Update app.py

app.py

@@ -1,3 +1,74 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from langchain.prompts import PromptTemplate
+from langchain.chains.summarize import load_summarize_chain
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+from pathlib import Path
 
-gr.load("models/mistralai/Mistral-7B-Instruct-v0.3").launch()
+# Load the Mistral model from Hugging Face
+model_name = "mistralai/Mistral-7B-Instruct-v0.3"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Define the text splitter and summarize chain
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+
+# Define the summarization function
+def summarize(file, n_words):
+    # Read the content of the uploaded file
+    file_path = file.name
+    with open(file_path, 'r', encoding='utf-8') as f:
+        file_content = f.read()
+    
+    # Split the content into chunks
+    chunks = text_splitter.create_documents([file_content])
+    
+    # Summarize each chunk and concatenate the results
+    summaries = []
+    for chunk in chunks:
+        inputs = tokenizer(chunk.text, return_tensors="pt", max_length=512, truncation=True)
+        summary_ids = model.generate(inputs["input_ids"], max_length=n_words, num_beams=4, early_stopping=True)
+        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+        summaries.append(summary)
+    
+    return " ".join(summaries)
+
+# Define the download summary function
+def download_summary(output_text):
+    if output_text:
+        file_path = Path('summary.txt')
+        with open(file_path, 'w', encoding='utf-8') as f:
+            f.write(output_text)
+        return file_path
+    else:
+        return None
+
+def create_download_file(summary_text):
+    file_path = download_summary(summary_text)
+    return str(file_path) if file_path else None
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Document Summarizer")
+
+    with gr.Row():
+        with gr.Column():
+            n_words = gr.Slider(minimum=50, maximum=500, step=50, label="Number of words")
+            file = gr.File(label="Submit a file")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="Summary will be printed here", lines=20)
+
+    submit_button = gr.Button("Summarize")
+    submit_button.click(summarize, inputs=[file, n_words], outputs=output_text)
+
+    download_button = gr.Button("Download Summary")
+    download_button.click(
+        fn=create_download_file,
+        inputs=[output_text],
+        outputs=gr.File()
+    )
+
+# Run the Gradio app
+demo.launch(share=True)