Update app.py

app.py

@@ -3,21 +3,17 @@ import requests
 from bs4 import BeautifulSoup
 
 api_token = os.environ.get("TOKEN")
-API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
 headers = {"Authorization": f"Bearer {api_token}"}
 
 def query(payload):
-    try:
-        response = requests.post(API_URL, headers=headers, json=payload)
-        response.raise_for_status()  # Raise an exception for bad status codes
-        return response.json()
-    except requests.exceptions.RequestException as e:
-        print(f"Error making request to API: {e}")
-        return None
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
 
 def analyze_sentiment(pl7_text):
-    prompt = f'''<|begin_of_text|>
-<|start_header_id|>system<|end_header_id|>
+    output = query({
+        "inputs": f'''
+system
 You're going to deeply analyze the text I'm going to give you and you're only going to tell me which category it belongs to by answering only the words that correspond to the following categories:
 For posts that talk about chat models/LLM, return "Chatmodel/LLM"
 For posts that talk about image generation models, return "image_generation"
@@ -29,27 +25,25 @@ For posts about tools and libraries, return "tools_libraries"
 For posts containing tutorials and guides, return "tutorials_guides"
 For posts about debugging and problem-solving, return "debugging"
 Respond only with the category name, without any additional explanation or text.
-<|eot_id|>
-<|start_header_id|>user<|end_header_id|>
+
+user
 {pl7_text}
-<|eot_id|>
-<|start_header_id|>assistant<|end_header_id|>
+
+assistant
 '''
+    })
     
-    print("Sending request to API...")
-    output = query({"inputs": prompt})
-    
-    if output is None:
-        return "Error: Failed to get response from API"
-    
-    print("Raw API response:")
-    print(output)
-    
-    if isinstance(output, list) and len(output) > 0:
-        generated_text = output[0].get('generated_text', '')
-        return generated_text.strip()
-    else:
-        return "Error: Unexpected response format from API"
+    print("API Response:", output)  # Print the full API response
+
+    # Extract the generated text
+    generated_text = output.get('generated_text', '')
+    print("Generated Text:", generated_text)  # Print the generated text
+
+    # Extract the first non-empty line as the category
+    lines = [line.strip().lower() for line in generated_text.split('\n') if line.strip()]
+    if lines:
+        return lines[0]
+    return "unknown"
 
 # Fetch a single post
 url = 'https://huggingface.co/posts'
@@ -60,12 +54,12 @@ if response.status_code == 200:
     pl7_element = soup.find(class_='pl-7')
     if pl7_element:
         pl7_text = pl7_element.text.strip()
-        print("Post content (first 100 characters):")
-        print(pl7_text[:100] + "..." if len(pl7_text) > 100 else pl7_text)
-        print("\nAnalyzing post...")
-        llm_response = analyze_sentiment(pl7_text)
-        print(f"\nLLM Response:\n{llm_response}")
+        print("Post content:")
+        print(pl7_text)
+        print("\nAnalyzing sentiment...")
+        sentiment = analyze_sentiment(pl7_text)
+        print(f"\nSentiment category: {sentiment}")
     else:
         print("No post found with class 'pl-7'")
 else:
-    print(f"Error {response.status_code} when retrieving {url}")
+    print(f"Error {response.status_code} when retrieving {url}")