Update app.py

app.py

@@ -2,37 +2,66 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import PyPDF2
 import io
-from docx import Document  # Fix the import issue
+from docx import Document  # Make sure you have installed python-docx
 
+# Initialize the client for Hugging Face inference.
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-def extract_text_from_pdf(pdf_file):
-    pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file))
-    text = ""
-    for page in pdf_reader.pages:
-        text += page.extract_text() + "\n"
-    return text
+def extract_text_from_pdf(pdf_file_bytes):
+    """Extract text from a PDF file given as bytes."""
+    try:
+        pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file_bytes))
+        text = ""
+        for page in pdf_reader.pages:
+            page_text = page.extract_text()
+            if page_text:
+                text += page_text + "\n"
+        return text.strip() or "No text could be extracted from the PDF."
+    except Exception as e:
+        return f"Error reading PDF: {str(e)}"
 
-def extract_text_from_docx(docx_file):
-    doc = Document(io.BytesIO(docx_file))  # Fixed import usage
-    return "\n".join([para.text for para in doc.paragraphs])
+def extract_text_from_docx(docx_file_bytes):
+    """Extract text from a DOCX file given as bytes."""
+    try:
+        doc = Document(io.BytesIO(docx_file_bytes))
+        text = "\n".join(para.text for para in doc.paragraphs)
+        return text.strip() or "No text could be extracted from the DOCX file."
+    except Exception as e:
+        return f"Error reading DOCX: {str(e)}"
 
 def parse_cv(file, job_description):
+    """Analyze a CV (PDF or DOCX) against a job description and generate a report."""
     if file is None:
         return "Please upload a CV file."
-    
+
     file_ext = file.name.split(".")[-1].lower()
-    file_bytes = file.read()
-    
+    try:
+        file_bytes = file.read()
+    except Exception as e:
+        return f"Error reading the uploaded file: {str(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 reading"):
+        return text  # Return error from extraction 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: {str(e)}"
     
-    prompt = f"Analyze the following CV against the job description provided. Provide a summary, an assessment of fit, and a score from 0 to 10.\n\nJob Description:\n{job_description}\n\nCandidate CV:\n{text}"
-    response = client.text_generation(prompt, max_tokens=512)
     return response
 
 def respond(
@@ -43,32 +72,40 @@ def respond(
     temperature,
     top_p,
 ):
+    """
+    Chatbot response generator that interacts with a conversational model.
+    """
     messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    for user_msg, bot_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if bot_msg:
+            messages.append({"role": "assistant", "content": bot_msg})
 
     messages.append({"role": "user", "content": message})
     response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-        yield response
+    try:
+        for message_chunk in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = message_chunk.choices[0].delta.content
+            response += token
+            yield response
+    except Exception as e:
+        yield f"Error during chat generation: {str(e)}"
 
+# Build the Gradio interface
 demo = gr.Blocks()
 
 with demo:
     gr.Markdown("## AI-powered CV Analyzer and Chatbot")
+    
     with gr.Tab("Chatbot"):
         chat_interface = gr.ChatInterface(
             respond,
@@ -87,7 +124,9 @@ with demo:
         )
     
     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."
+        )
         file_input = gr.File(label="Upload CV", type="file")
         job_desc_input = gr.Textbox(label="Job Description", lines=5)
         output_text = gr.Textbox(label="CV Analysis Report", lines=10)
@@ -96,4 +135,4 @@ with demo:
         analyze_button.click(parse_cv, inputs=[file_input, job_desc_input], outputs=output_text)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()