Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("MohamedMotaz/Examination-llama-8b-4bit")
+model = AutoModelForCausalLM.from_pretrained("MohamedMotaz/Examination-llama-8b-4bit")
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# App Title
+st.title("Exam Corrector: Automated Grading with LLama 8b Model")
+
+# Instructions
+st.markdown("""
+### Instructions:
+- Upload or type both the **Model Answer** and the **Student Answer**.
+- Click on the **Grade Answer** button to get the grade and explanation.
+""")
+
+# Input fields for Model Answer and Student Answer
+model_answer = st.text_area("Model Answer", "The process of photosynthesis involves converting light energy into chemical energy.")
+student_answer = st.text_area("Student Answer", "Photosynthesis is when plants turn light into energy.")
+
+# Display documentation in the app
+with st.expander("Click to View Documentation"):
+    st.markdown("""
+    ## Exam-Corrector: A Fine-tuned LLama 8b Model
+    
+    Exam-corrector is a fine-tuned version of the LLama 8b model, specifically adapted to function as a written question corrector. This model grades student answers by comparing them against model answers using predefined instructions.
+    
+    ### Model Description:
+    The model ensures consistent and fair grading for written answers. Full marks are given to student answers that convey the complete meaning of the model answer, even with different wording.
+    
+    ### Grading Instructions:
+    - Model Answer is only used as a reference and does not receive marks.
+    - Full marks are awarded when student answers convey the full meaning of the model answer.
+    - Partial marks are deducted for incomplete or irrelevant information.
+    
+    ### Input Format:
+    - **Model Answer**: {model_answer}
+    - **Student Answer**: {student_answer}
+    
+    ### Output Format:
+    - **Grade**: {grade} 
+    - **Explanation**: {explanation}
+    
+    ### Training Details:
+    - Fine-tuned with LoRA (Low-Rank Adaptation).
+    - Percentage of trainable model parameters: 3.56%.
+    """)
+
+# Button to trigger grading
+if st.button("Grade Answer"):
+    # Combine inputs into the required prompt format
+    inputs = f"Model Answer: {model_answer}\n\nStudent Answer: {student_answer}\n\nResponse:"
+
+    # Tokenize the inputs
+    input_ids = tokenizer(inputs, return_tensors="pt").input_ids
+    
+    # Generate the response using the model
+    outputs = model.generate(input_ids)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Display the grade and explanation
+    st.subheader("Grading Results")
+    st.write(response)
+
+# Footer and app creator details
+st.markdown("""
+---
+**App created by [Engr. Hamesh Raj](https://www.linkedin.com/in/hamesh-raj)**
+""")