Add local chat with pdf

chat_with_pdf/chat_pdf_llama3.py

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+# Import necessary libraries
+import os
+import tempfile
+import streamlit as st
+from embedchain import App
+
+# Define the embedchain_bot function
+def embedchain_bot(db_path):
+    return App.from_config(
+        config={
+            "llm": {"provider": "ollama", "config": {"model": "llama3:instruct", "max_tokens": 250, "temperature": 0.5, "stream": True, "base_url": 'http://localhost:11434'}},
+            "vectordb": {"provider": "chroma", "config": {"dir": db_path}},
+            "embedder": {"provider": "ollama", "config": {"model": "llama3:instruct", "base_url": 'http://localhost:11434'}},
+        }
+    )
+
+st.title("Chat with PDF")
+st.caption("This app allows you to chat with a PDF using Llama3 running locally wiht Ollama!")
+
+# Create a temporary directory to store the PDF file
+db_path = tempfile.mkdtemp()
+# Create an instance of the embedchain App
+app = embedchain_bot(db_path)
+
+# Upload a PDF file
+pdf_file = st.file_uploader("Upload a PDF file", type="pdf")
+
+# If a PDF file is uploaded, add it to the knowledge base
+if pdf_file:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as f:
+        f.write(pdf_file.getvalue())
+        app.add(f.name, data_type="pdf_file")
+    os.remove(f.name)
+    st.success(f"Added {pdf_file.name} to knowledge base!")
+
+# Ask a question about the PDF
+prompt = st.text_input("Ask a question about the PDF")
+# Display the answer
+if prompt:
+    answer = app.chat(prompt)
+    st.write(answer)