Update app.py

app.py

@@ -1,48 +1,14 @@
-import gradio as gr
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_community.vectorstores import FAISS
-from langchain_community.llms import HuggingFaceLLM
-from langchain.chains import MapReduceChain
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.prompts import PromptTemplate
-import os
+from transformers import pipeline
 
-# Load model and embeddings
-embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", device="cpu")
-llm = HuggingFaceLLM(model_name="meta-llama/Meta-Llama-3.1-8B-Instruct")
+# Use the correct model name available on Hugging Face
+model_name = "meta-llama/Meta-Llama-3.1-8B"
 
-def process_pdf_and_summarize(file):
-    try:
-        # Load PDF document
-        loader = PyPDFLoader(file.name)
-        documents = loader.load()
+# Initialize the text-generation pipeline
+pipe = pipeline("text-generation", model=model_name)
 
-        # Summarize the document
-        map_template = """Summarize the following text:\n\n{text}\n\nSummary:"""
-        map_prompt = PromptTemplate.from_template(map_template)
-        reduce_template = """Combine these summaries into a final summary:\n\nSummaries: {doc_summaries}\n\nFinal Summary:"""
-        reduce_prompt = PromptTemplate.from_template(reduce_template)
+# Generate text based on a prompt
+prompt = "In a distant future, humanity has developed AI"
+output = pipe(prompt, max_length=50, num_return_sequences=1)
 
-        chain = MapReduceChain.from_chain_type(
-            llm=llm,
-            chain_type="map_reduce",
-            map_prompt=map_prompt,
-            reduce_prompt=reduce_prompt,
-            text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-        )
-        summary = chain.run(documents)
-        return summary
-    except Exception as e:
-        return f"Error processing PDF: {str(e)}"
-
-# Gradio interface
-interface = gr.Interface(
-    fn=process_pdf_and_summarize,
-    inputs=gr.inputs.File(label="Upload PDF"),
-    outputs="text",
-    title="PDF Summarizer",
-    description="Upload a PDF document to generate a summary."
-)
-
-interface.launch()
+# Print the generated text
+print(output)