Update app.py

app.py

@@ -3,13 +3,22 @@ import streamlit as st
 # import ollama
 import transformers
 import torch
-from huggingface_hub import login
-login()
+
 model_id = "meta-llama/Meta-Llama-3-8B"
+#model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+
 pipeline = transformers.pipeline(
-    "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
 )
 
+terminators = [
+    pipeline.tokenizer.eos_token_id,
+    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
 # Load your CSV file
 df = pd.read_csv("your_file.csv")
 
@@ -19,25 +28,37 @@ df = pd.read_csv("your_file.csv")
 #     return response['message']['content']
 
 def generate_response(question):
-    response = pipeline(questions)
+    outputs = pipeline(
+    question,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+    )
+    response = (outputs[0]["generated_text"][-1])
     return response
     
 # Define the functions for solving problems, giving hints, and creating similar problems
 def show_problem(exam, year, problem):
     problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem"].values[0]
-    return generate_response(f"Render the following problem in latex: '{problem_statement}'.")
+    prompt = [{"role": "user", "content": "Render the following problem in latex:" + problem_statement}]
+    return generate_response(prompt)
 
 def solve_problem(exam, year, problem):
     solution_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["solution"].values[0]
-    return generate_response(f"Explain the solution: {solution_statement} step by step.")
-
+    prompt = [{"role": "user", "content": "Explain the solution:" + solution_statement + "step by step."}]
+    return generate_response(prompt)
+                             
 def give_hints(exam, year, problem):
     solution_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["solution"].values[0]
-    return generate_response(f"Give step by step hints for the following solution: {solution_statement}")
+    prompt = [{"role": "user", "content": "Give step by step hints for the following solution:" + solution_statement}]
+    return generate_response(prompt)
 
 def create_similar_problem(exam, year, problem):
     problem_statement = df[(df["exam name"] == exam) & (df["year"] == year) & (df["problem number"] == problem)]["problem"].values[0]
-    return generate_response(f" You are an AI assistant of a Statistics, Probability, and Mathematics University Professor. Your only job is to create mcq problem. Create a mcq problem similar to the following mcq problem: {problem_statement}")
+    prompt = [{"role": "user", "content": "You are an AI assistant of a Statistics, Probability, and Mathematics University Professor. Your only job is to create mcq problem. Create a mcq problem similar to the following mcq problem" + problem_statement"}]
+    return generate_response(prompt)
 
 # Streamlit interface
 st.markdown("<h1 style='text-align: center;'>Cheenta Academy Experts x AI</h1>", unsafe_allow_html=True)