Update app.py

app.py

@@ -1,4 +1,119 @@
 
+# import gradio as gr
+# from transformers import pipeline
+# import pytesseract
+# from sentence_transformers import SentenceTransformer, util
+# from PIL import Image
+# from typing import List
+# 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": f"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()
+
+# # 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})
+# #     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
+#     elif similarity_score >= 0.8:
+#         return 4
+#     elif similarity_score >= 0.7:
+#         return 3
+#     elif similarity_score >= 0.6:
+#         return 2
+#     else:
+#         return 1
+
+# # 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}"
+#     prompt=f"the student got grades: {grade} when Student's answer is: {student_answer} and Teacher's answer is: {model_answer}. justify the grades given to student"
+#     return grade, similarity_score * 100, feedback, prompt
+
+# # Main interface function for Gradio
+# def gradio_interface(image, languages: List[str], prompt=""):
+#     grade, similarity_score, feedback,prompt = evaluate_answer(image, languages)
+#     response = generate_response(prompt)
+#     return grade, similarity_score, feedback, response
+
+# # 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'),
+#         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()
+
+
+
+
+
+import os
+from groq import Groq
 import gradio as gr
 from transformers import pipeline
 import pytesseract
@@ -10,32 +125,40 @@ 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": f"Bearer {hf_TsCTtXxnvpmhFKABqKmcVLyLEhjQPsITSVx}"}
+# Initialize Groq client
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 
-# 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()
+# System prompt for Groq
+system_prompt = {
+    "role": "system",
+    "content": "You are a useful assistant. You reply with efficient answers."
+}
 
-# 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 interact with Groq for generating response
+async def chat_groq(message, history):
+    messages = [system_prompt]
 
-# Function to generate text response from GPT-2 model using Hugging Face API
-# def generate_response(prompt):
-#     response = query({"inputs": prompt})
-#     return response[0]['generated_text']
+    for msg in history:
+        messages.append({"role": "user", "content": str(msg[0])})
+        messages.append({"role": "assistant", "content": str(msg[1])})
+
+    messages.append({"role": "user", "content": str(message)})
+
+    response_content = ''
+    
+    stream = client.chat.completions.create(
+        model="llama3-70b-8192",
+        messages=messages,
+        max_tokens=1024,
+        temperature=1.3,
+        stream=True
+    )
+
+    for chunk in stream:
+        content = chunk.choices[0].delta.content
+        if content:
+            response_content += chunk.choices[0].delta.content
+        yield response_content
 
 # Extract text from an image using Tesseract
 def extract_text_from_image(filepath: str, languages: List[str]):
@@ -74,37 +197,38 @@ def evaluate_answer(image, languages):
     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}"
-    prompt=f"the student got grades: {grade} when Student's answer is: {student_answer} and Teacher's answer is: {model_answer}. justify the grades given to student"
+    prompt = f"The student got grade: {grade} when the student's answer is: {student_answer} and the teacher's answer is: {model_answer}. Justify the grade given to the student."
     return grade, similarity_score * 100, feedback, prompt
 
 # Main interface function for Gradio
-def gradio_interface(image, languages: List[str], prompt=""):
-    grade, similarity_score, feedback,prompt = evaluate_answer(image, languages)
-    response = generate_response(prompt)
+async def gradio_interface(image, languages: List[str], prompt="", history=[]):
+    grade, similarity_score, feedback, prompt = evaluate_answer(image, languages)
+    response = ""
+    async for result in chat_groq(prompt, history):
+        response = result  # Get the Groq response
     return grade, similarity_score, feedback, response
 
 # 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'),
-        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
-)
+with gr.Blocks(theme=gr.themes.Monochrome(), fill_height=True) as demo:
+    interface = gr.ChatInterface(gradio_interface,
+                                 inputs=[
+                                     gr.Image(type="filepath", label="Input"),
+                                     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()
-
+    demo.queue()
+    demo.launch()