Update models_api.py

models_api.py

@@ -1,57 +1,86 @@
-import requests
-import json
-import os
-from dotenv import load_dotenv
-load_dotenv()
-
-def get_answer(model_name, context, question):    
-    llm_key = os.getenv("llm_key")
-    url = os.getenv("main_url")
-    # Construct the prompt for the model
-    prompt = f"You are a Question Answering Model. Can you help me answer the question: {question} from the context: {context}? Just return the answer only. The document may contain some Arabic text; please translate that to English if needed."
-
-    # Prepare payload for API request
-    payload = {
-        "model": model_name,
-        "messages": [
-            {
-                "role": "user",
-                "content": prompt
-            }
-        ],
-        "max_tokens": 300,
-        "temperature": 0.2
-    }
-    
-    headers = {
-        'Authorization': f'Bearer {llm_key}',
-        'Content-Type': 'application/json'
-    }
-    
-    # Convert payload to JSON string
-    json_payload = json.dumps(payload)
-    
-    try:
-        # Send POST request to the API
-        response = requests.post(url, headers=headers, data=json_payload)
-        
-        # Check if request was successful
-        if response.status_code == 200:            
-            response_data = response.json()                                 # Parse response JSON            
-            answer = response_data['choices'][0]['message']['content']      # Extract model's answer from response
-            return answer
-        else:
-            print(f"Request failed with status code: {response.status_code}")
-            return None
-    
-    except requests.exceptions.RequestException as e:
-        print(f"Error occurred: {e}")
-        return None
-
-from huggingface_hub import InferenceClient
-
-def get_hugging_face_answer(model_name, context, question):
-    client = InferenceClient(model_name, token=os.getenv("HF_TOKEN"))
-    prompt = f"You are a Question Answering Model. Can you help me answer the question: {question} from the context: {context}? Just return the answer only. The document may contain some Arabic text; please translate that to English if needed."
-    output = client.text_generation(prompt , max_new_tokens = 200, stream=True, temperature=0.1)
-    return output
+# import requests
+# import json
+# import os
+# from dotenv import load_dotenv
+# load_dotenv()
+
+# def get_answer(model_name, context, question):    
+#     llm_key = os.getenv("llm_key")
+#     url = os.getenv("main_url")
+#     # Construct the prompt for the model
+#     prompt = f"You are a Question Answering Model. Can you help me answer the question: {question} from the context: {context}? Just return the answer only. The document may contain some Arabic text; please translate that to English if needed."
+
+#     # Prepare payload for API request
+#     payload = {
+#         "model": model_name,
+#         "messages": [
+#             {
+#                 "role": "user",
+#                 "content": prompt
+#             }
+#         ],
+#         "max_tokens": 300,
+#         "temperature": 0.2
+#     }
+    
+#     headers = {
+#         'Authorization': f'Bearer {llm_key}',
+#         'Content-Type': 'application/json'
+#     }
+    
+#     # Convert payload to JSON string
+#     json_payload = json.dumps(payload)
+    
+#     try:
+#         # Send POST request to the API
+#         response = requests.post(url, headers=headers, data=json_payload)
+        
+#         # Check if request was successful
+#         if response.status_code == 200:            
+#             response_data = response.json()                                 # Parse response JSON            
+#             answer = response_data['choices'][0]['message']['content']      # Extract model's answer from response
+#             return answer
+#         else:
+#             print(f"Request failed with status code: {response.status_code}")
+#             return None
+    
+#     except requests.exceptions.RequestException as e:
+#         print(f"Error occurred: {e}")
+#         return None
+
+# from huggingface_hub import InferenceClient
+
+# def get_hugging_face_answer(model_name, context, question):
+#     client = InferenceClient(model_name, token=os.getenv("HF_TOKEN"))
+#     prompt = f"You are a Question Answering Model. Can you help me answer the question: {question} from the context: {context}? Just return the answer only. The document may contain some Arabic text; please translate that to English if needed."
+#     output = client.text_generation(prompt , max_new_tokens = 200, stream=True, temperature=0.1)
+#     return output
+
+import os
+from groq import Groq
+from dotenv import load_dotenv
+load_dotenv()
+
+GROQ_API_KEY = os.getenv('GROQ_API')
+def get_answer_from_context(model_name, context, question):
+    client = Groq(api_key=GROQ_API_KEY)
+    chat_completion = client.chat.completions.create(
+        model=model_name,
+        messages=[
+            {
+                "role": "system",
+                "content": f"You are a Question Answering LLM that uses context provided: {context} to answer user's query. Just return the answer only. The document may contain some Arabic text; please translate that to English if needed."
+            },
+            {
+                "role": "user",
+                "content": question,
+            }
+        ],        
+        temperature=0.2,
+        max_tokens=200,
+        top_p=1,
+        stop=None,
+        stream=False,
+        # response_format={"type": "json_object"}
+    )
+    return chat_completion.choices[0].message.content