Update app.py

app.py

@@ -2,11 +2,12 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import PyPDF2
 import io
-from docx import Document  # Ensure you have installed "python-docx" (not "docx")
+from docx import Document
 
 # Initialize the inference client from Hugging Face.
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+
 def extract_text_from_pdf(pdf_file_bytes):
     """Extract text from PDF bytes."""
     try:
@@ -20,6 +21,7 @@ def extract_text_from_pdf(pdf_file_bytes):
     except Exception as e:
         return f"Error reading PDF: {e}"
 
+
 def extract_text_from_docx(docx_file_bytes):
     """Extract text from DOCX bytes."""
     try:
@@ -29,41 +31,53 @@ def extract_text_from_docx(docx_file_bytes):
     except Exception as e:
         return f"Error reading DOCX: {e}"
 
+
 def parse_cv(file, job_description):
     """Analyze the CV (PDF or DOCX) against the job description and return an analysis report."""
     if file is None:
         return "Please upload a CV file."
-    
-    file_ext = file.name.split(".")[-1].lower()
+
+    # Correctly handle the file object when type="binary"
     try:
-        file_bytes = file.read()
+        file_bytes = file
+        file_ext = "pdf"  # Assume PDF if we can't determine from name
+        if file_bytes:
+          # Heuristic to detect file type based on content
+          if file_bytes.startswith(b'%PDF'):
+              file_ext = "pdf"
+          elif file_bytes.startswith(b'PK\x03\x04'):  #DOCX magic number
+              file_ext = "docx"
+          else:
+              return "Unsupported file format. Cannot determine type from content"
+
     except Exception as e:
         return f"Error reading the uploaded file: {e}"
-    
+
     if file_ext == "pdf":
         text = extract_text_from_pdf(file_bytes)
     elif file_ext == "docx":
         text = extract_text_from_docx(file_bytes)
     else:
         return "Unsupported file format. Please upload a PDF or DOCX file."
-    
+
     if text.startswith("Error"):
         return text  # Return extraction error if any.
-    
+
     prompt = (
         f"Analyze the following CV against the provided job description. "
         f"Provide a summary, an assessment of fit, and a score from 0 to 10.\n\n"
         f"Job Description:\n{job_description}\n\n"
         f"Candidate CV:\n{text}"
     )
-    
+
     try:
         response = client.text_generation(prompt, max_tokens=512)
     except Exception as e:
         return f"Error during CV analysis: {e}"
-    
+
     return response
 
+
 def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
     """Generate a chatbot response based on the conversation history and parameters."""
     messages = [{"role": "system", "content": system_message}]
@@ -73,7 +87,7 @@ def respond(message, history: list[tuple[str, str]], system_message, max_tokens,
         if bot_msg:
             messages.append({"role": "assistant", "content": bot_msg})
     messages.append({"role": "user", "content": message})
-    
+
     response = ""
     try:
         # Stream response tokens from the chat completion endpoint.
@@ -90,16 +104,17 @@ def respond(message, history: list[tuple[str, str]], system_message, max_tokens,
     except Exception as e:
         yield f"Error during chat generation: {e}"
 
+
 # Build the Gradio interface
 demo = gr.Blocks()
-
 with demo:
     gr.Markdown("## AI-powered CV Analyzer and Chatbot")
-    
+
     with gr.Tab("Chatbot"):
         # Set type="messages" to use the OpenAI-style message format.
         chat_interface = gr.ChatInterface(
             respond,
+            chatbot=gr.Chatbot(value=[], label="Chatbot"),
             type="messages",
             additional_inputs=[
                 gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
@@ -108,16 +123,18 @@ with demo:
                 gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
             ],
         )
-    
+
     with gr.Tab("CV Analyzer"):
-        gr.Markdown("### Upload your CV (PDF or DOCX) and provide the job description to receive a professional analysis and suitability score.")
+        gr.Markdown(
+            "### Upload your CV (PDF or DOCX) and provide the job description to receive a professional analysis and suitability score."
+        )
         # Use type="binary" for the file component.
-        file_input = gr.File(label="Upload CV", type="binary")
+        file_input = gr.File(label="Upload CV", type="bytes")
         job_desc_input = gr.Textbox(label="Job Description", lines=5)
         output_text = gr.Textbox(label="CV Analysis Report", lines=10)
         analyze_button = gr.Button("Analyze CV")
-        
+
         analyze_button.click(parse_cv, inputs=[file_input, job_desc_input], outputs=output_text)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue().launch()