Update app.py

app.py

@@ -70,7 +70,7 @@ def system_prompt_to_extract_info(resume_text):
     """
     return prompt
 
-# Function to extract candidate information from resume text using Gemini API
+# Function to extract candidate information from resume text
 def extract_entities_via_gemini(resume_text):
     api_key = get_google_api_key()  # Fetch the API key from environment variables
     endpoint = "https://gemini.googleapis.com/v1/documents:analyzeEntities"  # Placeholder API endpoint (adjust as necessary)
@@ -86,50 +86,27 @@ def extract_entities_via_gemini(resume_text):
             "content": resume_text
         }
     }
-
+    
     # Send request to Gemini or another NLP API
     response = requests.post(endpoint, headers=headers, json=document)
 
-    # Debugging: Log raw response
-    print(f"Response Status Code: {response.status_code}")
-    print(f"Response Content: {response.text}")  # Log the raw response content
-
     if response.status_code != 200:
         return {"error": "Failed to extract entities from resume", "status_code": response.status_code, "response": response.text}
-
-    # Use the raw text response (instead of parsing as JSON)
-    response_text = response.text
-
-    # You can now use `response_text` directly as you wish. For example, if you're extracting specific information:
+    
+    # Process the response from the Gemini API (or similar NLP API)
+    entities = response.json().get('entities', [])
     extracted_info = {"name": "Unknown Candidate", "email": "No Email", "contact": "No Contact"}
 
-    # Check for keywords in the response text to extract candidate info
-    if "name" in response_text:
-        extracted_info['name'] = extract_info_from_text(response_text, 'name')
-    if "email" in response_text:
-        extracted_info['email'] = extract_info_from_text(response_text, 'email')
-    if "contact" in response_text:
-        extracted_info['contact'] = extract_info_from_text(response_text, 'contact')
-
+    for entity in entities:
+        if entity['type'] == 'PERSON':
+            extracted_info['name'] = entity['name']
+        if entity['type'] == 'EMAIL':
+            extracted_info['email'] = entity['name']
+        if entity['type'] == 'PHONE_NUMBER':
+            extracted_info['contact'] = entity['name']
+    
     return extracted_info
 
-# Helper function to extract specific information from raw response text
-def extract_info_from_text(response_text, info_type):
-    # Define simple patterns to match relevant information (you can improve this regex as needed)
-    if info_type == 'name':
-        match = re.search(r"Name: (\S+ \S+)", response_text)
-        if match:
-            return match.group(1)
-    elif info_type == 'email':
-        match = re.search(r"Email: (\S+@\S+)", response_text)
-        if match:
-            return match.group(1)
-    elif info_type == 'contact':
-        match = re.search(r"Contact: (\S+)", response_text)
-        if match:
-            return match.group(1)
-    return f"No {info_type}"
-
 # Function to check similarity between resumes and job description
 def check_similarity(job_description, resume_files):
     results = []
@@ -148,7 +125,7 @@ def check_similarity(job_description, resume_files):
         # Convert similarity score to percentage
         similarity_percentage = similarity_score * 100
 
-        # Extract leadership experience (make sure this function is implemented)
+        # Extract leadership experience
         leadership_experience = extract_leadership_experience(resume_text)
 
         # Extract name, email, and contact info using Google Gemini API
@@ -204,11 +181,10 @@ def download_results(results):
 interface = gr.Interface(
     fn=check_similarity,
     inputs=[job_desc_input, resumes_input],
-    outputs=[results_output, gr.File(label="Download CSV", file=download_results)],  # Updated here
+    outputs=[results_output, gr.File(label="Download CSV", file_output=download_results)],  # Updated here
     title="HR Assistant - Resume Screening & Leadership Experience",
     description="Upload job description and resumes to screen candidates for managerial and team leadership roles and extract candidate details.",
     allow_flagging="never"
 )
 
 interface.launch()
-