Update main.py

main.py

@@ -1,44 +1,65 @@
-from flask import Flask, render_template, request, jsonify
-from langchain_openai import OpenAI
-from langchain.prompts import PromptTemplate
-
-prompt = open('website_text.txt', 'r').read()
-
-hotel_assistant_template = prompt + """
-You are the hotel manager of Landon Hotel, named "Mr. Landon". 
-Your expertise is exclusively in providing information and advice about anything related to Landon Hotel. 
-This includes any general Landon Hotel related queries. 
-You do not provide information outside of this scope. 
-If a question is not about Landon Hotel, respond with, "I can't assist you with that, sorry!" 
-Question: {question} 
-Answer: 
-"""
-
-hotel_assistant_prompt_template = PromptTemplate( 
-    input_variables=["question"], 
-    template=hotel_assistant_template 
-    ) 
-
-llm = OpenAI(model='gpt-3.5-turbo-instruct', temperature=0) 
-
-llm_chain = hotel_assistant_prompt_template | llm 
-
-def query_llm(question): 
-    response = llm_chain.invoke({'question': question}) 
-    return response 
-
-app = Flask(__name__) 
-
-@app.route("/") 
-def index(): 
-    return render_template("index.html") 
-
-@app.route("/chatbot", methods=["POST"]) 
-def chatbot(): 
-    data = request.get_json() 
-    question = data["question"] 
-    response = query_llm(question) 
-    return jsonify({"response": response}) 
-
-if __name__ == "__main__": 
+from flask import Flask, render_template, request, jsonify
+from transformers import pipeline, LlamaTokenizer, LlamaForCausalLM
+
+# Load the LLaMA model and tokenizer
+model_name = "huggingface/llama-model"  # Replace with the specific LLaMA model you want to use
+tokenizer = LlamaTokenizer.from_pretrained(model_name)
+model = LlamaForCausalLM.from_pretrained(model_name)
+
+# Initialize the text generation pipeline
+llm_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Load the prompt from the text file
+with open('website_text.txt', 'r') as file:
+    prompt = file.read()
+
+
+
+hotel_assistant_template = prompt + """
+You are the hotel manager of Landon Hotel, named "Mr. Landon". 
+Your expertise is exclusively in providing information and advice about anything related to Landon Hotel. 
+This includes any general Landon Hotel related queries. 
+You do not provide information outside of this scope. 
+If a question is not about Landon Hotel, respond with, "I can't assist you with that, sorry!" 
+Question: {question} 
+Answer: 
+"""
+def query_llm(question):
+    # Create the final prompt by inserting the question into the template
+    final_prompt = hotel_assistant_template.format(question=question)
+    
+    # Generate a response using the LLaMA model
+    response = llm_pipeline(final_prompt, max_length=150, do_sample=True)[0]['generated_text']
+    
+    # Extract the answer from the response (after "Answer:" text)
+    answer = response.split("Answer:", 1)[-1].strip()
+    
+    return answer
+hotel_assistant_prompt_template = PromptTemplate( 
+    input_variables=["question"], 
+    template=hotel_assistant_template 
+    ) 
+
+llm = OpenAI(model='gpt-3.5-turbo-instruct', temperature=0) 
+
+llm_chain = hotel_assistant_prompt_template | llm 
+
+def query_llm(question): 
+    response = llm_chain.invoke({'question': question}) 
+    return response 
+
+app = Flask(__name__) 
+
+@app.route("/") 
+def index(): 
+    return render_template("index.html") 
+
+@app.route("/chatbot", methods=["POST"]) 
+def chatbot(): 
+    data = request.get_json() 
+    question = data["question"] 
+    response = query_llm(question) 
+    return jsonify({"response": response}) 
+
+if __name__ == "__main__": 
     app.run(debug=True)