Create app.py

app.py

@@ -0,0 +1,150 @@
+import gradio as gr
+import os
+import time
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.document_loaders import PyPDFLoader
+
+def load_doc(list_file_path):
+    loaders = [PyPDFLoader(x) for x in list_file_path]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1024, chunk_overlap=64
+    )
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+def create_db(splits):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vectordb = FAISS.from_documents(splits, embeddings)
+    return vectordb
+
+def initialize_chatbot(vector_db):
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    retriever = vector_db.as_retriever()
+    llm = HuggingFaceEndpoint(
+        repo_id="mistralai/Mistral-7B-Instruct-v0.2",
+        huggingfacehub_api_token=os.environ.get("HUGGINGFACE_API_TOKEN"),
+        temperature=0.5,
+        max_new_tokens=512,
+        task="text-generation"  # Explicitly specify the task type
+    )
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=retriever,
+        memory=memory,
+        verbose=False
+    )
+    return qa_chain
+
+def process_and_initialize(files):
+    if not files:
+        return None, None, "Please upload a file first."
+    
+    try:
+        list_file_path = [file.name for file in files if file is not None]
+        doc_splits = load_doc(list_file_path)
+        db = create_db(doc_splits)
+        qa = initialize_chatbot(db)
+        return db, qa, "Database created! Ready for questions."
+    except Exception as e:
+        return None, None, f"Processing error: {str(e)}"
+
+def user_query_typing_effect(query, qa_chain, chatbot):
+    history = chatbot or []
+    try:
+        response = qa_chain.invoke({"question": query, "chat_history": []})
+        assistant_response = response["answer"]
+        history.append({"role": "user", "content": query})
+        history.append({"role": "assistant", "content": ""})
+        for i in range(len(assistant_response)):
+            history[-1]["content"] += assistant_response[i]
+            yield history, ""
+            time.sleep(0.03)
+    except Exception as e:
+        history.append({"role": "assistant", "content": f"Error: {str(e)}"})
+        yield history, ""
+
+def demo():
+    custom_css = """
+    body {
+        background-color: #FF8C00;
+        font-family: Arial, sans-serif;
+    }
+    .gradio-container {
+        border-radius: 15px;
+        box-shadow: 0px 4px 20px rgba(0, 0, 0, 0.3);
+        padding: 20px;
+    }
+    footer {
+        visibility: hidden;
+    }
+    .chatbot {
+        border: 2px solid #000;
+        border-radius: 10px;
+        background-color: #FFF5E1;
+    }
+    """
+    with gr.Blocks(css=custom_css) as app:
+        vector_db = gr.State()
+        qa_chain = gr.State()
+        gr.Markdown("### 🌟 **PDF & TXT E-Book** 🌟")
+        gr.Markdown("#### Upload your document and ask questions interactively!")
+        with gr.Row():
+            with gr.Column(scale=1):
+                txt_file = gr.Files(
+                    label="📁 Upload Documents",
+                    file_types=[".txt", ".pdf"],
+                    type="filepath"
+                )
+                analyze_btn = gr.Button("🚀 Process Documents")
+                status = gr.Textbox(
+                    label="📊 Status",
+                    placeholder="Status updates will appear here...",
+                    interactive=False
+                )
+            with gr.Column(scale=3):
+                chatbot = gr.Chatbot(
+                    label="🤖 Chat with your data",
+                    height=600,
+                    bubble_full_width=False,
+                    show_label=False,
+                    render_markdown=True,
+                    type="messages",
+                    elem_classes=["chatbot"]
+                )
+                query_input = gr.Textbox(
+                    label="Ask a question",
+                    placeholder="Ask about the document...",
+                    show_label=False,
+                    container=False
+                )
+                query_btn = gr.Button("Ask")
+        analyze_btn.click(
+            fn=process_and_initialize,
+            inputs=[txt_file],
+            outputs=[vector_db, qa_chain, status],
+            show_progress="minimal"
+        )
+        query_btn.click(
+            fn=user_query_typing_effect,
+            inputs=[query_input, qa_chain, chatbot],
+            outputs=[chatbot, query_input],
+            show_progress="minimal"
+        )
+        query_input.submit(
+            fn=user_query_typing_effect,
+            inputs=[query_input, qa_chain, chatbot],
+            outputs=[chatbot, query_input],
+            show_progress="minimal"
+        )
+    app.launch() 
+
+if __name__ == "__main__":
+    demo()