Deploying the App

.gitignore

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+.env

Dockerfile

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+# Dockerfile
+
+# Use the official Python image with the desired version
+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy the requirements file to the working directory
+COPY requirements.txt /app
+
+# Install the dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code to the working directory
+COPY app.py /app
+
+# Expose the port that Gradio will run on (default is 7860)
+EXPOSE 7860
+
+ENV GRADIO_SERVER_NAME="0.0.0.0"
+
+# Command to run your application
+CMD ["python", "app.py"]

app.py

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+import os
+
+import gradio as gr
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
+from llama_index.embeddings.mixedbreadai import MixedbreadAIEmbedding
+from llama_index.llms.groq import Groq
+from llama_parse import LlamaParse
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# API keys
+llama_cloud_key = os.environ.get("LLAMA_CLOUD_API_KEY")
+groq_key = os.environ.get("GROQ_API_KEY")
+mxbai_key = os.environ.get("MXBAI_API_KEY")
+print(f"LLAMA Cloud API Key: {llama_cloud_key}")
+print(f"GROQ API Key: {groq_key}")
+print(f"Mixedbread AI API Key: {mxbai_key}")
+
+if not (llama_cloud_key and groq_key and mxbai_key):
+    raise ValueError(
+        "API Keys not found! Ensure they are passed to the Docker container."
+    )
+
+# models name
+# llm_model_name = "llama-3.1-70b-versatile"
+embed_model_name = "mixedbread-ai/mxbai-embed-large-v1"
+
+# Initialize the parser
+parser = LlamaParse(api_key=llama_cloud_key, result_type="markdown")
+
+# Define file extractor with various common extensions
+file_extractor = {
+    ".pdf": parser,
+    ".docx": parser,
+    ".doc": parser,
+    ".txt": parser,
+    ".csv": parser,
+    ".xlsx": parser,
+    ".pptx": parser,
+    ".html": parser,
+    ".jpg": parser,
+    ".jpeg": parser,
+    ".png": parser,
+    ".webp": parser,
+    ".svg": parser,
+}
+
+# Initialize the embedding model
+embed_model = MixedbreadAIEmbedding(api_key=mxbai_key, model_name=embed_model_name)
+
+# Initialize the LLM
+llm = Groq(model="llama3-70b-8192", api_key=groq_key)
+
+
+# File processing function
+def load_files(file_path: str):
+    global vector_index
+    if not file_path:
+        return "No file path provided. Please upload a file."
+   
+    valid_extensions = ', '.join(file_extractor.keys())
+    if not any(file_path.endswith(ext) for ext in file_extractor):
+        return f"The parser can only parse the following file types: {valid_extensions}"
+
+    document = SimpleDirectoryReader(input_files=[file_path], file_extractor=file_extractor).load_data()
+    vector_index = VectorStoreIndex.from_documents(document, embed_model=embed_model)
+    print(f"Parsing completed for: {file_path}")
+    filename = os.path.basename(file_path)
+    return f"Ready to provide responses based on: {filename}"
+
+
+# Respond function
+def respond(message, history):
+    try:
+        # Use the preloaded LLM
+        query_engine = vector_index.as_query_engine(streaming=True, llm=llm)
+        streaming_response = query_engine.query(message)
+        partial_text = ""
+        for new_text in streaming_response.response_gen:
+            partial_text += new_text
+            # Yield an empty string to cleanup the message textbox and the updated conversation history
+            yield partial_text
+    except (AttributeError, NameError):
+        print("An error occurred while processing your request.")
+        yield "Please upload the file to begin chat."
+
+
+# Clear function
+def clear_state():
+    global vector_index
+    vector_index = None
+    return [None, None, None]
+
+
+# UI Setup
+with gr.Blocks(
+    theme=gr.themes.Default(
+        primary_hue="green",
+        secondary_hue="blue",
+        font=[gr.themes.GoogleFont("Poppins")],
+    ),
+    css="footer {visibility: hidden}",
+) as demo:
+    gr.Markdown("# DataCamp Doc Q&A 🤖📃")
+    with gr.Row():
+        with gr.Column(scale=1):
+            file_input = gr.File(
+                file_count="single", type="filepath", label="Upload Document"
+            )
+            with gr.Row():
+                btn = gr.Button("Submit", variant="primary")
+                clear = gr.Button("Clear")
+            output = gr.Textbox(label="Status")
+        with gr.Column(scale=3):
+            chatbot = gr.ChatInterface(
+                fn=respond,
+                chatbot=gr.Chatbot(height=300),
+                theme="soft",
+                show_progress="full",
+                textbox=gr.Textbox(
+                    placeholder="Ask questions about the uploaded document!",
+                    container=False,
+                ),
+            )
+
+    # Set up Gradio interactions
+    btn.click(fn=load_files, inputs=file_input, outputs=output)
+    clear.click(
+        fn=clear_state,  # Use the clear_state function
+        outputs=[file_input, output],
+    )
+
+# Launch the demo
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+llama-index-embeddings-mixedbreadai
+llama-index-llms-groq
+llama-index