch()
+
+
+ 

requirements.txt

@@ -0,0 +1,2 @@
+langchain
+openai