Modified Gradio interfade

app.py

@@ -99,14 +99,12 @@ def gradio_app():
 
     with gr.Blocks(theme=theme) as demo:
         with gr.Row():
-            with gr.Column(scale=0.1):
-                gr.Image("rag_image.jpg", elem_id="flor-banner", scale=1, height=256, width=256, show_label=False, show_download_button = False, show_share_button = False)
+            # with gr.Column(scale=0.1):
+                # gr.Image("rag_image.jpg", elem_id="flor-banner", scale=1, height=256, width=256, show_label=False, show_download_button = False, show_share_button = False)
             with gr.Column():
                 gr.Markdown(
-                    """# TEST de Retrieval-Augmented Generation para proyectos de LangTech
-                    🔍 
-                    
-                    ⚠️ **Advertencias**: Esta es una versión experimental. 👀
+                    """## TEST de RAG (Retrieval-Augmented Generation) for LangTech projects (RENFE)
+                    ⚠️ **Advertencias**: Esta es una versión experimental.
                     """
                 )
         with gr.Row(equal_height=True):

rag.py

@@ -100,11 +100,10 @@ class RAG:
 
         api_key = os.getenv("HF_TOKEN")
 
-
         headers = {
-        "Accept" : "application/json",
-        "Authorization": f"Bearer {api_key}",
-        "Content-Type": "application/json" 
+            "Accept" : "application/json",
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json" 
         }
 
         query = f"### Instruction\n{instruction}\n\n### Context\n{context}\n\n### Answer\n "
@@ -112,16 +111,22 @@ class RAG:
 
 
         payload = {
-        "inputs": query,
-        "parameters": model_parameters
+            "inputs": query,
+            "parameters": model_parameters
         }
         
         response = requests.post(self.model_name, headers=headers, json=payload)
 
         return response.json()[0]["generated_text"].split("###")[-1][8:]
 
+    
     def predict_completion(self, instruction, context, model_parameters):
 
+        model = os.getenv("MODEL")
+        if not model:
+            logging.error("No model specified in the environment variable 'MODEL'.")
+            return "Model endpoint not specified."
+
         client = OpenAI(
                 base_url=os.getenv("MODEL"),
                 api_key=os.getenv("HF_TOKEN")
@@ -198,9 +203,8 @@ class RAG:
             text_context, full_context, source = self.beautiful_context(docs, source_metadata=self.source_metadata)
             # print(text_context)
             del model_parameters["NUM_CHUNKS"]
-
-            response ="HOLI!!"
-            # response = self.predict_completion(prompt, text_context, model_parameters)
+            
+            response = self.predict_completion(prompt, text_context, model_parameters)
             if not response:
                 return self.NO_ANSWER_MESSAGE