add sarvam transalte

accounts/admin.py

@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.

accounts/apps.py

@@ -0,0 +1,6 @@
+from django.apps import AppConfig
+
+
+class AccountsConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'accounts'

accounts/models.py

@@ -0,0 +1,3 @@
+from django.db import models
+
+# Create your models here.

accounts/tests.py

@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.

accounts/urls.py

@@ -0,0 +1,6 @@
+from django.urls import path, include
+
+urlpatterns = [
+    path('api/', include('djoser.urls')),
+    path('api/', include('djoser.urls.authtoken')),
+]

accounts/views.py

@@ -0,0 +1,3 @@
+from django.shortcuts import render
+
+# Create your views here.

inference/rag_process.py

@@ -0,0 +1,99 @@
+from mistralai import Mistral
+import requests
+import numpy as np
+import faiss
+import os
+
+api_key=os.getenv("MISTRAL_API_KEY", "")
+client = Mistral(api_key=api_key)
+
+def get_data():
+
+    response = requests.get('https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt')
+    text = response.text
+
+    f = open('essay.txt', 'w')
+    f.write(text)
+    f.close()
+
+
+    len(text)
+    return text
+
+def create_chunks(text):
+
+    chunk_size = 2048
+    chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
+
+    len(chunks)
+    return chunks
+
+def get_text_embedding(input):
+    embeddings_batch_response = client.embeddings.create(
+          model="mistral-embed",
+          inputs=input
+      )
+    return embeddings_batch_response.data[0].embedding
+
+
+def load_vectors(chunks):
+    text_embeddings = np.array([get_text_embedding(chunk) for chunk in chunks])
+
+    text_embeddings.shape
+
+    d = text_embeddings.shape[1]
+    index = faiss.IndexFlatL2(d)
+    index.add(text_embeddings)
+    return index
+
+def create_embed_for_question(question):
+    
+    question_embeddings = np.array([get_text_embedding(question)])
+    question_embeddings.shape
+    return question_embeddings
+
+def get_similar_chunks(index, question_embeddings, chunks):
+    D, I = index.search(question_embeddings, k=2)
+    print(I)
+    retrieved_chunk = [chunks[i] for i in I.tolist()[0]]
+    print(retrieved_chunk)
+    return retrieved_chunk
+
+def create_prompt(retrieved_chunk, question):
+    prompt = f"""
+    Context information is below.
+    ---------------------
+    {retrieved_chunk}
+    ---------------------
+    Given the context information and not prior knowledge, answer the query.
+    Query: {question}
+    Answer:
+    """
+    return prompt
+
+
+def run_mistral(user_message, model="mistral-large-latest"):
+    messages = [
+        {
+            "role": "user", "content": user_message
+        }
+    ]
+    chat_response = client.chat.complete(
+        model=model,
+        messages=messages
+    )
+    return (chat_response.choices[0].message.content)
+
+def main():
+    text = get_data()
+    chunks = create_chunks(text=text)
+    question = "What were the two main things the author worked on before college?"
+
+    index = load_vectors(chunks=chunks)
+    question_embeddings = create_embed_for_question(question=question)
+    retrieved_chunk = get_similar_chunks(index, question_embeddings, chunks)
+    prompt = create_prompt(retrieved_chunk, question)
+    answer = run_mistral(prompt)
+    print(answer)
+
+main()

inference/sarvam_inference.py

@@ -0,0 +1,32 @@
+
+import requests
+import os
+
+def translate_text(text_string, source_lang, target_lang):
+
+    api_key=os.getenv("SARVAM_API_KEY", "")
+    url = "https://api.sarvam.ai/translate"
+    payload = {
+        "input": text_string,
+        "source_language_code": source_lang,
+        "target_language_code": target_lang,
+        "speaker_gender": "Male",
+        "mode": "formal",
+        "model": "mayura:v1",
+        "enable_preprocessing": True
+    }
+    headers = {"Content-Type": "application/json",
+            'API-Subscription-Key': f"{api_key}"
+            }
+
+    response = requests.request("POST", url, json=payload, headers=headers)
+
+    #print(response.text)
+    return response.text
+
+
+#text_string = "Who is the founder of Bengaluru?"
+#source_lang = "en-IN"
+#target_lang = "kn-IN"
+#response_text = translate_text(text_string, source_lang, target_lang)
+#print(response_text)

recipes/urls.py

@@ -1,10 +1,11 @@
 from django.urls import path
 from .views import  recipe_generate_route
-from .views import VisionLLMView, NIMVisionLLMView, TextLLMView
+from .views import VisionLLMView, NIMVisionLLMView, TextLLMView, TranslateLLMView
 
 urlpatterns = [
     path('recipe_generate/', recipe_generate_route, name='recipe_generate'),
     path('vision_llm_url/', VisionLLMView.as_view()),
     path('nim_vision_llm_url/', NIMVisionLLMView.as_view()),
     path('text_llm_url/', TextLLMView.as_view()),
+    path('translate_llm_url/', TranslateLLMView.as_view()),
 ]

recipes/views.py

@@ -10,43 +10,80 @@ import base64
 import json
 import requests
 
+class TranslateLLMView(APIView):
+    def post(self, request, format=None):
+        try: 
+            data = request.data
+            prompt =  data['messages'][0]['prompt']
+            # Specify model
+            source_language = data['sourceLanguage']
+            target_language = data['targetLanguage']
+            #model = data['model']
+            # Define the messages for the chat
+            api_key=os.getenv("SARVAM_API_KEY", "")
+            url = "https://api.sarvam.ai/translate"
+
+            payload = {
+                "input": prompt,
+                "source_language_code": source_language,
+                "target_language_code": target_language,
+                "speaker_gender": "Male",
+                "mode": "formal",
+                "model": "mayura:v1",
+                "enable_preprocessing": True
+            }
+            headers = {"Content-Type": "application/json",
+                    'API-Subscription-Key': f"{api_key}"
+                    }
+
+            response = requests.request("POST", url, json=payload, headers=headers)
+            content = response.text
+            #print(chat_response.choices[0].message.content)
+            # Return the content of the response
+            return Response({"response": content})
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return Response({'error': 'Something went wrong'}, status=500)
 
 class TextLLMView(APIView):
     def post(self, request, format=None):
-        data = request.data
-        api_key = os.environ["MISTRAL_API_KEY"]
-
-        # Initialize the Mistral client
-        client = Mistral(api_key=api_key)
-
-        prompt =  data['messages'][0]['prompt']
-        # Specify model
-        #model = "pixtral-12b-2409"
-        model = data['model']
-        # Define the messages for the chat
-        messages = [
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": prompt
-                    }
-                ]
-            }
-        ]
+        try:
+            data = request.data
+            api_key = os.environ["MISTRAL_API_KEY"]
+
+            # Initialize the Mistral client
+            client = Mistral(api_key=api_key)
 
-        # Get the chat response
-        chat_response = client.chat.complete(
-            model=model,
-            messages=messages
-        )
+            prompt =  data['messages'][0]['prompt']
+            # Specify model
+            #model = "pixtral-12b-2409"
+            model = data['model']
+            # Define the messages for the chat
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": prompt
+                        }
+                    ]
+                }
+            ]
 
-        content = chat_response.choices[0].message.content
-        #print(chat_response.choices[0].message.content)
-        # Return the content of the response
-        return Response({"response": content})
+            # Get the chat response
+            chat_response = client.chat.complete(
+                model=model,
+                messages=messages
+            )
 
+            content = chat_response.choices[0].message.content
+            #print(chat_response.choices[0].message.content)
+            # Return the content of the response
+            return Response({"response": content})
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return Response({'error': 'Something went wrong'}, status=500)
 
 
 @api_view(['GET'])
@@ -69,44 +106,48 @@ def recipe_generate_route(request):
 
 class VisionLLMView(APIView):
     def post(self, request, format=None):
-        data = request.data
-        api_key = os.environ["MISTRAL_API_KEY"]
-
-        # Initialize the Mistral client
-        client = Mistral(api_key=api_key)
-
-        image_data = (data['messages'][0]['image'][0])
-        prompt =  data['messages'][0]['prompt']
-        # Specify model
-        #model = "pixtral-12b-2409"
-        model = data['model']
-        # Define the messages for the chat
-        messages = [
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": prompt
-                    },
-                    {
-                        "type": "image_url",
-                        "image_url": f"data:image/jpeg;base64,{image_data}" 
-                    }
-                ]
-            }
-        ]
+        try:
+            data = request.data
+            api_key = os.environ["MISTRAL_API_KEY"]
+
+            # Initialize the Mistral client
+            client = Mistral(api_key=api_key)
+
+            image_data = (data['messages'][0]['image'][0])
+            prompt =  data['messages'][0]['prompt']
+            # Specify model
+            #model = "pixtral-12b-2409"
+            model = data['model']
+            # Define the messages for the chat
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": prompt
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": f"data:image/jpeg;base64,{image_data}" 
+                        }
+                    ]
+                }
+            ]
 
-        # Get the chat response
-        chat_response = client.chat.complete(
-            model=model,
-            messages=messages
-        )
+            # Get the chat response
+            chat_response = client.chat.complete(
+                model=model,
+                messages=messages
+            )
 
-        content = chat_response.choices[0].message.content
-        #print(chat_response.choices[0].message.content)
-        # Return the content of the response
-        return Response({"response": content})
+            content = chat_response.choices[0].message.content
+            #print(chat_response.choices[0].message.content)
+            # Return the content of the response
+            return Response({"response": content})
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return Response({'error': 'Something went wrong'}, status=500)
 
 
 class NIMVisionLLMView(APIView):
@@ -155,5 +196,5 @@ class NIMVisionLLMView(APIView):
 
 
         except Exception as e:  # Added general exception handling
-            print(f"Error: {e}")
-        return None
+            print(f"An error occurred: {e}")
+            return Response({'error': 'Something went wrong'}, status=500)

requirements.txt

@@ -2,35 +2,50 @@ annotated-types==0.7.0
 anyio==4.5.0
 asgiref==3.8.1
 certifi==2024.8.30
+cffi==1.17.1
 charset-normalizer==3.3.2
 click==8.1.7
+cryptography==43.0.1
+defusedxml==0.8.0rc2
 Django==5.1.1
 django-cors-headers==4.4.0
 django-filter==24.3
+django-templated-mail==1.1.1
 django_csp==3.8
 djangorestframework==3.15.2
+djangorestframework-simplejwt==5.3.1
+djoser==2.2.3
 drf-yasg==1.21.7
 eval_type_backport==0.2.0
 exceptiongroup==1.2.2
+faiss-cpu==1.8.0.post1
 h11==0.14.0
 httpcore==1.0.5
 httpx==0.27.2
 idna==3.10
 inflection==0.5.1
 jsonpath-python==1.0.6
+Markdown==3.7
 mistralai==1.1.0
 mypy-extensions==1.0.0
-numpy==2.1.1
+numpy==1.26.4
+oauthlib==3.2.2
 packaging==24.1
 pandas==2.2.3
+pycparser==2.22
 pydantic==2.9.2
 pydantic_core==2.23.4
+PyJWT==2.9.0
 python-dateutil==2.8.2
+python3-openid==3.2.0
 pytz==2024.2
 PyYAML==6.0.2
 requests==2.32.3
+requests-oauthlib==2.0.0
 six==1.16.0
 sniffio==1.3.1
+social-auth-app-django==5.4.2
+social-auth-core==4.5.4
 sqlparse==0.5.1
 typing-inspect==0.9.0
 typing_extensions==4.12.2

spaces/settings.py

@@ -43,6 +43,10 @@ INSTALLED_APPS = [
     'space_walks',
     'recipes',
     'csp',
+    'rest_framework',
+    'rest_framework.authtoken',
+    'djoser',
+    'accounts'
 ]
 
 MIDDLEWARE = [
@@ -71,6 +75,10 @@ CSP_FONT_SRC = ("'self'", "https:", "data:")
 CORS_ORIGIN_WHITELIST = [
     'https://gaganyatri.in',
 ]
+CORS_ALLOWED_ORIGINS = [
+    "http://localhost:3000",
+    "http://127.0.0.1:3000"
+]
 
 CORS_ALLOW_CREDENTIALS = True
 
@@ -152,4 +160,16 @@ REST_FRAMEWORK = {
     'DEFAULT_RENDERER_CLASSES': [
         'rest_framework.renderers.JSONRenderer',
     ],
+    'DEFAULT_AUTHENTICATION_CLASSES': (
+        'rest_framework.authentication.TokenAuthentication',
+    ),
+    'DEFAULT_PERMISSION_CLASSES': [
+# TODO - remove comment after authentication in frontend is enforced
+#        'rest_framework.permissions.IsAuthenticated',
+    ]
+}
+
+# configure Djoser
+DJOSER = {
+    "USER_ID_FIELD": "username"
 }

spaces/urls.py

@@ -40,4 +40,5 @@ urlpatterns = [
     path('redoc/', schema_view.with_ui('redoc', cache_timeout=0), name='schema-redoc'),
     path('api/v1/space_walks/', include('space_walks.urls')),
     path('api/v1/recipes/', include('recipes.urls')),
+    path('api/v1/accounts/', include('accounts.urls')),
 ]