Update app.py

app.py

@@ -1,108 +1,3 @@
-# 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 pipeline
@@ -124,6 +19,19 @@ def query(payload):
     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):
+    response = query({"inputs": prompt})
+    
+    # Check if the response contains the expected format
+    if isinstance(response, list) and len(response) > 0 and 'generated_text' in response[0]:
+        return response[0]['generated_text']
+    else:
+        # Log the response if something unexpected is returned
+        print("Unexpected response format:", response)
+        return "Sorry, I couldn't generate a response."
+    
+
 # Function to generate text response from GPT-2 model using Hugging Face API
 def generate_response(prompt):
     response = query({"inputs": prompt})