Update app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
+from datasets import Dataset
+from groq import Groq
+import os
+
+# Initialize Groq client with your API key
+client = Groq(api_key="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")
+
+# Paths to your books
+book_paths = {
+    "DSM": "/content/Diagnostic and statistical manual of mental disorders _ DSM-5 ( PDFDrive.com ).pdf",
+    "Personality": "/content/b6c3v8_Theories_of_Personality_10.pdf",
+    "SearchForMeaning": "/content/Mans-Search-For-Meaning.pdf"
+}
+
+# Function to load and preprocess the data from books
+def load_data(paths):
+    data = []
+    for title, path in paths.items():
+        with open(path, "r", encoding="utf-8", errors='ignore') as file:
+            text = file.read()
+            paragraphs = text.split("\n\n")  # Split by paragraphs (adjust as needed)
+            for paragraph in paragraphs:
+                if paragraph.strip():  # Skip empty paragraphs
+                    data.append({"text": paragraph.strip()})
+    return Dataset.from_list(data)
+
+# Load and preprocess dataset for fine-tuning
+dataset = load_data(book_paths)
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
+from datasets import Dataset
+from groq import Groq
+import os
+
+# Initialize Groq client with your API key
+client = Groq(api_key="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")
+
+# Paths to your books
+book_paths = {
+    "DSM": "/app/Diagnostic and statistical manual of mental disorders _ DSM-5 ( PDFDrive.com ).pdf",
+    "Personality": "/app/b6c3v8_Theories_of_Personality_10.pdf",
+    "SearchForMeaning": "/app/Mans-Search-For-Meaning.pdf"
+}
+
+# Function to load and preprocess the data from books
+def load_data(paths):
+    data = []
+    for title, path in paths.items():
+        with open(path, "r", encoding="utf-8", errors='ignore') as file:
+            text = file.read()
+            paragraphs = text.split("\n\n")  # Split by paragraphs (adjust as needed)
+            for paragraph in paragraphs:
+                if paragraph.strip():  # Skip empty paragraphs
+                    data.append({"text": paragraph.strip()})
+    return Dataset.from_list(data)
+
+# Load and preprocess dataset for fine-tuning
+dataset = load_data(book_paths)
+
+# Load pretrained model and tokenizer from Hugging Face
+model_name = "gpt2"  # Replace with a larger model if needed and feasible
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Set the pad_token to be the same as eos_token (fix for missing padding token)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Tokenize data and create labels (shifted input for causal language modeling)
+def tokenize_function(examples):
+    # Tokenize the input text
+    encodings = tokenizer(examples["text"], truncation=True, padding=True, max_length=512)
+
+    # Create labels by shifting the input ids by one position (for causal LM)
+    labels = encodings["input_ids"].copy()
+    labels = [l if l != tokenizer.pad_token_id else -100 for l in labels]
+
+    # Return the encodings with labels
+    encodings["labels"] = labels
+    return encodings
+
+tokenized_dataset = dataset.map(tokenize_function, batched=True)
+
+# Split dataset into train and eval (explicit split for better validation)
+train_test_split = tokenized_dataset.train_test_split(test_size=0.1)
+train_dataset = train_test_split["train"]
+eval_dataset = train_test_split["test"]
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./results",          # Output directory for model and logs
+    eval_strategy="epoch",           # Use eval_strategy instead of evaluation_strategy
+    learning_rate=2e-5,              # Learning rate
+    per_device_train_batch_size=8,   # Batch size for training
+    per_device_eval_batch_size=8,    # Batch size for evaluation
+    num_train_epochs=3,              # Number of training epochs
+    weight_decay=0.01,               # Weight decay for regularization
+)
+
+# Initialize the Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,  # Pass eval dataset for evaluation
+    tokenizer=tokenizer,        # Provide tokenizer for model inference
+)
+
+# Fine-tune the model
+trainer.train()
+
+# Save the model after fine-tuning
+model.save_pretrained("./fine_tuned_model")
+tokenizer.save_pretrained("./fine_tuned_model")
+
+# Step 4: Define response function with emergency keyword check
+def get_response(user_input):
+    # Check for emergency/distress keywords
+    distress_keywords = ["hopeless", "emergency", "help", "crisis", "urgent"]
+    is_distress = any(word in user_input.lower() for word in distress_keywords)
+
+    # Use Groq API for generating a response
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": user_input}],
+        model="llama3-8b-8192",  # Or replace with another model
+    )
+    response = chat_completion.choices[0].message.content
+
+    # Append emergency message if distress keywords are detected
+    if is_distress:
+        response += "\n\nThis seems serious. Please consider reaching out to an emergency contact immediately. In case of an emergency, call [emergency number]."
+
+    return response
+
+# Step 5: Set up Gradio Interface
+import gradio as gr
+
+def chatbot_interface(input_text):
+    return get_response(input_text)
+
+# Launch the Gradio app
+gr.Interface(fn=chatbot_interface, inputs="text", outputs="text", title="Virtual Psychiatrist Chatbot").launch()