Streamlit app using langchain and gpt2 model

app.py

@@ -0,0 +1,26 @@
+import streamlit as st
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from langchain.prompts import PromptTemplate
+
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2LMHeadModel.from_pretrained('gpt2')
+
+st.title("GPT 2 Chat Bot?")
+input_text = st.text_area("Enter text to classify:")
+
+
+if st.button("""Enter >>>>> """):
+    if input_text:
+        prompt_template = PromptTemplate(template="Answer the following question and classify it: {question}",
+                                         input_variables=["question"], output_variables=["answer", "classification"])
+        # Model loading
+        format_prompt = prompt_template.format(question=input_text)
+        encoded_input = tokenizer(format_prompt, return_tensors='pt')
+        # Run the model
+        output = model.generate(**encoded_input, max_length=100)  # Use generate method for text generation
+        # Decode the model output to text
+        decoded_output = tokenizer.decode(output[0])
+        response_text = decoded_output.split('\n\n')
+        st.write(response_text[1])
+
+

requirements.txt

@@ -0,0 +1,7 @@
+streamlit
+langchain==0.2.5
+langchain-community==0.2.5
+python-multipart==0.0.9
+transformers==4.41.2
+torch==2.3.1
+tensorflow==2.16.2