Create app.py

app.py

@@ -0,0 +1,115 @@
+import gradio as gr
+from openai import OpenAI
+import os
+from fpdf import FPDF  # For PDF conversion
+from docx import Document  # For DOCX conversion
+
+css = '''
+.gradio-container{max-width: 1000px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
+}
+'''
+
+ACCESS_TOKEN = os.getenv("HF_TOKEN")
+
+client = OpenAI(
+    base_url="https://api-inference.huggingface.co/v1/",
+    api_key=ACCESS_TOKEN,
+)
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+    
+    for message in  client.chat.completions.create(
+        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+        messages=messages,
+    ):
+        token = message.choices[0].delta.content
+        
+        response += token
+        yield response
+
+def save_as_file(input_text, output_text, conversion_type):
+    if conversion_type == "PDF":
+        pdf = FPDF()
+        pdf.add_page()
+        pdf.set_font("Arial", size=12)
+        pdf.multi_cell(0, 10, f"User Query: {input_text}\n\nResponse: {output_text}")
+        file_name = "output.pdf"
+        pdf.output(file_name)
+    elif conversion_type == "DOCX":
+        doc = Document()
+        doc.add_heading('Conversation', 0)
+        doc.add_paragraph(f"User Query: {input_text}\n\nResponse: {output_text}")
+        file_name = "output.docx"
+        doc.save(file_name)
+    elif conversion_type == "TXT":
+        file_name = "output.txt"
+        with open(file_name, "w") as f:
+            f.write(f"User Query: {input_text}\n\nResponse: {output_text}")
+    else:
+        return None
+
+    return file_name
+
+def convert_and_download(history, conversion_type):
+    if not history:
+        return None
+    
+    input_text = "\n".join([f"User: {h[0]}" for h in history if h[0]])
+    output_text = "\n".join([f"Assistant: {h[1]}" for h in history if h[1]])
+
+    file_path = save_as_file(input_text, output_text, conversion_type)
+    return file_path
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-P",
+        ),
+        gr.Dropdown(choices=["PDF", "DOCX", "TXT"], label="Conversion Type"),
+        gr.Button("Convert and Download"),
+    ],
+    css=css,
+    theme="allenai/gradio-theme",
+)
+
+def on_convert_and_download(history, conversion_type):
+    file_path = convert_and_download(history, conversion_type)
+    return file_path
+
+demo.launch(on_event={"Convert and Download": on_convert_and_download})
+
+if __name__ == "__main__":
+    demo.launch()