Update app.py

app.py

@@ -1,8 +1,6 @@
 # --- Imports ---
-import spaces
 import gradio as gr
 from transformers import pipeline
-import os
 
 # --- Load Model ---
 pipe = pipeline(model="InstaDeepAI/ChatNT", trust_remote_code=True)
@@ -10,84 +8,112 @@ pipe = pipeline(model="InstaDeepAI/ChatNT", trust_remote_code=True)
 # --- Logs ---
 log_file = "logs.txt"
 
-class Log:
-    def __init__(self, log_file):
-        self.log_file = log_file
 
-    def __call__(self):
-        if not os.path.exists(self.log_file):
-            return ""
-        with open(self.log_file, "r") as f:
-            return f.read()
-
-# --- Main Function ---
-@spaces.GPU
-def run_chatnt(dna_text, fasta_file, custom_question):
+def log_message(message: str):
     with open(log_file, "a") as log:
-        log.write("Request started\n\n")
+        log.write(f"{message}\n")
+
 
-    # Read DNA sequence from text field or file
+# --- Utilities ---
+def read_dna_sequence(dna_text, fasta_file):
+    """
+    Returns:
+        dna_sequence: str
+        warning: str if any
+        error: str if any
+    """
     dna_sequence = ""
+    warning = ""
+    error = ""
+
+    # Pasted text
     if dna_text and dna_text.strip():
         dna_sequence = dna_text.strip().replace("\n", "")
-    elif fasta_file is not None:
-        with open(fasta_file.name, "r") as f:
-            file_content = f.read()
-        lines = file_content.splitlines()
-        sequence = ""
-        for line in lines:
-            line = line.strip()
-            if not line or line.startswith(">"):
-                continue
-            sequence += line
-        dna_sequence = sequence
-
-    dna_sequences = []
-    if dna_sequence:
-        dna_sequences.append(dna_sequence)
 
-    with open(log_file, "a") as log:
-        log.write(f"DNA sequences found: {dna_sequences}\n")
+    # Uploaded FASTA overrides text
+    if fasta_file is not None:
+        if dna_sequence:
+            warning = "Warning: Both pasted DNA and FASTA file provided. Using file only."
 
-    # Check DNA sequences count
-    if len(dna_sequences) > 1:
-        return "You must use only one DNA sequence."
+        try:
+            with open(fasta_file.name, "r") as f:
+                content = f.read()
 
-    if not custom_question or custom_question.strip() == "":
-        return "Please provide a question."
+            if not content.startswith(">"):
+                error = "Invalid FASTA: must start with '>' header line."
+                return "", warning, error
 
-    # Build prompt
-    num_placeholders = custom_question.count("<DNA>")
+            sequence = ""
+            for line in content.splitlines():
+                if not line or line.startswith(">"):
+                    continue
+                sequence += line.strip()
 
-    if len(dna_sequences) == 0:
-        english_sequence = custom_question
-    else:
-        if num_placeholders == 0:
-            return "Your question must include the <DNA> token at the position where the DNA sequence should be inserted."
-        elif num_placeholders == 1:
-            english_sequence = custom_question
-        else:
-            return "You can only provide one DNA sequence, so you must use exactly one <DNA> placeholder."
+            dna_sequence = sequence
 
-    with open(log_file, "a") as log:
-        log.write(f"Initial user question: {custom_question}\n")
-        log.write(f"Full english prompt: {english_sequence}\n")
-        log.write("Calling model\n")
-
-    output = pipe(
-        inputs={
-            "english_sequence": english_sequence,
-            "dna_sequences": dna_sequences
-        }
-    )
+        except Exception:
+            error = "Could not read the FASTA file."
 
-    if len(dna_sequences) == 0:
-        return f"{output}\n\nNote: Careful, you did not provide any DNA sequence."
+    if dna_sequence and not dna_sequence.isupper():
+        dna_sequence = dna_sequence.upper()
+        warning += "\nNote: DNA sequence was converted to uppercase."
 
-    with open(log_file, "a") as log:
-        log.write(f"Output: {output}\n")
+    return dna_sequence, warning.strip(), error
+
+
+def validate_inputs(dna_sequence, custom_question):
+    """
+    Returns:
+        valid: bool
+        error: str
+    """
+    placeholders = custom_question.count("<DNA>")
+
+    if not custom_question.strip():
+        return False, "Please provide a question."
+
+    if dna_sequence and placeholders == 0:
+        log_message("Error: DNA sequence provided but no <DNA> token.")
+        return False, "Your question must contain the <DNA> token if you provide a DNA sequence."
+
+    if not dna_sequence and placeholders == 1:
+        log_message("Error: <DNA> token but no sequence.")
+        return False, "You must provide a DNA sequence if you use the <DNA> token."
+
+    if placeholders > 1:
+        return False, "Only one <DNA> token is allowed."
+
+    return True, ""
+
+
+# --- Main Inference ---
+def run_chatnt(dna_text, fasta_file, custom_question):
+    feedback_msgs = []
+
+    dna_sequence, warning, fasta_error = read_dna_sequence(dna_text, fasta_file)
+
+    if fasta_error:
+        return "", fasta_error
+
+    is_valid, validation_error = validate_inputs(dna_sequence, custom_question)
+    if not is_valid:
+        return "", validation_error
+
+    final_prompt = custom_question
+
+    inputs = {
+        "english_sequence": final_prompt,
+        "dna_sequences": [dna_sequence] if dna_sequence else []
+    }
+
+    output = pipe(inputs=inputs)
+    result = output
+
+    if warning:
+        feedback_msgs.append(warning)
+
+    return result, "\n".join(feedback_msgs)
 
-    return output
 
 # --- Gradio Interface ---
 css = """
@@ -96,50 +122,104 @@ css = """
 footer { display: none !important; }
 """
 
+example_dna = "ATGCATGCATGCATGC"
+example_question = "Does this sequence <DNA> contain a donor splice site?"
+
 with gr.Blocks(css=css) as demo:
     gr.Markdown("# 🧬 ChatNT: A Multimodal Conversational Agent for DNA, RNA and Protein Tasks")
+    gr.Markdown(
+        "[ChatNT]{https://www.nature.com/articles/s42256-025-01047-1} is the first multimodal conversational agent designed with a deep understanding of biological sequences (DNA, RNA, proteins). It enables users — even those with no coding background — to interact with biological data through natural language and it generalizes across multiple biological tasks and modalities.\n"
+        "This Hugging Face Space is powered by a [ZeroGPU](https://huggingface.co/docs/hub/en/spaces-zerogpu), which is free but **limited to 5 minutes per day per user**.\n"
+    )
+
+    gr.Image("https://media.springernature.com/w440/springer-static/cover-hires/journal/42256/7/6")
 
     with gr.Row():
         with gr.Column(scale=1):
             dna_text = gr.Textbox(
                 label="Paste your DNA sequence",
-                placeholder="ATGCATGCATGC...",
+                placeholder="ATGCATGC...",
                 lines=4
             )
 
             fasta_file = gr.File(
                 label="Or upload your FASTA file",
-                file_types=[".fasta", ".fa", ".txt"]
+                file_types=[".fasta", ".fa", ".txt"],
+                height=50
             )
 
             custom_question = gr.Textbox(
                 label="English Question",
-                placeholder="e.g., Does this sequence <DNA> contain a donor splice site?"
+                placeholder="Does this sequence <DNA> contain a donor splice site?"
             )
 
+            use_example = gr.Button("Use Example")
             submit_btn = gr.Button("Run Query", variant="primary")
 
-        with gr.Row():
-            output = gr.Textbox(label="Answer", lines=6)
+        with gr.Column(scale=1):
+            output = gr.Textbox(
+                label="Model Answer",
+                lines=12
+            )
+            error_box = gr.Textbox(
+                label="Execution Feedback",
+                lines=4
+            )
 
     submit_btn.click(
         run_chatnt,
         inputs=[dna_text, fasta_file, custom_question],
-        outputs=output,
+        outputs=[output, error_box],
     )
 
-    gr.Markdown("""
-**Note:**  
-✅ You must use **exactly one DNA sequence** (either paste it or upload a file).  
-✅ Your question must include the `<DNA>` token **exactly once** at the position where the DNA will be inserted.  
-Example: *"Does this sequence `<DNA>` contain a donor splice site?"*
-    """)
+    use_example.click(
+        lambda: (example_dna, None, example_question),
+        inputs=[],
+        outputs=[dna_text, fasta_file, custom_question]
+    )
+
+    gr.Markdown(
+        """
+You must use **exactly one `<DNA>` token** if you want the model to see your sequence. It is also possible to use the model without any DNA sequence (in this case, the `<DNA>` token must not be present in the question).
+You can either paste a sequence or upload a FASTA file.  
+
+---
+
+### ✅ Good queries
+- "Does this sequence `<DNA>` contain a donor splice site?"
+- "Is it possible for you to identify whether there's a substantial presence of H3 histone protein occupancy in the nucleotide sequence `<DNA>` in yeast?"
+- "Determine the degradation rate of the mouse RNA sequence `<DNA>` within the -5 to 5 range."
 
-    with gr.Accordion("Logs", open=True):
-        log_display = Log(log_file)
-        gr.Markdown(log_display)
+### ❌ What will not work properly
+- "What is the length of this sequence `<DNA>`?"
+
+For more examples, you can refer to the [training dataset]{https://huggingface.co/datasets/InstaDeepAI/ChatNT_training_data}.
+        """
+    )
+
+    gr.Markdown("""
+### 📚 Citation
+
+If you use **ChatNT**, please cite:  
+
+```bibtex
+@article{deAlmeida2025,
+  title  = {A multimodal conversational agent for DNA, RNA and protein tasks},
+  author = {de Almeida, Bernardo P. and Richard, Guillaume and Dalla-Torre, Hugo and Blum, Christopher and Hexemer, Lorenz and Pandey, Priyanka and Laurent, Stefan and Rajesh, Chandana and Lopez, Marie and Laterre, Alexandre and Lang, Maren and Şahin, Uğur and Beguir, Karim and Pierrot, Thomas},
+  journal = {Nature Machine Intelligence},
+  year   = {2025},
+  volume = {7},
+  number = {6},
+  pages  = {928--941},
+  doi    = {10.1038/s42256-025-01047-1},
+  url    = {https://doi.org/10.1038/s42256-025-01047-1},
+  issn   = {2522-5839}
+}
+```
+""",
+        show_copy_button=True
+    )
 
-# --- Launch ---
 if __name__ == "__main__":
     demo.queue()
     demo.launch(debug=True, show_error=True)