Update app.py

app.py

@@ -17,6 +17,84 @@ sentence_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
 # Initialize Groq client
 client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 
+
+# System prompt for Groq
+system_prompt = {
+    "role": "system",
+    "content": "You are a useful assistant. You reply with efficient answers."
+}
+
+# Function to interact with Groq for generating response
+async def chat_groq(message, history):
+    messages = [system_prompt]
+
+    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]):
+    image = Image.open(filepath)
+    lang_str = '+'.join(languages)  # Join languages for Tesseract
+    return pytesseract.image_to_string(image=image, lang=lang_str)
+
+
+# Assign badges based on the grade
+def assign_badge(grade):
+    if grade == 5:
+        return "Gold Badge 🌟"
+    elif grade == 4:
+        return "Silver Badge 🥈"
+    elif grade == 3:
+        return "Bronze Badge 🥉"
+    else:
+        return "Keep Improving Badge 💪"
+
+
+# Categorize feedback into clarity, completeness, and accuracy
+def detailed_feedback(similarity_score):
+    if similarity_score >= 0.9:
+        return {"Clarity": "Excellent", "Completeness": "Complete", "Accuracy": "Accurate"}
+    elif similarity_score >= 0.8:
+        return {"Clarity": "Good", "Completeness": "Almost Complete", "Accuracy": "Mostly Accurate"}
+    elif similarity_score >= 0.7:
+        return {"Clarity": "Fair", "Completeness": "Partial", "Accuracy": "Some Errors"}
+    else:
+        return {"Clarity": "Needs Improvement", "Completeness": "Incomplete", "Accuracy": "Inaccurate"}
+
+# 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 get BERT embeddings
 def get_bert_embedding(text):
     inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)