launch!

app.py

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+import requests
+
+import random
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
+import torch
+import gradio as gr
+import pandas as pd
+from datetime import datetime
+
+# Load emotion model and tokenizer
+emotion_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")
+emotion_model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-emotion")
+
+# Load text generation model and tokenizer
+import os
+token=os.getenv('hftoken')
+text_tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it", token=token)
+text_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it", token=token)
+
+# Set device to cpu
+device = torch.device('cpu')
+emotion_model.to(device)
+text_model.to(device)
+
+
+# Function to predict emotion
+def get_emotion(text):
+    input_ids = emotion_tokenizer.encode(text + '</s>', return_tensors='pt').to(device)
+    output = emotion_model.generate(input_ids=input_ids, max_length=2)
+    dec = [emotion_tokenizer.decode(ids, skip_special_tokens=True) for ids in output]
+    label = dec[0].strip()
+    return label
+
+def generate_quote(original_text, emotion):
+    # Generate one inspirational quote based on emotion and original text
+    input_text = f"Text: {original_text}\nEmotion: {emotion}\nInspirational Quote:"
+    input_ids = text_tokenizer(input_text, return_tensors="pt").to(device)
+    outputs = text_model.generate(**input_ids, max_new_tokens=70, do_sample=True, temperature=0.7)
+    generated_text = text_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    if "Inspirational Quote:" in generated_text:
+        quote = generated_text.split("Inspirational Quote:")[1].strip().split("\n")[0]
+    else:
+        quote = generated_text.strip()
+
+    return quote
+
+import os
+import pandas as pd
+
+# Ensure file exists and get absolute path
+csv_file = os.path.join(os.getcwd(), 'diary_entries.csv')
+if not os.path.exists(csv_file):
+    df = pd.DataFrame(columns=["Date", "Diary Text", "Emotion", "Quote"])
+    df.to_csv(csv_file, index=False)
+else:
+    df = pd.read_csv(csv_file)
+
+# Function to handle emotion detection, quote generation, and image display
+def journal_interface(Diary):
+    try:
+        # Step 1: Detect Emotion
+        emotion = get_emotion(Diary)
+
+        # Step 2: Generate Inspirational Quote
+        quote = generate_quote(Diary, emotion)
+
+        # Step 3: Save to CSV
+        date_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        new_entry = pd.DataFrame([[date_time, Diary, emotion, quote]], columns=["Date", "Diary Text", "Emotion", "Quote"])
+        global df
+        df = pd.concat([df, new_entry], ignore_index=True)
+        df.to_csv(csv_file, index=False)
+
+        return emotion, quote
+    except Exception as e:
+        print(f"Error encountered: {str(e)}")
+        return f"Error: {str(e)}", ""
+
+# Update the Gradio interface
+interface = gr.Interface(
+    fn=journal_interface,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your thoughts here..."),
+    outputs=[
+        gr.Textbox(label="Detected Emotion"),
+        gr.Textbox(label="Generated Quote")
+    ],
+    title="AI-Powered Personal Journal",
+    description="Enter your thoughts, and the AI will detect the emotion and generate an inspirational quote based on it.",
+    theme=gr.themes.Soft()
+)
+
+# Launch the Gradio app
+interface.launch(share=True)

requirements.txt

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+requests
+transformers
+torch
+gradio
+pandas
+tiktoken
+sentencepiece