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app.py

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+import PyPDF2
+import streamlit as st
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load pre-trained GPT-3 model and tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
+model = GPT2LMHeadModel.from_pretrained("meta-llama/Meta-Llama-3-8B")
+
+def extract_text_from_pdf(pdf_path):
+    text = ""
+    with open(pdf_path, "rb") as f:
+        reader = PyPDF2.PdfFileReader(f)
+        for page_num in range(reader.numPages):
+            text += reader.getPage(page_num).extractText()
+    return text
+
+def generate_response(user_input):
+    input_ids = tokenizer.encode(user_input, return_tensors="pt")
+    output = model.generate(input_ids, max_length=100, num_return_sequences=1, temperature=0.7)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+def main():
+    st.title("PDF Chatbot")
+
+    # File upload
+    uploaded_file = st.file_uploader("Upload PDF", type=["pdf"])
+
+    if uploaded_file is not None:
+        pdf_text = extract_text_from_pdf(uploaded_file)
+        st.text_area("PDF Content", pdf_text)
+
+    user_input = st.text_input("You:", "")
+    if st.button("Send"):
+        response = generate_response(user_input)
+        st.text_area("Chatbot:", response)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+PyPDF2==3.0.1
+streamlit==1.33.0
+transformers==4.40.1
+torch==2.3.0