Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import PyPDF2
+import torch
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    try:
+        pdf_reader = PyPDF2.PdfReader(pdf_file)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    except Exception as e:
+        return f"Error parsing PDF: {str(e)}"
+    return text
+
+# Load Summarization Model
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=0 if torch.cuda.is_available() else -1)
+
+# Summarization Function
+def summarize_text(text):
+    try:
+        summaries = summarizer(text[:1024], max_length=150, min_length=40, do_sample=False)
+        return summaries[0]['summary_text']
+    except Exception as e:
+        return f"Error during summarization: {str(e)}"
+
+# Load Question-Answering Model
+qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2", device=0 if torch.cuda.is_available() else -1)
+
+# Key Findings Extraction Function
+def extract_key_findings(text, query="What are the main findings?"):
+    try:
+        result = qa_pipeline({'question': query, 'context': text[:1024]})
+        return result['answer']
+    except Exception as e:
+        return f"Error extracting key findings: {str(e)}"
+
+# Main Function to Process PDF
+def process_paper(pdf_file):
+    extracted_text = extract_text_from_pdf(pdf_file.name)
+    if "Error" in extracted_text:
+        return extracted_text, None, None
+    
+    summary = summarize_text(extracted_text)
+    findings = extract_key_findings(extracted_text)
+    
+    return extracted_text, summary, findings
+
+# Gradio Interface
+with gr.Blocks() as app:
+    gr.Markdown("## AI-Assisted Literature Review Tool")
+    
+    with gr.Row():
+        pdf_input = gr.File(label="Upload Research Paper (PDF)")
+        process_button = gr.Button("Process")
+
+    with gr.Row():
+        extracted_text_output = gr.Textbox(label="Extracted Text", lines=10, interactive=False)
+        summary_output = gr.Textbox(label="Summary", lines=5, interactive=False)
+        findings_output = gr.Textbox(label="Key Findings", lines=5, interactive=False)
+    
+    process_button.click(process_paper, inputs=pdf_input, outputs=[extracted_text_output, summary_output, findings_output])
+
+app.launch()