Update app.py

app.py

@@ -29,27 +29,38 @@ 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://ff44-70-167-32-130.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://ff44-70-167-32-130.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}")
+            llm_stream = llm_text_response()(prompt)
+    
+            # Create a generator to stream the data
+            
+            for chunk in llm_stream:
+                
+                yield chunk
+
+                
 def send_prompt():
     return "please respond according to the prompt asked below from the above context"
     
@@ -105,25 +116,36 @@ def model_single_out(prompt):
     image = pipe(prompt).images[0]
     return image
 
-def model_out_put(init_image,mask_image,prompt,negative_prompt):
-    API_URL = "https://ff44-70-167-32-130.ngrok-free.app/api/llm-response"
-    initial_image_base64 = numpy_to_list(np.array(init_image))
-    mask_image_base64 = numpy_to_list(np.array(mask_image))
-    payload = {
-        "prompt": prompt,  # Replace with your desired prompt
-        "initial_img": initial_image_base64,
-        "masked_img": mask_image_base64,
-        "negative_prompt": negative_prompt # Replace with your negative prompt
-    }
-    response_ = requests.post(API_URL, json=payload)
-    response_data = response_.json()
-    output_image_base64 = response_data.get("img", "")
-
-    output_image=np.array(output_image_base64,dtype=np.uint8)
-
-    output_image = Image.fromarray(output_image)
-    # output_image.show()
-    return output_image
+# def model_out_put(init_image,mask_image,prompt,negative_prompt):
+#     API_URL = "https://ff44-70-167-32-130.ngrok-free.app/api/llm-response"
+#     initial_image_base64 = numpy_to_list(np.array(init_image))
+#     mask_image_base64 = numpy_to_list(np.array(mask_image))
+#     payload = {
+#         "prompt": prompt,  # Replace with your desired prompt
+#         "initial_img": initial_image_base64,
+#         "masked_img": mask_image_base64,
+#         "negative_prompt": negative_prompt # Replace with your negative prompt
+#     }
+#     response_ = requests.post(API_URL, json=payload)
+#     response_data = response_.json()
+#     output_image_base64 = response_data.get("img", "")
+
+#     output_image=np.array(output_image_base64,dtype=np.uint8)
+
+#     output_image = Image.fromarray(output_image)
+#     # output_image.show()
+#     return output_image
+
+def model_out_put(init_image, mask_image, prompt, negative_prompt):
+    # Run the inpainting pipeline
+    pipeline_ = load_model()
+    image = pipeline_(
+        prompt=prompt, 
+        negative_prompt=negative_prompt, 
+        image=init_image, 
+        mask_image=mask_image
+    ).images[0]
+    return image
 
 @st.cache_resource
 def multimodel():