Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.document_loaders import PyPDFLoader
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import HuggingFaceHub
+
+# Function to load and process the document (PDF)
+def load_document(file):
+    loader = PyPDFLoader(file.name)
+    documents = loader.load()
+    return documents
+
+# Function to embed the documents using sentence-transformers and store them in FAISS
+def embed_documents(documents):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    vector_store = FAISS.from_documents(documents, embeddings)
+    return vector_store
+
+# Function to handle the chatbot's conversation by querying the document embeddings
+def chat_with_document(query, vector_store):
+    retriever = vector_store.as_retriever()
+    llm = HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature":0.2})
+    chain = load_qa_chain(llm, chain_type="stuff")
+    results = retriever.get_relevant_documents(query)
+    answer = chain.run(input_documents=results, question=query)
+    return answer
+
+# Function to build the Gradio interface
+def chatbot_interface():
+    vector_store = None
+
+    # Function to handle file upload and document embedding
+    def upload_file(file):
+        nonlocal vector_store
+        documents = load_document(file)
+        vector_store = embed_documents(documents)
+        return "Document uploaded and processed. You can now ask questions."
+
+    # Function to handle user queries
+    def ask_question(query):
+        if vector_store:
+            return chat_with_document(query, vector_store)
+        return "Please upload a document first."
+
+    # Gradio interface components
+    upload = gr.File(label="Upload a PDF document")
+    question = gr.Textbox(label="Ask a question about the document")
+    answer = gr.Textbox(label="Answer", readonly=True)
+
+    # Linking the functions to Gradio interface
+    upload_button = gr.Interface(fn=upload_file, inputs=upload, outputs="text")
+    chat_box = gr.Interface(fn=ask_question, inputs=question, outputs=answer)
+
+    # Gradio app layout
+    with gr.Blocks() as demo:
+        gr.Markdown("# Document Chatbot")
+        with gr.Row():
+            upload_button.render()
+        with gr.Row():
+            question.render()
+            answer.render()
+    
+    # Launch the Gradio app
+    demo.launch()
+
+# Start the chatbot interface
+if __name__ == "__main__":
+    chatbot_interface()