updated score model

Agent.py

@@ -217,7 +217,7 @@ def generate_environmental_score(state: EnvironmentalAnalysisState):
     6. End-of-life disposal
     
     Provide:
-    - Overall score (0-100, where 100 is most sustainable)
+    - Overall score (0-100, where 100 is most sustainable and 0 is least sustainable)
     - Category breakdown
     - Improvement recommendations
     """)
@@ -235,7 +235,7 @@ def generate_environmental_score(state: EnvironmentalAnalysisState):
     ]
     
     result = llm.chat.completions.create(
-        model="deepseek-ai/DeepSeek-R1",
+        model="meta-llama/Meta-Llama-3.1-70B-Instruct",
         messages=messages,
         max_tokens=1500,
         temperature=0.2

test.py

@@ -0,0 +1,96 @@
+import os
+from dotenv import load_dotenv
+from huggingface_hub import InferenceClient
+from langchain.prompts import ChatPromptTemplate
+import time
+from huggingface_hub import InferenceClient
+from huggingface_hub.errors import HfHubHTTPError # Corrected import path
+
+# --- Setup for environment variables and client ---
+# 1. Create a .env file in the same directory as this script.
+# 2. Add your Hugging Face Access Token to it:
+#    HF_TOKEN="hf_YOUR_ACTUAL_HUGGING_FACE_TOKEN"
+#    (The 'hf_' prefix is important for Hugging Face tokens)
+load_dotenv()
+
+# Initialize llm with your standard Hugging Face Token
+# The InferenceClient automatically looks for HF_TOKEN if not explicitly provided
+try:
+    hf_token = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJra3VuYWxnZ3VwdGEyMDBAZ21haWwuY29tIiwiaWF0IjoxNzQ5MDI5MzE2fQ.FyaF9EEw5MlkVNjq3SxjfzIiFqGCm8Z-glIqGEuL8ac"
+    if not hf_token:
+        print("Warning: HF_TOKEN not found in .env file or environment variables.")
+        print("The InferenceClient might work for public models, but private models or higher rate limits may require a token.")
+        # If no token is found, try to proceed without it (might work for public models)
+        llm = InferenceClient()
+    else:
+        llm = InferenceClient(token=hf_token) # Explicitly pass the token if you want to be sure
+    
+except Exception as e:
+    print(f"Error initializing InferenceClient: {e}")
+    print("Please ensure your .env file has HF_TOKEN set correctly.")
+    exit()
+
+# --- Sample Prompt and API Call ---
+def test_llm_timeout():
+    # Use a simple prompt for testing
+    test_prompt = ChatPromptTemplate.from_template(
+        "Explain the concept of neural networks in a simple way."
+    )
+    rendered_prompt_content = test_prompt.format()
+
+    messages = [
+        {
+            "role": "user",
+            "content": rendered_prompt_content
+        }
+    ]
+
+    # The model you specified in your Agent.py
+    model_name = "deepseek-ai/DeepSeek-R1" 
+    max_retries = 3
+    
+    print(f"Attempting to call model: {model_name}")
+    print(f"Prompt: '{rendered_prompt_content[:50]}...'")
+
+    for attempt in range(max_retries):
+        print(f"\n--- Attempt {attempt + 1}/{max_retries} ---")
+        try:
+            # Make the API call
+            result = llm.chat.completions.create(
+                model=model_name,
+                messages=messages,
+                max_tokens=200, # Keep max_tokens reasonable for testing
+                temperature=0.7,
+                # Explicitly specify the router if you know it, though usually not needed with token
+                # router="https://router.huggingface.co/hyperbolic/v1/" # This might be the actual endpoint you need
+            )
+            # If successful, print the result and break
+            print("API call successful!")
+            print("Response:", result.choices[0].message.content)
+            return
+
+        except HfHubHTTPError as e:
+            if e.response.status_code == 504:
+                print(f"Caught 504 Gateway Time-out on attempt {attempt + 1}.")
+                if attempt < max_retries - 1:
+                    wait_time = 2 ** (attempt + 1) # Exponential backoff: 2, 4 seconds
+                    print(f"Retrying in {wait_time} seconds...")
+                    time.sleep(wait_time)
+                else:
+                    print(f"Max retries ({max_retries}) reached. Still encountering 504.")
+                    print("This indicates a persistent issue with the API or model availability.")
+                    print(f"Full error: {e}")
+                    return
+            else:
+                # Re-raise other HTTP errors
+                print(f"Caught unexpected HTTP error: {e.response.status_code} - {e.response.reason}")
+                print(f"Full error response: {e.response.text}")
+                raise
+        except Exception as e:
+            # Catch any other unexpected errors
+            print(f"An unexpected error occurred: {e}")
+            return
+
+# Run the test
+if __name__ == "__main__":
+    test_llm_timeout()