Update app.py

app.py

@@ -480,7 +480,7 @@ def display_results(data):
         st.warning("No data to display.")
         return None
 # Add the generate_literature_survey function below your other function definitions
-def generate_literature_survey(papers, api_key="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"):
+def generate_literature_survey(papers, api_key="YOUR_API_KEY"):
     """
     Generate a literature survey based on paper abstracts using Groq API with Llama-3.3-70B-Versatile
     
@@ -491,16 +491,19 @@ def generate_literature_survey(papers, api_key="gsk_G80LBPxmvDjQZ77zX0FIWGdyb3FY
     Returns:
     str: Generated literature survey
     """
+    # Check if we have papers with abstracts
     if not papers or len(papers) == 0:
         return "No papers found to generate a literature survey."
     
+    # Filter papers that have abstracts
     papers_with_abstracts = [p for p in papers if p.get("Abstract") and len(p.get("Abstract")) > 50]
     
     if len(papers_with_abstracts) == 0:
         return "Cannot generate a literature survey because none of the papers have substantial abstracts."
     
+    # Construct the prompt for the LLM
     paper_info = []
-    for i, paper in enumerate(papers_with_abstracts[:10]):
+    for i, paper in enumerate(papers_with_abstracts[:10]):  # Limit to 10 papers to avoid token limits
         paper_info.append(f"Paper {i+1}:\nTitle: {paper.get('Title', 'Unknown')}\nAuthors: {paper.get('Author(s)', 'Unknown')}\nYear: {paper.get('Published', 'Unknown')}\nAbstract: {paper.get('Abstract', 'No abstract available')}\n")
     
     papers_text = "\n".join(paper_info)
@@ -518,14 +521,15 @@ Please organize the survey by themes rather than by individual papers, creating
 Format your response with markdown headings for better readability.
 """
 
-    url = "https://api.groq.com/v1/chat/completions"  # Updated endpoint
+    # Make the API request to Groq
+    url = "https://api.groq.com/openai/v1/completions"  # Updated endpoint
     headers = {
         "Authorization": f"Bearer {api_key}",
         "Content-Type": "application/json"
     }
     
     data = {
-        "model": "llama-70b",  # Adjust this based on Groq's available models
+        "model": "llama-3.3-70b-versatile",  
         "messages": [
             {"role": "system", "content": "You are an academic research assistant that creates comprehensive literature surveys."},
             {"role": "user", "content": prompt}
@@ -536,11 +540,17 @@ Format your response with markdown headings for better readability.
     
     try:
         response = requests.post(url, headers=headers, data=json.dumps(data))
-        response.raise_for_status()
+        print(f"Response Status Code: {response.status_code}")  # Log status code
+        print(f"Response Body: {response.text}")  # Log full response body
+        response.raise_for_status()  # Raise an exception for HTTP errors
         result = response.json()
         survey_text = result["choices"][0]["message"]["content"]
         return survey_text
+    except requests.exceptions.HTTPError as e:
+        print(f"HTTP Error: {e}")
+        return f"Failed to generate literature survey due to an HTTP error: {str(e)}"
     except Exception as e:
+        print(f"Unexpected Error: {e}")
         return f"Failed to generate literature survey due to an error: {str(e)}"
 
 # Example usage