Update chatbot.py

chatbot.py

@@ -9,6 +9,25 @@ from ayush_messages import ayush_surprises
 load_dotenv()
 HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
 
+# List of open-source models for fallback responses
+AI_MODELS = [
+    {
+        "name": "google/gemma-2b",
+        "endpoint": "https://api-inference.huggingface.co/models/google/gemma-2b",
+        "parameters": {"max_length": 50, "temperature": 0.7}
+    },
+    {
+        "name": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "endpoint": "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "parameters": {"max_length": 50, "temperature": 0.7}
+    },
+    {
+        "name": "facebook/blenderbot-400M-distill",
+        "endpoint": "https://api-inference.huggingface.co/models/facebook/blenderbot-400M-distill",
+        "parameters": {"max_length": 50, "temperature": 0.7}
+    }
+]
+
 # Simple context tracking (e.g., last topic discussed)
 conversation_context = {
     "last_topic": None,  # Store the last keyword matched (e.g., "birthday", "riddle")
@@ -21,7 +40,7 @@ def get_krishna_response(user_input):
     - Match user input to predefined messages in krishna_blessings or ayush_surprises using keywords.
     - Use context to provide more coherent responses.
     - Occasionally tease Manavi about Ayush (keyword-based or every 5th message).
-    - Fall back to Hugging Face's Inference API for unmatched inputs.
+    - Fall back to multiple open-source AI models for unmatched inputs.
     """
     user_input_lower = user_input.lower().strip()
 
@@ -60,7 +79,7 @@ def get_krishna_response(user_input):
         conversation_context["last_topic"] = "ayush"
         return random.choice(ayush_teasing["ayush"])
 
-    # New trigger for "chat with you"
+    # Trigger for "chat with you"
     if "chat with you" in user_input_lower or "want to chat" in user_input_lower:
         conversation_context["last_topic"] = "chat_with_you"
         return krishna_blessings["chat_with_you"]
@@ -319,30 +338,45 @@ def get_krishna_response(user_input):
         if last_topic in krishna_blessings:
             return krishna_blessings[last_topic] + " What else would you like to talk about, Manavi?"
 
-    # Fallback to Hugging Face Inference API if no keywords match
+    # Fallback to multiple open-source AI models if no keywords match
+    # Shuffle the models to try them in random order
+    models_to_try = AI_MODELS.copy()
+    random.shuffle(models_to_try)
+
     headers = {
         "Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}",
         "Content-Type": "application/json"
     }
-    payload = {
-        "inputs": f"You are Little Krishna, playful and wise. Respond to this in a fun, Krishna-like way: '{user_input}'",
-        "parameters": {
-            "max_length": 50,
-            "temperature": 0.7
-        }
-    }
-    try:
-        response = requests.post(
-            "https://api-inference.huggingface.co/models/google/gemma-2b",
-            headers=headers,
-            json=payload
-        )
-        if response.status_code == 200:
-            result = response.json()
-            return result[0]["generated_text"].strip()
-        else:
-            print(f"Error with Gemma: {response.text}")
-            return "Hare Manavi! My flute got a bit shy—let’s try something else, shall we?"
-    except Exception as e:
-        print(f"Error connecting to Hugging Face Inference API: {str(e)}")
-        return "Hare Manavi! I seem to be lost in Vrindavan’s magic—let’s try a different tune!"
+
+    for model in models_to_try:
+        try:
+            payload = {
+                "inputs": f"You are Little Krishna, playful and wise. Respond to this in a fun, Krishna-like way: '{user_input}'",
+                "parameters": model["parameters"]
+            }
+            response = requests.post(
+                model["endpoint"],
+                headers=headers,
+                json=payload
+            )
+            if response.status_code == 200:
+                result = response.json()
+                # Handle different response formats based on the model
+                if isinstance(result, list) and len(result) > 0 and "generated_text" in result[0]:
+                    return result[0]["generated_text"].strip()
+                elif isinstance(result, dict) and "generated_text" in result:
+                    return result["generated_text"].strip()
+                elif isinstance(result, str):
+                    return result.strip()
+                else:
+                    print(f"Unexpected response format from {model['name']}: {result}")
+                    continue
+            else:
+                print(f"Error with {model['name']}: {response.text}")
+                continue
+        except Exception as e:
+            print(f"Error connecting to {model['name']}: {str(e)}")
+            continue
+
+    # If all models fail, return a default message
+    return "Hare Manavi! I seem to be lost in Vrindavan’s magic—let’s try a different tune!"