Create app.py

app.py

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+# Importing Necessary Libraries
+import gradio as gr
+from llama_index import download_loader, ServiceContext, VectorStoreIndex
+from llama_index.embeddings import HuggingFaceEmbedding
+from llama_index import Prompt
+import torch
+device = torch.device("cpu")
+
+# Loading the Zephyr Model using Llama CPP
+from llama_index.llms import LlamaCPP
+llm = LlamaCPP(
+    model_url='https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/resolve/main/zephyr-7b-beta.Q5_K_M.gguf?download=true',
+    model_path=None,
+    temperature=0.5,
+    max_new_tokens=2000,
+    context_window=3900,
+    # set to at least 1 to use GPU
+    model_kwargs={"n_gpu_layers": 0},
+    verbose=True
+)
+
+# Loading Embedding Model
+embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")
+
+# Defining custom Prompt
+TEMPLATE_STR = (
+    '''You are an helpful and responsible AI assistant who is excited to help user and answer the question politely but will never harm humans or engage in the activity that causes harm to anyone. Use the given context below if useful.
+{context}
+<|user|>\n
+{query_str}\n
+<|assistant|>\n'''
+)
+QA_TEMPLATE = Prompt(TEMPLATE_STR)
+
+# User Interface functions
+def build_the_bot(file):
+    global service_context, index
+    if file is not None and file.name.endswith(".xlsx"):
+        # Loading Data
+        PandasExcelReader = download_loader("PandasExcelReader")
+        loader = PandasExcelReader(pandas_config={"header": 0})
+        documents = loader.load_data(file=file)
+
+        service_context = ServiceContext.from_defaults(
+            chunk_size=150,chunk_overlap=10,
+            llm=llm,embed_model=embed_model,
+            )
+
+        index = VectorStoreIndex.from_documents(documents, service_context=service_context,text_qa_template=QA_TEMPLATE)
+
+        return (gr.update(visible=True),gr.update(visible=True),gr.update(visible=True),gr.update(visible=True,)) #(4 gr.update because the outputs are 4 of upload.change)
+    else:
+        # Display a message if no file is uploaded 
+        return (gr.Textbox(placeholder="Please upload an excel file, refresh the page to restart the app"),gr.update(visible=True),gr.update(visible=False),gr.update(visible=True))
+
+def chat(user_input,history):
+  if user_input=="":
+        return "Please write your query so that I can assist you even better.",history
+  else:
+    global service_context, index
+    query_engine = index.as_query_engine(streaming=False)
+    bot_response = query_engine.query(user_input)
+    bot_response = str(bot_response)
+    history.append((user_input, bot_response))
+    return "", history
+
+def clear_everything():
+    return (None, None, None)
+
+# Adding themes in UI Interface
+custom_theme = gr.themes.Monochrome()
+
+# UI Design and Logic
+with gr.Blocks(theme=custom_theme, title="Marketing Email Generator") as demo:
+    gr.HTML("<h1 style='text-align: center;'>Marketing Email Generator</h1>")
+    gr.Markdown("Drop you Excel file here 👇 and ask your query about it!")
+    with gr.Row():
+        with gr.Column(scale=3):
+            upload = gr.File(label="Upload Your Excel File only", type="filepath")
+            with gr.Row():
+                clear_button = gr.Button("Clear", variant="secondary")
+
+        with gr.Column(scale=6):
+            chatbot = gr.Chatbot()
+            with gr.Row():
+                with gr.Column(scale=8):
+                    question = gr.Textbox(
+                        show_label=False,
+                        placeholder="Type your query here after uploading the excel file...",
+                    )
+                with gr.Column(scale=1, min_width=60):
+                    submit_button = gr.Button("Ask me 🤖", variant="primary")
+
+    upload.change(fn=build_the_bot,
+                  inputs=[upload],
+                  outputs=[question,clear_button,submit_button,chatbot],
+                  api_name="upload")
+
+    question.submit(chat, [question, chatbot], [question, chatbot])
+    submit_button.click(chat, [question, chatbot], [question, chatbot])
+
+    clear_button.click(fn=clear_everything,inputs=[],
+                       outputs=[upload, question, chatbot],
+                       api_name="clear")
+
+if __name__ == "__main__":
+    demo.launch(share=True, debug=True)