Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import pandas as pd
+
+# Load pretrained model and tokenizer
+model_name = "jrocha/tiny_llama"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Load data
+df = pd.read_csv('splitted_df_jo.csv')
+
+# Function to prepare context
+def prepare_context():
+    pubmed_information_column = df['section_text']
+    pubmed_information_cleaned = ""
+    for text in pubmed_information_column.tolist():
+        objective_index = text.find("Objective")
+        if objective_index != -1:
+            cleaned_text = text[:objective_index]
+            pubmed_information_cleaned += cleaned_text
+        else:
+            pubmed_information_cleaned += text
+    max_length = 1000  # Adjust as needed
+    return pubmed_information_cleaned[:max_length]
+
+# Function to generate answer
+def answer_question(question, history):
+    pubmed_information_cleaned = prepare_context()
+
+    # Prepare input sequence
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a friendly chatbot who responds to questions about cancer. Please be considerate.",
+        },
+        {"role": "user", "content": question},
+    ]
+    prompt_with_pubmed = f"{pubmed_information_cleaned}\n\n"  # Adjust formatting as needed
+    prompt_with_pubmed += tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+
+    # Generate response
+    input_ids = tokenizer.encode(prompt_with_pubmed, return_tensors='pt')
+    output = model.generate(input_ids, max_length=600, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+
+    # Decode and return generated text
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    position_assistant = generated_text.find("<|assistant|>") + len("<|assistant|>")
+    return generated_text[position_assistant:]
+
+def main():
+    """"
+    Initializes a Women Cancer ChatBot interface using Hugging Face models for question answering.
+    
+    This function loads a pretrained tokenizer and model from the Hugging Face model hub
+    and creates a Gradio interface for the ChatBot. Users can input questions related to
+    women's cancer topics, and the ChatBot will generate answers based on the provided context.
+    
+    Returns:
+    None
+    Example:
+    >>> main()
+    """
+    iface = gr.Interface(fn=answer_question,
+                         inputs=["text"],
+                         outputs=[gr.Textbox(label="Answer")],
+                         title="Women Cancer ChatBot",
+                         description="How can I help you?",
+                         examples=[
+                             ["What is breast cancer?"],
+                             ["What are treatments for cervical cancer?"]
+                         ])
+    
+    return iface.launch(debug = True, share=True)
+
+main()