Update app.py

app.py

@@ -1,48 +1,152 @@
+# import gradio as gr
+# from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+# import torch
+# import numpy as np
+# import cv2
+# from PIL import Image
+# import pytesseract
+# from sentence_transformers import SentenceTransformer, util
+# import io
+# from typing import List
+
+# def extract_text_from_image(filepath: str, languages: List[str]):
+#     image = Image.open(filepath)
+#     return pytesseract.image_to_string(image=image, lang=', '.join(languages))
+
+# # tess.pytesseract.tesseract_cmd = r"tesseract"
+
+# import requests
+
+# API_URL = "https://api-inference.huggingface.co/models/openai-community/gpt2"
+# headers = {"Authorization": "hf_TsCTtXxnvpmhFKABqKmcVLyLEhjQPsITSVx"}
+
+# def query(payload):
+# 	response = requests.post(API_URL, headers=headers, json=payload)
+# 	return response.json()
+	
+# # output = query({
+# # 	"inputs": "Can you please let us know more details about your ",
+# # })
+
+# def generate_response(prompt):
+#     # Generate response from the API
+#     response = query({"inputs":prompt})
+#     return response[0]['generated_text']
+
+
+# def get_embedding(text):
+#     return model1.encode(text, convert_to_tensor=True)
+
+# def calculate_similarity(text1, text2):
+#     embedding1 = get_embedding(text1)
+#     embedding2 = get_embedding(text2)
+#     similarity = util.pytorch_cos_sim(embedding1, embedding2)
+#     return similarity.item()
+
+# def get_grade(similarity_score):
+#     if similarity_score >= 0.9:
+#         return 5
+#     elif similarity_score >= 0.8:
+#         return 4
+#     elif similarity_score >= 0.7:
+#         return 3
+#     elif similarity_score >= 0.6:
+#         return 2
+#     else:
+#         return 1
+
+
+# def evaluate_answer(image,languages):
+#     student_answer = extract_text_from_image(image,languages)
+#     model_answer = "The process of photosynthesis helps plants produce glucose using sunlight."
+#     similarity_score = calculate_similarity(student_answer, model_answer)
+#     grade = get_grade(similarity_score)
+#     feedback = f"Student's answer: {student_answer}\nTeacher's answer: {model_answer}"
+#     return grade, similarity_score * 100, feedback
+
+# def generate_response(prompt):
+#     # Generate response from the new model using the pipeline
+#     response = pipe(prompt, max_length=150, temperature=0.7)
+#     return response[0]['generated_text']
+
+# def gradio_interface(image, languages: List[str]):
+#     grade, similarity_score, feedback = evaluate_answer(image,languages)
+#     response = generate_response(prompt)
+#     return grade, similarity_score, response
+
+# # # Define Gradio interface
+# # interface = gr.Interface(
+# #     fn=gradio_interface,
+# #     inputs=[gr.Image(type="pil"), gr.Textbox(lines=2, placeholder="Enter your prompt here")],
+# #     outputs=[gr.Label(), gr.Label(), gr.Textbox(), gr.Textbox()],
+# #     live=True
+# # )
+# language_choices = pytesseract.get_languages()
+# interface = gr.Interface(
+#     fn=gradio_interface,
+#     inputs=[
+#         gr.Image(type="filepath", label="Input"), 
+#         gr.CheckboxGroup(language_choices, type="value", value=['eng'], label='language')
+#         ],
+#     outputs=[gr.Text(label="Grade"), gr.Number(label="Similarity Score (%)"), gr.Text(label="Feedback")],
+#     title="Automated Grading System",
+#     description="Upload an image of your answer sheet to get a grade from 1 to 5, similarity score, and feedback based on the model answer.",
+#     live=True
+# )
+    
+
+# if __name__ == "__main__":
+#     interface.launch()
+
+
+
+
+
+
+
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-import torch
-import numpy as np
-import cv2
-from PIL import Image
+from transformers import pipeline
 import pytesseract
 from sentence_transformers import SentenceTransformer, util
-import io
+from PIL import Image
 from typing import List
-
-def extract_text_from_image(filepath: str, languages: List[str]):
-    image = Image.open(filepath)
-    return pytesseract.image_to_string(image=image, lang=', '.join(languages))
-
-# tess.pytesseract.tesseract_cmd = r"tesseract"
-
 import requests
 
+# Initialize sentence transformer model
+model1 = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# Hugging Face API details
 API_URL = "https://api-inference.huggingface.co/models/openai-community/gpt2"
-headers = {"Authorization": "hf_TsCTtXxnvpmhFKABqKmcVLyLEhjQPsITSVx"}
+headers = {"Authorization": "Bearer hf_TsCTtXxnvpmhFKABqKmcVLyLEhjQPsITSVx"}
 
+# Function to interact with Hugging Face API for GPT-2
 def query(payload):
-	response = requests.post(API_URL, headers=headers, json=payload)
-	return response.json()
-	
-# output = query({
-# 	"inputs": "Can you please let us know more details about your ",
-# })
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
 
+# Function to generate text response from GPT-2 model using Hugging Face API
 def generate_response(prompt):
-    # Generate response from the API
-    response = query({"inputs":prompt})
+    response = query({"inputs": prompt})
     return response[0]['generated_text']
 
+# Extract text from an image using Tesseract
+def extract_text_from_image(filepath: str, languages: List[str]):
+    image = Image.open(filepath)
+    lang_str = '+'.join(languages)  # Join languages for Tesseract
+    return pytesseract.image_to_string(image=image, lang=lang_str)
 
+# Function to get embeddings for text using SentenceTransformer
 def get_embedding(text):
     return model1.encode(text, convert_to_tensor=True)
 
+# Calculate similarity between two texts using cosine similarity
 def calculate_similarity(text1, text2):
     embedding1 = get_embedding(text1)
     embedding2 = get_embedding(text2)
     similarity = util.pytorch_cos_sim(embedding1, embedding2)
     return similarity.item()
 
+# Assign grades based on similarity score
 def get_grade(similarity_score):
     if similarity_score >= 0.9:
         return 5
@@ -55,45 +159,43 @@ def get_grade(similarity_score):
     else:
         return 1
 
-
-def evaluate_answer(image,languages):
-    student_answer = extract_text_from_image(image,languages)
+# Function to evaluate student's answer by comparing it to a model answer
+def evaluate_answer(image, languages):
+    student_answer = extract_text_from_image(image, languages)
     model_answer = "The process of photosynthesis helps plants produce glucose using sunlight."
     similarity_score = calculate_similarity(student_answer, model_answer)
     grade = get_grade(similarity_score)
     feedback = f"Student's answer: {student_answer}\nTeacher's answer: {model_answer}"
     return grade, similarity_score * 100, feedback
 
-def generate_response(prompt):
-    # Generate response from the new model using the pipeline
-    response = pipe(prompt, max_length=150, temperature=0.7)
-    return response[0]['generated_text']
-
-def gradio_interface(image, languages: List[str]):
-    grade, similarity_score, feedback = evaluate_answer(image,languages)
+# Main interface function for Gradio
+def gradio_interface(image, languages: List[str], prompt):
+    grade, similarity_score, feedback = evaluate_answer(image, languages)
     response = generate_response(prompt)
-    return grade, similarity_score, response
+    return grade, similarity_score, feedback, response
 
-# # Define Gradio interface
-# interface = gr.Interface(
-#     fn=gradio_interface,
-#     inputs=[gr.Image(type="pil"), gr.Textbox(lines=2, placeholder="Enter your prompt here")],
-#     outputs=[gr.Label(), gr.Label(), gr.Textbox(), gr.Textbox()],
-#     live=True
-# )
+# Get available Tesseract languages
 language_choices = pytesseract.get_languages()
+
+# Define Gradio interface
 interface = gr.Interface(
     fn=gradio_interface,
     inputs=[
         gr.Image(type="filepath", label="Input"), 
-        gr.CheckboxGroup(language_choices, type="value", value=['eng'], label='language')
-        ],
-    outputs=[gr.Text(label="Grade"), gr.Number(label="Similarity Score (%)"), gr.Text(label="Feedback")],
+        gr.CheckboxGroup(language_choices, type="value", value=['eng'], label='language'),
+        gr.Textbox(lines=2, placeholder="Enter your prompt here", label="Prompt")
+    ],
+    outputs=[
+        gr.Text(label="Grade"), 
+        gr.Number(label="Similarity Score (%)"), 
+        gr.Text(label="Feedback"), 
+        gr.Text(label="Generated Response")
+    ],
     title="Automated Grading System",
     description="Upload an image of your answer sheet to get a grade from 1 to 5, similarity score, and feedback based on the model answer.",
     live=True
 )
-    
 
 if __name__ == "__main__":
     interface.launch()
+