Update app.py

app.py

@@ -1,3 +1,42 @@
 import gradio as gr
+from langchain import HuggingFacePipeline, PromptTemplate, LLMChain
+from transformers import AutoTokenizer
+import transformers
+import torch
 
-gr.load("models/manohar02/NN-Llama-2-7b-finetune").launch()
+# Define the Hugging Face model
+model = "models/manohar02/NN-Llama-2-7b-finetune"
+
+# Define the Hugging Face pipeline
+pipeline = transformers.pipeline(
+    "text-generation",  # task
+    model=model,
+    torch_dtype=torch.bfloat16,
+    max_length=20000,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=AutoTokenizer.from_pretrained(model).eos_token_id
+)
+
+llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': 0})
+
+# Define the template for summarization
+template = """
+Write a concise summary of the following text delimited by triple backquotes.
+'''{text}'''
+SUMMARY:
+"""
+
+prompt = PromptTemplate(template=template, input_variables=["text"])
+
+llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+# Function to generate summary
+def generate_summary(text):
+    summary = llm_chain.run(text)
+    return summary.split('SUMMARY:')[-1].strip()
+
+# Create a Gradio interface
+iface = gr.Interface(fn=generate_summary, inputs="text", outputs="text", title="LLaMA2 Summarizer")
+iface.launch()