Update app.py

app.py

@@ -6,58 +6,118 @@ For more information on `huggingface_hub` Inference API support, please check th
 """
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# -*- coding: utf-8 -*-
+"""juri_chatbot.ipynb
 
-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_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
+Automatically generated by Colab.
 
+Original file is located at
+    https://colab.research.google.com/drive/1o40EH1zkgiGTxJERwU7Tr2b2hb5DPdLX
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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 (nucleus sampling)",
-        ),
-    ],
-)
 
+from google.colab import drive
+drive.mount('/content/gdrive')
+
+if torch.cuda.is_available():
+    print("CUDA is available! 🚀")
+    num_gpus = torch.cuda.device_count()
+    print(f"Number of available GPUs: {num_gpus}")
+    devices = [torch.device(f"cuda:{i}") for i in range(num_gpus)]
+else:
+    print("CUDA is not available. Switching to CPU.")
+    devices = [torch.device("cpu")]
+
+print("Selected devices:", devices)
+
+pip install gradio
+
+pip install sentence_transformers
+
+import gradio as gr
+import pandas as pd
+import transformers
+import torch
+from sentence_transformers import SentenceTransformer, util
+
+# Load the SBERT model
+sbert_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# Function to initialize the Llama 3 8B model pipeline
+def initiate_pipeline():
+    model = "meta-llama/Meta-Llama-3-8B-Instruct"
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    return transformers.pipeline(
+        "text-generation",
+        model=model,
+        model_kwargs={"torch_dtype": torch.bfloat16},
+        device=device,
+    )
+
+# Initialize the model
+llama_model = initiate_pipeline()
+
+# Load the Q&A pairs from the CSV
+qa_data = pd.read_csv("/content/gdrive/MyDrive/Colab Notebooks/rag_juri_cv.csv")
+
+# Function to retrieve the top 5 relevant Q&A pairs using Sentence-BERT
+def retrieve_top_k(query, k=5):
+    # Combine the questions from the CSV into a list
+    questions = qa_data['QUESTION'].tolist()
+
+    # Encode the questions and the query using Sentence-BERT
+    question_embeddings = sbert_model.encode(questions, convert_to_tensor=True)
+    query_embedding = sbert_model.encode(query, convert_to_tensor=True)
+
+    # Compute cosine similarities between the query and all questions
+    cosine_scores = util.pytorch_cos_sim(query_embedding, question_embeddings).flatten()
+
+    # Get the indices of the top k most similar questions
+    top_k_indices = torch.topk(cosine_scores, k=k).indices.cpu()
+
+    # Retrieve the corresponding Q&A pairs
+    top_k_qa = qa_data.iloc[top_k_indices]
+
+    return top_k_qa
+
+def chatbot(query):
+    # Retrieve the top 5 relevant Q&A pairs
+    top_k_qa = retrieve_top_k(query)
+
+    # Generate the prefix, body, and suffix for the prompt
+    prefix = """
+    <|begin_of_text|><|start_header_id|>user<|end_header_id|>You are a chatbot specialized in answering questions about Juri Grosjean's CV.
+    Please only use the information provided in the context to answer the question.
+    Here is the question to answer:
+    """ + query + "\n\n"
+
+    context = "This is the context information to answer the question:\n"
+    for index, row in top_k_qa.iterrows():
+        context += f"Information {index}: {row['ANSWER']}\n\n"
+
+    suffix = "<|eot_id|><|start_header_id|>assistant<|end_header_id|>"
+
+    prompt = prefix + context + suffix
+
+    # Generate a response
+    outputs = llama_model(
+        prompt,
+        max_new_tokens=500,
+        do_sample=True,
+        temperature=0.6,
+        top_p=0.9,
+    )
+
+    # Extract and return the chatbot's answer
+    output = outputs[0]["generated_text"]
+    return output.split("assistant")[-1].strip()
+
+
+# Set up the Gradio interface
+demo = gr.Interface(
+        fn=chatbot,
+        inputs=gr.Textbox(lines=5, placeholder="Ask a question about Juri Grosjean's CV"),
+        outputs="text"
+    )
 
 if __name__ == "__main__":
     demo.launch()