Add application file

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import re
+from gtts import gTTS
+import os
+import logging
+
+# Set up logging
+logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Function to set up the model and tokenizer
+def setup_model(model_name):
+    logging.info('Setting up model and tokenizer.')
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        device_map="auto",
+        trust_remote_code=False,
+        revision="main"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+    model.eval()
+    logging.info('Model and tokenizer setup completed.')
+    return model, tokenizer
+
+# Function to generate a response from the model
+def generate_response(model, tokenizer, prompt, max_new_tokens=140):
+    logging.info('Generating response for the prompt.')
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=max_new_tokens)
+    response = tokenizer.batch_decode(outputs)[0]
+  
+    # Extract only the response part (assuming everything after the last newline belongs to the response)
+    response_parts = response.split("\n")
+    logging.info('Response generated.')
+    return response_parts[-1]  # Return the last element (response)
+
+# Function to remove various tags using regular expressions
+def remove_tags(text):
+    logging.info('Removing tags from the text.')
+    # Combine multiple tag removal patterns for broader coverage
+    tag_regex = r"<[^>]*>"  # Standard HTML tags
+    custom_tag_regex = r"<.*?>|\[.*?\]|{\s*?\(.*?\)\s*}"  # Custom, non-standard tags (may need adjustments)
+    all_tags_regex = f"{tag_regex}|{custom_tag_regex}"  # Combine patterns
+    cleaned_text = re.sub(all_tags_regex, "", text)
+    logging.info('Tags removed.')
+    return cleaned_text
+
+# Function to generate the audio file
+def text_to_speech(text, filename="response.mp3"):
+    logging.info('Generating speech audio file.')
+    tts = gTTS(text)
+    tts.save(filename)
+    logging.info('Speech audio file saved.')
+    return filename
+
+# Main function for the Gradio app
+def main(comment):
+    logging.info('Main function triggered.')
+    instructions_string = (
+        "virtual marketer assistant, communicates in business, focused on services, "
+        "escalating to technical depth upon request. It reacts to feedback aptly and ends responses "
+        "with its signature Mr.jon will tailor the length of its responses to match the individual's comment, "
+        "providing concise acknowledgments to brief expressions of gratitude or feedback, thus keeping the interaction natural and supportive.\n"
+    )
+
+    model_name = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
+    try:
+        model, tokenizer = setup_model(model_name)
+
+        if comment:
+            prompt_template = lambda comment: f"[INST] {instructions_string} \n{comment} \n[/INST]"
+            prompt = prompt_template(comment)
+
+            response = generate_response(model, tokenizer, prompt)
+            # Apply tag removal before displaying the response
+            response_without_tags = remove_tags(response)
+            # Remove the "[/INST]" string at the end (assuming it's always present)
+            response_without_inst = response_without_tags.rstrip("[/INST]")
+
+            # Generate and return the response and the audio file
+            audio_file = text_to_speech(response_without_inst)
+            logging.info('Response and audio file generated.')
+            return response_without_inst, audio_file
+        else:
+            logging.warning('No comment entered.')
+            return "Please enter a comment to generate a response.", None
+    except Exception as e:
+        logging.error(f'Error occurred: {str(e)}')
+        return "An error occurred. Please try again later.", None
+
+iface = gr.Interface(
+    fn=main,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a comment..."),
+    outputs=["text", "file"],
+    title="Virtual Marketer Assistant",
+    description="Enter a comment and get a response from the virtual marketer assistant. Download the response as an MP3 file."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+transformers
+torch
+gtts