Update app.py

app.py

@@ -1,14 +1,13 @@
 import gradio as gr
 import requests
 import os
-import json # Import json for better error message handling
+import json
 
 # --- Configuration ---
-# Using the specific Nebius/HF router URL from your snippet
+# API configuration but hidden from end users
 API_URL = "https://router.huggingface.co/nebius/v1/chat/completions"
-# Using the model from your snippet
-MODEL_ID = "google/gemma-3-27b-it-fast"
-# Get Hugging Face token from environment variable/secrets
+MODEL_ID = "google/gemma-3-27b-it-fast"  # Real model hidden from users
+PUBLIC_MODEL_NAME = "AgriAssist_LLM"     # What users will see
 HF_TOKEN = os.getenv("HF_TOKEN")
 
 if not HF_TOKEN:
@@ -16,96 +15,71 @@ if not HF_TOKEN:
 
 HEADERS = {"Authorization": f"Bearer {HF_TOKEN}", "Content-Type": "application/json"}
 
-# --- No Local Model Loading Needed ---
-print(f"Application configured to use Hugging Face Inference API.")
-print(f"Target Model: AgriAssist_LLM")
-print(f"API Endpoint: {API_URL}")
+# --- Logging that doesn't expose real model name ---
+print(f"Application configured to use {PUBLIC_MODEL_NAME}.")
+print(f"API Endpoint configured.")
 
 # --- Inference Function (Using Hugging Face API) ---
-def generate_response(prompt, max_new_tokens=512): # Using max_tokens from your snippet
+def generate_response(prompt, max_new_tokens=512):
     print(f"Received prompt: {prompt}")
-    print("Preparing payload for API...")
+    print(f"Preparing payload for API...")
 
-    # Construct the payload based on the API requirements
-    # NOTE: This version assumes text-only input matching the Gradio interface.
-    #       To handle image input like your snippet, the Gradio interface
-    #       and payload structure would need modification.
     payload = {
         "messages": [
             {
                 "role": "user",
                 "content": prompt
-                # Example for multimodal if Gradio input changes:
-                # "content": [
-                #    {"type": "text", "text": prompt},
-                #    {"type": "image_url", "image_url": {"url": "some_image_url.jpg"}}
-                # ]
             }
         ],
-        "model": MODEL_ID,
+        "model": MODEL_ID,  # Real model used in API call
         "max_tokens": max_new_tokens,
-        # Optional parameters you might want to add:
-        # "temperature": 0.7,
-        # "top_p": 0.9,
-        # "stream": False # Set to True for streaming responses if API supports it
     }
 
-    print(f"Sending request to API for model AgriAssist_LLM...")
+    print(f"Sending request to API for {PUBLIC_MODEL_NAME}...")
     try:
-        # Make the POST request
         response = requests.post(API_URL, headers=HEADERS, json=payload)
-
-        # Raise an exception for bad status codes (like 4xx or 5xx)
         response.raise_for_status()
-
-        # Parse the JSON response
         result = response.json()
-        print("API Response Received Successfully.")
+        print(f"{PUBLIC_MODEL_NAME} Response Received Successfully.")
 
-        # Extract the generated text - Structure matches your snippet's expectation
         if "choices" in result and len(result["choices"]) > 0 and "message" in result["choices"][0] and "content" in result["choices"][0]["message"]:
             api_response_content = result["choices"][0]["message"]["content"]
-            print(f"API generated content: {api_response_content}")
+            print(f"{PUBLIC_MODEL_NAME} generated content")
             return api_response_content
         else:
-            # Handle unexpected response structure
-            print(f"Unexpected API response structure: {result}")
-            return f"Error: Unexpected API response structure. Full response: {json.dumps(result)}"
+            print(f"Unexpected API response structure")
+            return f"Error: {PUBLIC_MODEL_NAME} encountered an issue processing your request. Please try again."
 
     except requests.exceptions.RequestException as e:
-        # Handle network errors, timeout errors, invalid responses, etc.
-        error_message = f"Error calling Hugging Face API: {e}"
-        # Try to get more details from the response body if it exists
         error_detail = ""
         if e.response is not None:
             try:
-                error_detail = e.response.json() # Try parsing JSON error
+                error_detail = e.response.json()
             except json.JSONDecodeError:
-                error_detail = e.response.text # Fallback to raw text
-        print(f"{error_message}\nResponse details: {error_detail}")
-        return f"{error_message}\nDetails: {error_detail}"
+                error_detail = e.response.text
+        print(f"Error calling API: {e}")
+        return f"{PUBLIC_MODEL_NAME} is currently experiencing connectivity issues. Please try again later."
 
     except Exception as e:
-        # Handle other potential errors during processing
         print(f"An unexpected error occurred: {e}")
-        return f"An unexpected error occurred: {e}"
+        return f"{PUBLIC_MODEL_NAME} encountered an unexpected error. Please try again later."
 
 # --- Gradio Interface ---
 iface = gr.Interface(
     fn=generate_response,
     inputs=gr.Textbox(lines=5, label="Enter your prompt", placeholder="Type your question or instruction here..."),
-    outputs=gr.Textbox(lines=8, label=f"AgriAssist_LLM Says (via API):"), # Updated label
-    title=f"Chat with AgriAssist_LLM via Inference API", # Updated title
-    description=("This demo sends your text to a remote server for processing."), # Updated description
+    outputs=gr.Textbox(lines=8, label=f"{PUBLIC_MODEL_NAME} Response:"),
+    title=f"Chat with {PUBLIC_MODEL_NAME}",
+    description=(f"This demo connects you with {PUBLIC_MODEL_NAME}, a specialized agricultural assistant. "
+                "Submit your farming, crop management, or agricultural questions below."),
     allow_flagging="never",
-    examples=[ # Examples should still be relevant
-        ["Explain the concept of cloud computing in simple terms."],
-        ["Write Python code to list files in a directory."],
-        ["What are the main benefits of using Generative AI?"],
-        ["Translate 'Cloud computing offers scalability' to German."],
+    examples=[
+        ["What are sustainable practices for improving soil health in organic farming?"],
+        ["Explain crop rotation benefits and scheduling for small vegetable farms."],
+        ["How can I identify and treat common tomato plant diseases?"],
+        ["What irrigation methods are most water-efficient for drought-prone regions?"],
     ]
 )
 
 # --- Launch the App ---
-# You can add share=True if you want to create a temporary public link (use with caution)
-iface.launch(server_name="0.0.0.0", server_port=7860) # Makes it accessible in Codespaces/docker
+iface.launch(server_name="0.0.0.0", server_port=7860)