Update app.py

app.py

@@ -1,63 +1,250 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+"""### **Set up the enviroment**"""
+
+import os
+from langchain_community.llms import Together
+os.environ["TOGETHER_API_KEY"] = "YOUR KEY HERE"
+
+"""### **part 2: Data Collection and Preprocessing**"""
+
+import fitz  # PyMuPDF
+
+def extract_and_split_pdf(pdf_path, split_key="ENDOFTUT"):
+    combined_list = []
+
+    # Open the PDF file
+    document = fitz.open(pdf_path)
+
+    # Extract text from each page
+    all_text = ""
+    for page_num in range(document.page_count):
+        page = document.load_page(page_num)
+        all_text += page.get_text()
+
+    # Split the text by the key
+    combined_list = all_text.split(split_key)
+
+    return combined_list
+
+# Example usage
+pdf_path = "Mech-chunks.pdf"
+combined_list = extract_and_split_pdf(pdf_path)
+
+#take json file and make each q&a in single cell in the list
+import json
+
+# Load the JSON file
+with open('output_data (1).json', 'r') as file:
+    data = json.load(file)
+
+
+for item in data:
+    question = item.get("question", "")
+    answer = item.get("answer", "")
+    combined_list.append(f"Q: {question}?\nA: {answer}\n")
+
+"""### **part 3: save embeddings in a vector store**"""
+
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.schema import Document
+
+db = FAISS.from_texts(combined_list,
+                          HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))
+
+"""### **part 4: create the retriever**"""
+
+retriever = db.as_retriever(
+    search_type="similarity",
+    search_kwargs={'k': 4}
+)
+
+# Manually test retrieval to verify correct chunk fetching
+query = "what are the tools needed to change Brake Discs on a Car?"
+retrieved_docs = db.similarity_search(query, k=5)
+
+# Print out the content of retrieved documents to verify
+for idx, doc in enumerate(retrieved_docs):
+    print(f"Document {idx + 1}:", doc.page_content)
+
+"""### **part 5: load the llama model**"""
+
+llm = Together(
+
+    model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
+    max_tokens=512,
+    temperature=0.2,
+    #top_p=0.7,
+    #top_k=50,
+    repetition_penalty=1.1,
+)
+
+"""### **part 7:Load the Llama Guard model**"""
+
+Llama_guard = Together(
+
+    model="meta-llama/Meta-Llama-Guard-3-8B",
+    max_tokens=10,
+    temperature=0.2,
+    #top_p=0.7,
+    #top_k=50,
+    repetition_penalty=1.1,
+)
 
+"""### **part 8: Moderating Chat Messages with a Template and Guard Model**"""
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+guard_temp_ans = """
+  <|system|>
+  Task: Check if there is unsafe content in conversations according our safety policy with the below categories.
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+  <BEGIN UNSAFE CONTENT CATEGORIES>
+  1: Violence and Hate.
+  Should not
+  - Help people plan or engage in violence.
+  - Encourage people to engage in violence.
+  - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.
+  - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.
+  - Provide information that can be used to promote or engage in violence, hate, or discrimination.
+  - Provide guidance or instructions on how to engage in violent or hateful behavior, except if related to 3D and Blender creation for artistic purposes.
 
-    messages.append({"role": "user", "content": message})
+  2: Sexual Content.
+  Should not
+  - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.
+  - Create or provide explicit depictions of sexual acts or nudity.
+  - Provide information that could be used to create sexually explicit content
 
-    response = ""
+  3: Criminal Planning.
+  Should not
+  - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.
+  - Encourage people to engage in criminal activities or condone criminal behavior.
+  - Provide detailed instructions or information that could be used to plan or execute criminal activities, except if related to 3D and Blender creation for artistic purposes.
 
-    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
+  4: Guns and Illegal Weapons.
+  Should not
+  - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.
+  - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.
+  - Provide information that could be used to create or use illegal weapons, except if related to 3D and Blender creation for artistic purposes.
 
-        response += token
-        yield response
+  5: Regulated or Controlled Substances.
+  Should not
+  - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.
+  - Assist or encourage people to create such substances when it is illegal to do so.
+  - Provide information that could be used to create or use regulated or controlled substances, except if related to 3D and Blender creation for artistic purposes.
 
+  6: Self-Harm.
+  Should not
+  - Encourage people to harm themselves.
+  - Romanticize or condone self-harm or suicide.
+  - Provide information regarding the methods of suicide or self-harm.
+  - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.
+  - Provide information that could be used to promote or engage in self-harm, except if related to 3D and Blender creation for artistic purposes.
+
+
+  <END UNSAFE CONTENT CATEGORIES>
+
+  <BEGIN CONVERSATION>
+  {answer}
+  <END CONVERSATION>
+
+  Provide your safety assessment for {answer} in the above conversation:
+  - First line must read 'safe' or 'unsafe'.
+  - If unsafe, do not include a second line.
+  """
+
+#Removed extra space before the line below to fix the indentation error
+prompt_ans_guard = ChatPromptTemplate.from_template(guard_temp_ans)
+
+guard_chain = (
+      prompt_ans_guard
+      | Llama_guard
+  )
+
+def moderate(chat):
+  input_ids = Llama_guard.apply_chat_template(chat, return_tensors="pt").to(device)
+  output = model.generate(input_ids=input_ids, max_new_tokens=100, pad_token_id=0)
+  prompt_len = input_ids.shape[-1]
+  return Llama_guard.decode(output[0][prompt_len:], skip_special_tokens=True)
+
+"""### **part 9: Define the prompt template and LLMChain**"""
+
+# Define the prompt template
+prompt_template = PromptTemplate(
+    input_variables=["context", "question", "history"],
+    template=("""
+You are a mechanic assistant and your name is MechBot, these human will ask you questions about Cars,
+use Use following piece of context and chat history  to answer the question.
+If you don't know the answer, just say you don't know.
+If the question is start with how to, answer with steps and mention the tools if you know it.
+
+Chat History: ({history})
+
+Context: ({context})
+
+Question: {question}
+
+        Answer:
+        """
+
+    )
+)
+
+llm_chain = prompt_template | llm | StrOutputParser()
+
+"""### **part 12: Question Answering Function with Content Moderation**
+
+This function, **answer_question**, answers user questions while ensuring content appropriateness through moderation checks.
+
+**Moderate User Question:** Checks if the question is safe. If not, it returns an apologetic message.
+
+**Generate AI Response:** Uses the RAG chain to generate an answer if the question is safe.
+
+**Update and Moderate Chat History:** Adds the question and answer to the chat history and checks if the AI response is appropriate. If not, it apologizes and indicates an attempt to generate a better answer.
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+
+def answer_question(question,gh):
+    global counter
+    global history
+    global reter
+    if "unsafe" in guard_chain.invoke({"answer":question}):
+            return "I'm sorry, but I can't respond to that question as it may contain inappropriate content."
+    reter = ""
+    retrieved_docs = db.similarity_search(question, k=2)  # Consider reducing 'k' if context is too large
+
+    for doc in retrieved_docs:
+        reter += doc.page_content + "\n"
+
+     #Truncate history if it's too long
+    if len(history) > 3000:  # Adjust this value as needed
+       history = history[-2000:]
+
+    formatted_prompt = prompt_template.format(context=reter, history=history, question=question)
+    print("Formatted Prompt:")
+    print(formatted_prompt)
+
+    answer = llm_chain.invoke({"context": reter,"history": history, "question": question})
+    history += "\n" + "user question: " + question + "\n" + "AI answer: " + answer
+    #print(reter)
+    counter += 1
+    return answer
+
+"""### **part 13: interface**
+
 """
-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)",
-        ),
-    ],
-)
 
+import gradio as gr
+history = ""
+counter = 1
+# Create the Chat interface
+iface = gr.ChatInterface(
+    answer_question,  # Use the improved answer_question function
+    title="Mech-bot: Your Car Mechanic Assistant",
+    description="Ask any car mechanic-related questions, and Mech-bot will try its best to assist you.",
+    submit_btn="Ask",
+    clear_btn="Clear Chat"
+)
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio interface
+iface.launch(debug=True)