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app.py

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+import gradio as gr
+from transformers import pipeline
+import pdfplumber
+
+# Load the pre-trained question-answering model
+qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
+
+def answer_question(file, ques: str):
+    try:
+        # Read and extract text from the uploaded PDF file
+        with pdfplumber.open(file) as pdf:
+            text = ""
+            for page in pdf.pages:
+                text += page.extract_text()
+
+        # Ask a default question
+        question = ques
+
+        # Ask the question using the question-answering model
+        answer = qa_pipeline({"context": text, "question": question})
+
+        return answer["answer"]
+
+    except Exception as e:
+        return f"Error processing PDF: {str(e)}"
+
+iface = gr.Interface(
+    fn=answer_question,
+    inputs=gr.File(label="Upload PDF"),
+    outputs="text",
+    live=True,
+    title="PDF Documents Question-Answering",
+    description="Ask a question about the contents of the uploaded PDF file.",
+)
+
+iface.launch()

requirements.txt

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+transformers
+pdfplumber
+gradio