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| import gradio as gr | |
| import requests | |
| from transformers import pipeline | |
| # ========================================================== | |
| # MODEL: flan-t5-base (बेहतर जवाब!) | |
| # ========================================================== | |
| MODEL_ID = "google/flan-t5-small" # ← t5-small पर परीक्षण के लिए | |
| # MODEL_ID = "google/flan-t5-base" # ← जब समस्या हल हो जाए तो इसे वापस स्विच कर सकते हैं | |
| print(f"Loading {MODEL_ID}...") | |
| generator = pipeline("text2text-generation", model=MODEL_ID, device=-1) | |
| # ========================================================== | |
| # PROMPT | |
| # ========================================================== | |
| def get_prompt(user_input): | |
| text = user_input.strip() | |
| if not text: | |
| return "Question: कोई प्रश्न नहीं।\nAnswer:" | |
| text = text.replace(" या ", " है या नहीं? ") | |
| if not text.endswith("?"): | |
| text += "?" | |
| return f"Question: {text}\nAnswer:" | |
| # ========================================================== | |
| # MODEL CALL | |
| # ========================================================== | |
| def call_model(user_input): | |
| try: | |
| result = generator( | |
| get_prompt(user_input), | |
| max_new_tokens=150, # max_length की जगह इसे इस्तेमाल करें | |
| do_sample=False, | |
| temperature=0.3 | |
| ) | |
| answer = result[0]["generated_text"] | |
| if "Answer:" in answer: | |
| answer = answer.split("Answer:")[-1].strip() | |
| return answer if answer else "मैं अभी इस पर जवाब नहीं दे सकता।" | |
| except Exception as e: | |
| # Debugging: पूरी एरर मैसेज प्रिंट करें | |
| print(f"Error in call_model: {e}") | |
| return f"त्रुटि: {str(e)}" | |
| # ========================================================== | |
| # SIA LAYER (वही) - अभी इन्हें सीधे उपयोग नहीं करेंगे | |
| # ========================================================== | |
| def fact_check(text): | |
| try: | |
| q = text.split('.')[0].strip()[:80] | |
| if len(q) < 3: return False, None | |
| res = requests.get("https://en.wikipedia.org/w/api.php", params={ | |
| "action": "opensearch", "search": q, "limit": 1, "format": "json" | |
| }, timeout=5).json() | |
| return bool(res[1]), res[1][0] if res[1] else None | |
| except requests.exceptions.Timeout: | |
| print("Wikipedia API call timed out in fact_check!") | |
| return False, None | |
| except requests.exceptions.RequestException as e: | |
| print(f"Wikipedia API request failed in fact_check: {e}") | |
| return False, None | |
| except Exception as e: | |
| print(f"An unexpected error occurred in fact_check: {e}") | |
| return False, None | |
| def add_empathy(text): | |
| if any(w in text.lower() for w in ["मदद", "कृपया", "बताएं"]): | |
| return "मैं समझता/समझती हूँ कि यह महत्वपूर्ण है। " + text | |
| return "ध्यानपूर्वक: " + text | |
| def remove_bias(text): | |
| return text.replace("idiot", "व्यक्ति").replace("मूर्ख", "अज्ञानी") | |
| def calculate_ehi(raw, final): | |
| score = 50 | |
| if "समझ" in final or "ध्यानपूर्वक" in final: score += 30 | |
| if len(final) >= 20: score += 20 | |
| return min(score, 100) | |
| # ========================================================== | |
| # MAIN - SIA Layer को बाईपास करने के लिए संशोधित | |
| # ========================================================== | |
| def run_demo(user_input): | |
| print(f"User input received: {user_input}") # नया | |
| if not user_input.strip(): | |
| return "प्रश्न लिखें।", "", "EHI: 0", "None" | |
| print("Calling model (SIA bypass)...") # नया | |
| raw = call_model(user_input) | |
| print(f"Raw output from model: {raw}") # नया | |
| # SIA Layer को बाईपास कर दिया गया है | |
| sia_filtered_output = "SIA bypassed" # या आप raw आउटपुट ही दिखा सकते हैं | |
| ehi_score = "EHI: N/A" | |
| fact_anchor = "N/A (SIA bypassed)" | |
| # यदि आप चाहते हैं कि raw आउटपुट SIA-Filtered बॉक्स में भी दिखे: | |
| # sia_filtered_output = raw | |
| return raw, sia_filtered_output, ehi_score, fact_anchor | |
| # ========================================================== | |
| # UI | |
| # ========================================================== | |
| with gr.Blocks(title="SIA Infinity") as demo: | |
| gr.Markdown("# SIA Infinity — Ethical AI Demo\n**Free • Local • Hindi+English**") | |
| inp = gr.Textbox(label="प्रश्न लिखें", lines=3, placeholder="AI क्या है?\nभारत की राजधानी?") | |
| with gr.Row(): | |
| raw_out = gr.Textbox(label="Raw Output", lines=4) | |
| sia_out = gr.Textbox(label="SIA-Filtered", lines=4) | |
| with gr.Row(): | |
| ehi = gr.Textbox(label="EHI") | |
| fact = gr.Textbox(label="Fact Anchor") | |
| gr.Button("Run Demo", variant="primary").click(run_demo, inp, [raw_out, sia_out, ehi, fact]) | |
| demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=True) |