Update app.py

app.py

@@ -26,15 +26,16 @@ MODEL = "meta-llama/Llama-3.3-70B-Instruct"
 # SHARED: TOKEN / MODEL / TTS
 # =========================
 
-def call_llama(prompt, max_tokens=1200):
+def call_llama(prompt, user_token=None, max_tokens=1200):
     """
-    Pulls Llama directly via Hugging Face using only HF_TOKEN.
-    No provider routing. No OpenAI/Anthropic/Google keys.
+    Pulls Llama directly via Hugging Face using HF_TOKEN.
+    Prioritizes user_token from UI if provided, otherwise looks for HF_TOKEN env var.
     """
-    token = os.environ.get("HF_TOKEN", "").strip()
+    # Use user provided token first, then fallback to env var
+    token = (user_token or os.environ.get("HF_TOKEN", "")).strip()
 
     if not token:
-        return "Model error: HF_TOKEN missing. Add your Hugging Face token as a Space secret named HF_TOKEN."
+        return "Model error: HF_TOKEN missing. Add your Hugging Face token as a Space secret named HF_TOKEN or enter it in the input field."
 
     if not token.startswith("hf_"):
         return "Model error: HF_TOKEN exists but does not look like a Hugging Face token. It should start with hf_."
@@ -125,7 +126,7 @@ def extract_pdf_text(path):
         return f"[PDF read error: {e}]"
 
 
-def phoenix_analyse(pdf_file):
+def phoenix_analyse(pdf_file, hf_token_input):
     if not pdf_file:
         return "Please upload a PDF.", "", "", ""
 
@@ -140,14 +141,16 @@ def phoenix_analyse(pdf_file):
         base
         + "You are Introbot — part of the Metabolic Phoenix Framework by Dwayne Anthony Brian Galloway.\n\n"
         + "From this document extract every key fact, who said it and when; every admission; every denial; every contradiction; every gap; and any statutory duties triggered.\n\n"
-        + "Format as a clean numbered FACT LEDGER."
+        + "Format as a clean numbered FACT LEDGER.",
+        user_token=hf_token_input
     )
 
     r2 = call_llama(
         base
         + "You are RHB 2.0 + CPF — part of the Metabolic Phoenix Framework by Dwayne Anthony Brian Galloway.\n\n"
         + "Map every institution to every statutory duty. Mark COMPLIANT or NON-COMPLIANT with evidence. Identify legal errors, causal chain, Equality Act 2010 issues especially s.13 and s.20, and remedies.\n\n"
-        + "Be forensic. Cite statute sections where possible."
+        + "Be forensic. Cite statute sections where possible.",
+        user_token=hf_token_input
     )
 
     r3 = call_llama(
@@ -158,7 +161,8 @@ def phoenix_analyse(pdf_file):
         + "RING B — BACKWARD SWEEP: decisions that would be decided differently under current law.\n\n"
         + "RING C — TYPOLOGY MUTATION: patterns of institutional misconduct.\n\n"
         + "RING D — DRIFT CORRECTION: internal contradictions and inconsistencies.\n\n"
-        + "End with ESCALATION MAP."
+        + "End with ESCALATION MAP.",
+        user_token=hf_token_input
     )
 
     r4 = call_llama(
@@ -174,7 +178,8 @@ def phoenix_analyse(pdf_file):
         "TOP 5 ARGUMENTS — strongest first\n"
         "RECOMMENDED NEXT STEPS — in priority order\n"
         "ESCALATION MAP — which body gets what and when\n\n"
-        "Ms. Nala | Injectiv8 | Dwayne Anthony Brian Galloway"
+        "Ms. Nala | Injectiv8 | Dwayne Anthony Brian Galloway",
+        user_token=hf_token_input
     )
 
     return r1, r2, r3, r4
@@ -502,9 +507,9 @@ class DocumentMerger:
         return "\n".join(output)
 
 
-def ai_summarise(merged_doc: str, analysis: Dict) -> str:
+def ai_summarise(merged_doc: str, analysis: Dict, hf_token_input=None) -> str:
     try:
-        hf_token = os.environ.get("HF_TOKEN")
+        hf_token = (hf_token_input or os.environ.get("HF_TOKEN", "")).strip()
         risk_summary = {}
         for risk in analysis.get("risk_language", []):
             risk_summary[risk["type"]] = risk_summary.get(risk["type"], 0) + 1
@@ -688,7 +693,7 @@ def export_docx(merged_doc: str, analysis_report: str, summary: str) -> str:
         return None
 
 
-def process_documents(file1, file2, file3):
+def process_documents(file1, file2, file3, hf_token_input):
     try:
         raw_inputs = [file1, file2, file3]
         files = []
@@ -752,7 +757,7 @@ def process_documents(file1, file2, file3):
         else:
             analysis_report += "No risk language patterns detected.\n"
 
-        summary = ai_summarise(merged_doc, analysis)
+        summary = ai_summarise(merged_doc, analysis, hf_token_input=hf_token_input)
 
         txt_path = export_txt(merged_doc, analysis_report)
         pdf_path = export_pdf(merged_doc, analysis_report)
@@ -867,6 +872,15 @@ with gr.Blocks(css=CSS, title="The Metamorphic Phoenix") as demo:
         """
     )
 
+    with gr.Row():
+        with gr.Column(scale=1):
+            hf_token_input = gr.Textbox(
+                label="Hugging Face Token (HF_TOKEN)", 
+                placeholder="hf_...", 
+                type="password",
+                info="Pulled from Space Secrets if empty."
+            )
+
     with gr.Tabs():
         with gr.TabItem("Metabolic Phoenix"):
             with gr.Row():
@@ -919,7 +933,7 @@ with gr.Blocks(css=CSS, title="The Metamorphic Phoenix") as demo:
                 lib_dl_file = gr.File(label="Your file", visible=False)
                 lib_msg = gr.Markdown("")
 
-            run_btn.click(phoenix_analyse, inputs=[pdf_input], outputs=[out1, out2, out3, out4])
+            run_btn.click(phoenix_analyse, inputs=[pdf_input, hf_token_input], outputs=[out1, out2, out3, out4])
 
             top_tts_btn.click(
                 lambda a, b, c, d: make_tts(
@@ -981,7 +995,7 @@ with gr.Blocks(css=CSS, title="The Metamorphic Phoenix") as demo:
 
             analyze_btn.click(
                 fn=process_documents,
-                inputs=[file1, file2, file3],
+                inputs=[file1, file2, file3, hf_token_input],
                 outputs=[merged_output, analysis_output, summary_output, txt_file, pdf_file, docx_file],
             )