Update app.py

app.py

@@ -29,36 +29,55 @@ import streamlit.components.v1 as components
 from datetime import datetime
 from streamlit_js_eval import streamlit_js_eval
 from streamlit_pdf_viewer import pdf_viewer
+# def consume_llm_api(prompt):
+#     """
+#     Sends a prompt to the LLM API and processes the streamed response.
+#     """
+#     url = "https://8417-201-238-124-65.ngrok-free.app/api/llm-response"
+#     headers = {"Content-Type": "application/json"}
+#     payload = {"prompt": prompt}
+
+#     try:
+#         print("Sending prompt to the LLM API...")
+#         with requests.post(url, json=payload, headers=headers, stream=True) as response:
+#             response.raise_for_status()
+#             print("Response from LLM API:\n")
+#             for line in response:
+#                 yield(line.decode('utf-8'))
+#             # print(type(response))
+#             # yield(response)
+#     except requests.RequestException as e:
+#         print(f"Error consuming API: {e}")
+#     except Exception as e:
+#         print(f"Unexpected error: {e}")
+
 def consume_llm_api(prompt):
-    """
-    Sends a prompt to the LLM API and processes the streamed response.
-    """
-    url = "https://8417-201-238-124-65.ngrok-free.app/api/llm-response"
-    headers = {"Content-Type": "application/json"}
-    payload = {"prompt": prompt}
+    
+    client = Groq(
+        api_key="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"
+    )
 
-    try:
-        print("Sending prompt to the LLM API...")
-        with requests.post(url, json=payload, headers=headers, stream=True) as response:
-            response.raise_for_status()
-            print("Response from LLM API:\n")
-            for line in response:
-                yield(line.decode('utf-8'))
-            # print(type(response))
-            # yield(response)
-    except requests.RequestException as e:
-        print(f"Error consuming API: {e}")
-    except Exception as e:
-        print(f"Unexpected error: {e}")
+    completion = client.chat.completions.create(
+        
+        model="llama-3.3-70b-versatile",
+        messages=[
 
-# def consume_llm_api(prompt):
-#             llm_stream = llm_text_response()(prompt)
-    
-#             # Create a generator to stream the data
-            
-#             for chunk in llm_stream:
-                
-#                 yield chunk
+            {
+                "role": "system",
+                "content": prompt
+            },
+        ],
+
+        temperature=1,
+        # max_completion_tokens=1024,
+        top_p=1,
+        stream=True,
+        stop=None,
+    )
+
+    for chunk in completion:
+        if chunk.choices[0].delta.content:
+            yield chunk.choices[0].delta.content
 
                 
 def send_prompt():